1

Semantic Web (2014) 1–27
IOS Press

Semantic Technologies for Historical
Research: A Survey
Editor(s): Aldo Gangemi, LIPN Université Paris13-CNRS-Sorbonne Cité, France and ISTC-CNR Roma, Italy
Solicited review(s): Monica Wachowicz, University of New Brunswick, Canada; Jack Owens, Idaho State University, USA; Aldo Gangemi,
LIPN Université Paris13-CNRS-Sorbonne Cité, France and ISTC-CNR Roma, Italy

Albert Meroño-Peñuela a,b , Ashkan Ashkpour c , Marieke van Erp d , Kees Mandemakers c ,Leen Breure e , Andrea Scharnhorst b , Stefan Schlobach a , and Frank van Harmelen aaDepartment of Computer Science, VU University Amsterdam, De Boelelaan 1081a, 1081HV Amsterdam, NL
E-mail: {albert.merono, k.s.schlobach, frank.van.harmelen}@vu.nlbData Archiving and Networked Services, Anna van Saksenlaan 10, 2593HT Den Haag, NL
E-mail: {albert.merono, andrea.scharnhorst}@dans.knaw.nlcInternational Institute of Social History, Cruquiusweg 31, 1019AT Amsterdam, NL
E-mail: {ashkan.ashkpour, kma}@iisg.nldFaculty of Arts, VU University Amsterdam, De Boelelaan 1081a, 1081HV Amsterdam, NL
E-mail: marieke.van.erp@vu.nleUniversiteit Utrecht, Princetonplein 5, De Uithof, 3584 CC Utrecht, NL
E-mail: l.breure@uu.nl

Abstract. During the nineties of the last century, historians and computer scientists created together a research agenda aroundthe life cycle of historical information. It comprised the tasks of creation, design, enrichment, editing, retrieval, analysis andpresentation of historical information with help of information technology. They also identified a number of problems andchallenges in this field, some of them closely related to semantics and meaning. In this survey paper we study the joint work ofhistorians and computer scientists in the use of Semantic Web methods and technologies in historical research. We analyse towhat extent these contributions help in solving the open problems in the agenda of historians, and we describe open challenges
and possible lines of research pushing further a still young, but promising, historical Semantic Web.
Keywords: Semantic Web, Historical research, Digital Humanities

1. Introduction
Historians have a long tradition in using computers for their research [16]. The field of historical research is currently undergoing major changes in its
methodology, largely due to the advent and availabilityof high-quality digital data sources. More recently, theWeb has shaken the paradigm of research data publication, particularly since the inception of the Semantic Web [13] and the Linked Data principles [43]. This
paper looks forward on how Semantic Web technology has been applied to historical data, and how thesetechnologies can facilitate, boost and improve researchby historians. This survey revisits the open problems
in historical data and historical research, and analyses
current contributions, namely papers, projects, online
resources and tools, that apply semantic technologies
to solve such problems. We study how successful thesesolutions have been and propose some challenges forthe future.
Historical research is an interesting domain for the
Semantic Web. Historical data are extremely context
dependent, and always open to a variety of possible interpretations. Availability on the Web of historical re-1570-0844/14/$27.50 c 2014 – IOS Press and the authors. All rights reserved

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search data, which concerns the study and understanding of our past, is growing. The Semantic Web is anevolution of the existing Web (based on the paradigmof the document) into a Semantic Web (based on theparadigm of structured data and meaning). It is not a
separate Web but an extension of the current one, inwhich information is given well-defined meaning, better enabling computers and people to work in cooperation. This survey studies the crossroads of the Semantic Web and history as research domains.
We consider surveying the state of the art in Semantic Web and history a fundamental task for both fields.
First, it is necessary as a knowledge organisation task,
in order to articulate research and discern contributions. Second, it fosters development of semantic technology and history, both individually and as a uniquefield, and helps on building research agendas. Other attempts on gathering research efforts on Semantic Web
and history exist, but most of them study specific history subfields [75,97,131] or analyse concrete taskoriented tools [37,76] and methodologies [43,50,51].
Moreover, none of them consist in surveys or literature reviews. To the best of our knowledge, this is thefirst survey reviewing contributions on history and the
Semantic Web as generic fields of research.
The elaboration of the study in this paper is not freeof obstacles. The first of them is the large amount ofresearch contributions to survey, which had to be filtered to fit strictly the Semantic Web goals and the historical research goals in order to be feasible. By historical research we mean strictly research performedby historians, and talk about history as a research domain. Thus, we exclude other fields of the humanities
in which historical research is also performed, such as
art history or history of literature. Nevertheless, in theend the number of contributions amount to more thana hundred. Secondly, and even though the corpus ofavailable literature is large, we also encountered difficulties on accessing some of the sources. To solve
this, we combined the contributions with the knowledge of domain experts, conducting eight interviews
with pioneers in this area. Third, structuring and articulating all this work is an arduous task that requires
a lot of schemas, tables and discussions. Finally, theclash of the vocabularies used by two different research communities, usually pointing at similar issues,
is problematic. To bridge different jargon we devote
some space to cover existing classifications of historical data, especially discussing terms like structure, andwe map historical data problems in terms of Semantic
Web solutions.

The paper delivers four contributions. First, it describes a classification of historical data depending on
several factors, merging existing distinctions by historians with structural approaches from computer science. Second, it articulates the research conducted in
the emerging field of historical Semantic Web in termsof several tasks, and depicts the current landscape on
advances in representing historical data with semantic
technology. Third, we map the open problems of historical data with the solutions provided by the surveyed
research. Finally, we show some open challenges forthe future, considering first the not (or only partially)solved problems, and secondly Semantic Web facilities
still to be explored in historical research.
While we concentrate on historical research, similar solutions emerge also in other humanities fields at
the turn to e-humanities or Digital Humanities [15,95].
As historical research overlaps with literary studies,
ancient language studies, archaeology, art history andother humanities fields, these areas of encounter are
also predestined candidates for the travel of genericmethods developed from a semantic technology perspective for historical research to other humanities
fields [64].
The survey is organized as follows. In Section 2 weintroduce some background on historical research andthe Semantic Web. In Section 3 we study the ecosystem of historical data. We describe the life cycle ofhistorical information, propose a classification for historical sources, and show open problems of historical
data. In Section 4 we articulate contributions that apply
semantic technologies to historical research. In Section 5 we answer the question on how the contributions presented in Section 4 solve the open problems
we describe in Section 3. In Section 6 we show thechallenges that are still left to solve. Finally, in Section
7 we discuss our findings and establish some conclusions.

2. Background2.1. Historical research
The field of historical research concerns the study
and the understanding of the past. The field is currently
undergoing major changes in its methodology, largely
due to the advent of computers and the Web [16].
Computer science has inspired historians from thestart. History and computing or Humanities computing
were labels used before the inception of the Web [63].

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Many pioneers in computer aided historical analysis
have a background both in history and in informatics,
and reflected early on about the usefulness of computational and digital techniques for historical research[16]. Ever since the advent of computing, historians
have been using it in their research or teachings in one
way or the other. The first revolution in the 1960s allowed researchers to harness the potential of computational techniques in order to analyze more data than
had ever been possible before, enabling verificationand comparisons of their research data but also giving more precision to their findings [3]. However thiswas a marginal group within the historical research: ingeneral, the usage of computers by humanists could be
described as occasional [34]. The emphasis was moreon providing historians with the tools to do what they
have always done, but now in a more effective and efficient way. Concretely,– databases and document management systems facilitated the transition from historical documents
to historical knowledge through text analysis;– statistical methods were used predominantly fortesting hypotheses, although with time were morevalued as a descriptive or exploratory tool than as
an inductive method;– image management aided historians to digitize,enrich, retrieve images and visualize data [16].
Although computing tools are currently embeddedin the daily life of most researchers, the use of thesetools did not revolutionized all sciences equally. Accordingly, history failed to acknowledge many of thetools computing had come up with [16]. Instead ofimproving the quality of the work of historians andassisting them in their processes, software developed
for historians often requires attending several summer
schools [17]. Currently there are still many challenges
and information problems in historical research. Thesedifficulties mainly range from textual, linkage, structuring, interpretation, to visualization problems [16].
Despite these challenges, computing in history andin the broader sense the humanities, also broughtsome significant contributions in certain fields like linguistics (corpus annotations, text mining, historicalthesauri etc..), archaeology (impossible without geographic information systems (GIS) nowadays), andother fields using sources that have been digitizedfor historical (comparative) research and converted todatabases [16]. The use of electronic tools and media is incredibly valuable and important for opening
up various sources for research which would other-3

wise remain unused. Open access to research data has
always been an issue, especially in the humanities.
However, over the past years various efforts have been
made in opening up these black boxes and making
them available for researchers. These different sources
contain rich information from various fields, which areoften digital in nature in the form of databases, text
corpora or images. These sources, in practice isolated
databases, often contain a lot of semantics, but theirdata models were asynchronously designed, making
them difficult to compare. So, while more and more
sources are being digitized, more attention has to begiven to the development of computational methods toprocess and analyze all these different types of information [41].
A key issue for historians and other humanities researchers when dealing with historical data for comparative research concerns the lack of consistency andcomparability across time and space, due to changing
meanings, various interpretations of the same historical situations or processes, changing classifications,etc.
Though not all research dreams materialized in theway initially envisioned [56], the inception of the Web
allowed historians to aim for world-wide, large scalecollaborations, especially in the area of economic andsocial history. This kind of web based cooperation allows to collect, distribute, annotate and analyze historical information all around the globe [29].
Changes in historical research are closely connectedto the emergence of new scientific methods, and thisco-evolution holds for decades and centuries. Statistics has influenced many fields including history, andpaved the ground for quantitative studies [57]. However, these kind of historical studies became more andmore the domain of sociologists, economists and demographers than scientists educated as historians [91].
Late important changes are consequences of recent
technological trends connected to the emergence of theWeb [71] and the inception of Semantic Web technologies [5].
2.2. The Semantic Web
The advent of the Semantic Web poses new perspectives, challenges and research opportunities forhistorical research. Envisioned in 2001 by BernersLee, Hendler and Lassila [13], the Semantic Web wasconceived as an evolution of the existing Web (basedon the paradigm of the document) into a Semantic
Web (based on the paradigm of structured data and4

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meaning). By that time, most of the contents of the
Web were designed for humans to read, but not forcomputer programs to process meaningfully. Althoughcomputer programs could parse the source code of
Web pages to extract layout information and text, computers had no mechanism to process the semantics. In
other words, the Semantic Web is not a separate Web
but an extension of the current one, in which information is given well-defined meaning, better enabling
computers and people to work in cooperation [13].
More practically, the Semantic Web can be definedas the collaborative movement and the set of standards
that pursue the realization of this vision. The World
Wide Web Consortium [14] (W3C) is the leading international standards body, and the Resource Description
Framework [135] (RDF) is the basic layer in which the
Semantic Web is built on. RDF is a set of W3C specifications designed as a metadata data model. It is used asa conceptual description method: entities of the world
are represented with nodes (e.g. Dante Alighieri or
The Divine Comedy), while the relationships between
these nodes are represented through edges that connect
them (e.g. Dante Alighieri wrote The Divine Comedy).
These statements about nodes and edges are expressedas triples. A triple consists of a subject, a predicate,
and an object, and describes a fact in a very similar way
as natural language sentences do (e.g. subject: Dante
Alighieri; predicate: wrote; object: The Divine Comedy). Subjects and predicates must be URIs (Uniform
Resource Identifiers, the strings of characters used to
identify and name a web resource like a web page),while objects can be either URIs or literals (like integer numbers or strings) [43]. RDF can be considereda knowledge representation paradigm where facts andthe vocabularies used to describe them have the formof a graph. This setting makes RDF very suitable fordata publishing and querying on the Web, especiallywhen (a) the dataset does not follow a static schema;and (b) there is an interest of linking the dataset toother datasets.
Efforts on standardization have produced ontologies
and vocabularies to describe multiple domains. An ontology is an explicit specification of a conceptualization [40] and contains the classes, properties and individuals that characterize a given domain, such as history. In the Semantic Web, the design of ontologies isdone using the Web Ontology Language [132] (OWL).
OWL consists of several language variants built upon
different modalities of Description Logics [9] (DL), a
family of formal knowledge representation languages.
Such languages allow reasoning, that is, to extract ordeduce consequences and new knowledge from theoriginal.
A large number of RDF datasets have been published and interlinked on the Web, using these ontologies and vocabularies and following the Linked
Data principles [12]. In the middle of the documentWeb and the data-Web, formats and vocabularies forrich structured document markup (such as RDFa [134]or schema.org [94]) are enabling software agents to
crawl semantics from web pages, bridging the gap between the Web for humans and the Web for machines.
These efforts have evolved the Web into a global data
space [43] where data can be queried e.g. using theSPARQL query language (SPARQL Protocol and RDF
Query Language) [136]. Although the transition fromthe document-Web to the database-Web exists in theform of these standards and technologies, the simple
idea of the Semantic Web remains largely unrealized[98].

3. Historical data
Since the introduction of computers in the field, historical research has produced high-quality digital resources [16]. Historical datasets encompass texts, images, statistical tables and objects that contain information about events, people and processes throughout
history. Converted or born-digital, historical datasets
are now analyzed at big scale and published on theWeb. Their temporal perspective makes them valuable
resources and interesting objects of study.
In this section we describe the ecosystem where historical information lives. First we introduce the life cycle of historical information, which is the framework
we use to study how historical data is created, enriched, edited, retrieved, analysed, and presented. Then
we propose a classification of historical data depending on several factors. Finally, we revisit the traditional
open problems of historical data. Some of these problems have found solutions in current Semantic Web developments we present in Section 4.
3.1. The life cycle
The main object of study in historical research is
historical information, and the multiple ways to create,
design, enrich, edit, retrieve, analyse and present historical information with help of information technology. It is important to distinguish historical information from raw data in historical sources. These data areA. Meroño-Peñuela et al. / Semantic Technologies for Historical Research: A Survey

selected, edited, described, reorganized and publishedin some form, before they become part of the historian’s body of scientific knowledge. We use the life cycle of historical information proposed by Boonstra etal. [16] to study the workflow of historical informationin historical research.
Historical objects go through distinct phases in historical research. In each phase, these objects are transformed in order to produce an outcome meeting specific historical requirements. The phases can be laidout as the workflow of a historical information life cycle (see Figure 1). The phases, although sequentiallypresented, do not always have to be passed through inrigorous order; some can be skipped if necessary. The
phases are also quite comparable with the practice inother fields of science.

Fig. 1. The life cycle of historical information (Boonstra et al. [16]).
The phases in the life cycle are: (1) creation; (2) enrichment; (3)editing; (4) retrieval; (5) analysis; and (6) presentation.

The life cycle of historical information consists ofsix phases:
1. Creation. The first stage of the life cycle is thecreation stage. The main aspect of this stage consists of the physical creation of digital data, including the design of the information structure
and the research project. Examples of activities
in this phase would be the data entry plan, digitisation of documents (through e.g. OCR), or considering the appropriate database software.
2. Enrichment. The main goal of this phase is toenrich the data created in the previous step with5

metadata, describing the historical informationin more detail, preferably using standards suchas Dublin Core [28], and intelligible to retrieval
software. This phase also comprises the linkage
of individual data that belongs together in the historical reality, because these data belong to thesame person, place or event.
3. Editing. Editing includes the actual encoding of
textual information, like inserting mark-up tags
or entering data in the fields of database records,
with the intention of changing or adding historical data of convenience. All data transformations
through algorithmic processes prior to analysis
also belong to this phase. Editing also extends toannotating original data with background information, bibliographical references and links torelated passages.
4. Retrieval. In this phase information is retrieved,that is, selected, looked up, and used. The retrieval stage mainly involves selection mechanism look-ups such as SQL-queries for traditional databases or Xpath [137] and Xquery[138] for XML-encoded texts.
5. Analysis. Analyzing information means quite
different things in historical research. It varies
from qualitative comparison and assessment ofquery results, to advanced statistical analysis ofdata sets.
6. Presentation. Historical information is to becommunicated in different circumstances throughmultiple forms of presentation. It may take very
different shapes, varying from electronic text
editions, online databases, virtual exhibitions tosmall-scale visualizations. It can happen frequently in other phases as well.
In the middle of the historical information life cycle,
three aspects are identified which are central to history
and computing, but also in the humanities in general:– Durability ensures the long term deployment of
the produced historical information.
– Usability refers to the ease of efficiency, effectiveness and user satisfaction.
– Modeling denotes to more general modeling ofresearch processes and historical information systems.
3.2. A classification of historical data
The continuous usage of computing in different areas of historical research has produced digital histori-

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cal data with different formats, perspectives and goals.
To be used in the Semantic Web, these historical data
have to be represented semantically, using the current
standards (see Section 2.2). In this section we proposea classification of historical data in order to bridge thegap between the data representation tradition in historical research, and the standard modelling paradigms of
the Semantic Web [5,43].
3.2.1. Primary and secondary sources
Historical sources can be characterized and divided
in many ways. A basic distinction used by historians is
between primary and secondary sources.
Primary sources are original materials created at thetime under study [11]. They present information in its
original form, neither interpreted, condensed nor evaluated by other writers, and describe original thinking and data [8]. Examples of primary sources are
scientific journal articles reporting experimental research results, persons with direct knowledge of a situation, government documents, legal documents (e.g.
the Constitution of Canada), original manuscripts, diaries (e.g. the Diary of Anne Frank) and creative work.
Primary sources can be distinguished into administrative sources and narrative sources, like biographies orchronicles. Administrative sources contain records ofsome administration (census, birth, marriage and deathrolls, administrative accounts of taxes and expenses,
resolutions minutes of administrative bodies, deeds,
contracts, etc.). Typically, historians want to extract
the facts in order to gather statistical data. Narrative
sources are full text documents containing a description of the past, made by an author being an eyewitness: think of diaries, chronicles, newspaper articles,
diplomatic reports, political pamphlets, etc. Historians
may be interested in both, factual information and theauthor’s vision and the bias.
Secondary sources are materials that have been written by historians or their predecessors about the past[127]. They describe, interpret, analyze and evaluate
the primary sources. Usually, secondary sources gather
modified, selected, or rearranged information of primary sources for a specific purpose or audience [8].
Examples of secondary sources are bibliographies, encyclopedias, review articles and literature reviews, orworks of criticism and interpretation.
Since historical data have not been produced under
the controlled conditions of an experiment, historicalresearch always has something of the work of a detective, and certain details (read: annoying inconsistencies) cannot be destroyed or manipulated. These de-tails may contain relevant information. On the other
hand, to be able to extract statistical information andcome up with more general statements, some formalization, relating information and harmonizing expressions of what is later used as variables is needed. Harmonization, the process of making data-sources uniformly accessible without altering its original form, isclosely related to issues of standardization and formalization [65].
3.2.2. Intended further processing
Some historians [16] propose to structure historical
data depending on their required further machine processing. They distinguish between textual data, quantitative data and visual data. Textual data comprises
the whole set of unstructured historical sources, suchas letters, memoranda or biographies, all in a form of
free text. Quantitative data can be seen as historicalsources aiming at a quantitative analysis, like church
registers, census tables and municipality micro-data.
Finally, visual data gathers all kinds of historical evidence not encoded by text or numbers, such as photographs, video footage and sound records.
3.2.3. Source oriented vs. goal oriented
Researchers make the distinction between source
oriented and goal oriented historical data [16]. Whendealing with historical data it is important to decidein an early stage whether the data should be modeled according to a source or goal oriented approach.
The source oriented approach aims to postpone enforcing any standards or classifications, resemble theunderlying source data as close as possible (schema
free representation) and hence allow room for multiple interpretations of the data. Another approach is thegoal or model oriented approach. Historical data is often plagued with inconsistencies, changing structuresand classifications, redundant or erroneous data andso forth. The goal oriented perspective therefore advocates the use of more sound data models to start with.
This means restructuring the data according to certain
views or goals which are mainly dependent on expert
knowledge. Accordingly, this perspective commits to acertain data model in an early stage.
3.2.4. Level of structure
At the end of the creation phase (see Section 3.1)
one may expect to have a historical dataset suitable forfurther processes. However, the nature of the steps tobe taken thereafter may strongly depend on the way
the resulting dataset is structured. Indeed, attaching
Semantic Web technologies to these historical sources

A. Meroño-Peñuela et al. / Semantic Technologies for Historical Research: A Survey(e.g. to extract RDF triples from them, or enrich themsemantically) is strongly dependent on their level ofstructure. We propose the historical data classification
shown in Figure 2. We distinguish three levels of inner structure in historical datasets: structured, semistructured and unstructured. Each level of structure
can be divided into several types of structure.
Structured data. Structured data refers to sourcesthat have a clearly defined data model. A data modelis an abstract model that documents and organizes
data for communication, and is used as a plan fordeveloping applications. An example of a structured
dataset would be census material published in rows
and columns, or a database of historical events. Well
known generic examples of such a structure are sources
encoded as relational databases, XML files, spreadsheet workbooks or RDF datasets. It is easy to see that
all these examples meet a certain abstract model forthe data they represent (relational schemas, DTD constraints, tabular formats and RDF triple statements).
Structured historical data are usually managed with relational databases, graph/tree representations and tabular representations. Relational databases are the mostwell-known way of committing to some schema forrepresenting historical objects and their relationships.
Because their structure, relational databases are ideal
for goal or model-oriented representation of historical
data [16] with some concrete conception of reality in
mind.
Relational databases. Relational databases havetheir own languages (SQL) and systems (MySQL, Microsoft SQL Server, PostgreSQL, Microsoft Access,Oracle, etc.) to represent and store historical data.
They all follow the relational model [26]. Some issues,
especially when trying to integrate data from different databases and modelled with different conceptual
schemas, appear often in historical datasets encoded
this way.
Graph/tree representations. Relying on graph theory, graph databases offer mechanisms for storage
and retrieval of data with less constrained consistencymodels than traditional relational databases. They provide variable performance and scalability, but highflexibility and complexity support. AllegroGraph, IBM
DB2, OpenLink Virtuoso and OWLIM are typical examples. To exchange historical data in graph form,
RDF (see Section 2.2) is used. Graph/tree data is foundin historical samples that come in formats such as
XML (trees), RDF (graphs) or JSON (JavaScript Object Notation). Although they are conceived for modeling data in very disparate models (a tree, a graph and7nested dictionaries, respectively) and purposes (e.g.
JSON is mainly used for data interchange between web
applications and services), these formats also follow
some assumptions to put structure on historical data.
Tabular representations. Some historical datasets
are encoded in tabular form. Tables consist of an ordered set of rows and columns, the latter typically
identified with a name. The intersection of a row anda column is a cell. Depending on the specific encoding (Comma-separated values (CSV), Microsoft Excel
spreadsheets, etc.) tables can offer variable features.
Tables are used to store all kinds of historical data, especially meso, macro and microdata about individuals,
registries, or historical population censuses.
Semi-structured data. Semi-structured data appear
more often as an intermediate representation between
unstructured and structured historical data than as raw
historical data. Typical technologies applied here aremarkup languages, such as XML, to denote special
characteristics of historical texts in specific regions of
the corpus. Annotated corpora are the most important
example of semi-structured data. They usually consistof raw historical texts with annotations on well-defined
text regions, usually implemented with a markup language, like XML.
Unstructured data. In case a data model such as
we described for structured data does not exist, we talkabout unstructured data. In unstructured data there is
scarce or no structure at all. The typical example isunconstrained, raw corpora encoded in plain text files.
Unstructured sources are the most common representation of historical data, typically transcriptions of historical texts. Objects with a high variety of historicalnature can be included in this category: letters, books,
memoranda, acts, etc.
The use of the terms structured and unstructured incomputer science to describe datasets is very different
from the use of those notions in history, where administrative sources are often labeled as structured and the
textual secondary sources as unstructured. Also narrative sources have internal structures, which can bemade explicit. From the 19th century onwards historians have made scholarly source editions, which contain structured and annotated information. Nowadays
the printed source editions are replaced and supplemented by databases and XML-based digital editions.
So, structured or unstructured are relative notions: administrative sources usually have an obvious structured layout, while narrative sources have a latent, at
first sight hidden structure, which is made explicit assoon as they appear in a scholarly source edition. So,

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Fig. 2. Classification of historical data according to their level of structure. Dotted arrows indicate the direction of usual transformations inworkflows that identify historical entities (and their relations), from unstructured to structured representations.

both administrative and narrative sources can appear
in the form of structured or unstructured data in computer science jargon.
3.2.5. Discussion
Although structure really matters for deciding what
specific computing technique or semantic model has tobe applied to the sources, being those sources administrative or narrative, deliberate or inadvertent, does notreally matter if their inner structure is clearly identified. Their belonging to one type of another may havean influence at some point, but in general the procedure
to extract RDF triples from the sources strongly relieson the type of source we have regarding their structure. The goal is a faithful representation of the sourcein Semantic Web formats: a source-close representation allowing to model data as-is, meeting the samerequirements of faithfulness than critical source editions (which is the standard for historians). It is criticalfor semantic representations to consider context andsource structure as critical editions do, because they
may be relevant for interpretation of the data. A digitized, semantically-enabled historical source should
ideally preserve context and structure and support
goal-oriented extraction of data, in order to constructhistorical facts in the framework of a certain research.

By means of dataset interlinking and appropriate design and usage of ontologies and vocabularies, context
and source structure should be able to be preserved
using semantic technologies. To this end, ontologies
can be contextualized to conciliate a party’s subjective
view of a domain [18].
3.3. Open problems
The classification proposed in Section 3.2.4 is notstrict and admits hybrid examples. For instance, annotated digital text sources can be provided both as XML
files or stored in a relational database (e.g. for statistical analysis). Some authors classify sources that combine primary and secondary sources like these as tertiary sources [72].
Although many advances have been made in different fields and computers are seen as valuable assets,a high percentage of historians are unfamiliar with orremain unconvinced that semantic technologies may
become a new methodological asset [3,103]. The reason is that the weapon of choice of historians was andremains mostly the database, particularly in relational
form [3]. This not only enabled historians to retainsome of the integrity of the original data sources, butalso paved way for rapid advances on issues such as

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classifications and record linkage. Therefore, historians typically do research using their own datasets, resulting in the creation of a vast amount of scattereddata and specific technological challenges. In this section we revisit the traditional open problems of historical research derived from this tradition. In Section 5,
after presenting the state of the art in the application ofsemantic technologies to historical research in Section
4, we point to the specific open problems describedhere that are eventually solved with such technologies.
Historical data problems can be divided into fourmain categories: information problems of historicalsources, information problems of relationship between
sources, information problems in historical analysis, and information problems of the presentation ofsources [16].
3.3.1. Historical sources
The first set of open problems in historical researchhappens in phase 1 of the historical data life cycle (seeSection 3.1). This is when the historical data are created.
Manually encoded or OCR-scanned, the creation of
the dataset reveals the first barriers. Some characters,
words or entire phrases in the original material may belost or impossible to read or recognise by the human or
the computer. Moreover, different techniques may extract historical entities differently. An example would
be: what is the word that is written on this thirteenthcentury manuscript?
The next question usually is: what does it mean?
Background knowledge is provided by libraries in theoffline world. But the computer aiding tools also needto have means to help the historian, using the Web aschannel and semantics as meaning.
Related to background knowledge is the provenanceof the data. Even if the source is clearly identified andits meaning deciphered, the historian needs to know
more. To which issue does it relate? Why was it put
there? Why was the text written? Who was the author?
Who was supposed to read the manuscript? Why has it
survived?
Another main issue relates to the structuring problem of historical data [87]. How can historical objects be encoded in a database? Researchers have todecide on what is an adequate data model for theirdatasets. As historians often have no clear researchquestion when starting an investigation, it is neither
possible nor desirable to model the data according tocertain requirements in advance. Moreover, different
sources have been produced throughout different peri-

9

ods in history with different views and motives. Historical census data is a good example, having varying
structures and changing levels of detail which hinders
comparative social history research both in past andpresent efforts [87].
The main discussion regarding this involves whether
to use a source or a goal oriented data model for historical data (see Section 3.2.3). Researchers in favor of
the source oriented approach claim that a commitment
to a certain data model suitable for analysis should
be postponed to the final stages of a project, in orderto maintain flexibility and build on the data in a nondestructive manner. This is especially the case when
the database is supposed to be shared with other researchers or used in the future [62].
3.3.2. Relationships between sources
As historical researchers deal with various isolated
sources, they face the problem of how to integratethese dissimilar sources for their purposes. This typically happens in phase 2 (enrichment) of the life cycle of historical information (see Section 3.1). An example would be: is this Lars Erikson, from this register, the same man as the Lars Eriksson from this otherregister?
Quite often several sources are used in historicalresearch, which makes linking different sources another key problem. Micro data of the same person
contained in different censuses, parish registers, marriage or death certificates are a good example. Obvious linkage problems are how to disambiguate between persons with the same name, how to manage changing names (e.g. in case of marriage of awoman) and how to standardize spelling variations in
the names. In databases, several issues affect data comparability. Schema mismatch occurs when two different databases cannot be compared because of semantic
differences in the concepts of their defining schemas.
For instance, two XML files conformant to different
DTD schemas may define and structure differently thesame historical entity. Additionally, value mismatchoccurs when the allowed values for columns or variables in two databases are different. It may also happen
across datasets despite being schema or vocabularycompatible. For instance, an attribute may encode thevariable social class with categories A, B, C, whileother dataset may do so with categories high, medium,low.
Other problems relate to how to link historical datawith their spatial and temporal context. For example,
some historical facts may need to be linked with occu-

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A. Meroño-Peñuela et al. / Semantic Technologies for Historical Research: A Survey

pational titles that evolve over time [45] or with countries with changing geographical boundaries (compare
for example the contemporary geographic position ofcountries in Europe with the situation in 1930 andin 1900; or the fact that the city of Rotterdam suffered nine major changes in its composition between
1886 and 1941 [1]). As historical research often deals
with changes in time and space, historians require tools
which enable them to deal with these aspects. Accordingly several techniques have been developed for historical research, but the applicability of these has yet
to be determined [16].
3.3.3. Historical analysis
Historical analysis is a fundamental part of the life
cycle (see phase 5 in Section 3.1). It usually implies
data transformations that aid historians in guiding theirresearch. It also builds the bridge between their hypotheses and historical evidence.
The first issue in analysis is the massive treatment ofhistorical data processed in previous stages to satisfy
historical requirements, or to support a specific historical interpretation. An example would be: from thishuge amount of digital records, is it possible to discern
patterns that add to our knowledge of history? Various
statistical techniques are borrowed from the social sciences to this end, like multilevel regression, and othertechniques have been specifically developed for historical research, such as event history analysis. However,addressing historical data analysis in a broad sense remains essentially unsolved.
In historical research the meaning of data cannot exist without interpretations [16]. Due to drifting concepts in history, different interpretations could exist
with regards to certain data [140]. However as interpretation of data is a subjective matter, this information
should be added in a non destructive way, preserving
the original source data.
3.3.4. Presentation
Presentation is the final phase of the historical information life cycle (see Section 3.1). Its goal is to use visualizations to aid the study and comprehension of historical data. An example problem of such phase would
be: how do you put time-varying historical information
on a historical map?
Presentation of historical data must be adequate.
Different types of presentations are suitable at different stages of a research project. Presentation may takedifferent shapes, varying from digitized documents,
poorly and well modelled databases, or visualizations
and representations on Geographic Information Sys-

tems (GIS). Currently there is a great need for tools
and methods to present changes over time and space.

4. Findings
In this section we review the current state of the art
in the application of semantic technologies to historical research, describing relevant contributions towards
a historical Semantic Web.
The contributions are classified in the categories ofscientific papers, research projects, online resources(presentations, online articles), and tools, ontologies
and lexical resources (ontologies, demos, applications
or programming libraries). Tables 1, 2, 3 and 4 showthese contributions. Additionally, we map each contribution to one or more specific tasks. These tasks areshared areas of concern for both historical researchand the Semantic Web. We identify the following fourtasks: knowledge modelling, text processing and mining, search and retrieval, and semantic interoperability. We develop the work on each task in the following sections. We group contributions that cover the full
spectrum of tasks under a final section in longitudinalapproaches.
4.1. Knowledge modelling
Under this category we study research that has been
conducted to model historical knowledge or historical facts using standard Semantic Web representations(see Section 2.2). We group contributions to a semantically enabled historical web by the following emphasis of research: historical ontologies, and linking historical data.
4.1.1. Historical ontologies
Data models are necessary for giving structure to
any historical data, since they are the abstract models that document and organise data properly for communication. Ontologies encode such models in the Semantic Web [13] (see Section 2.2), and attention has
been given to the need of historical ontologies [51].
In historical research, ontologies are the providers of
metadata and background knowledge in phases 2 (enrichment) and 3 (editing) of the historical information
life cycle (see Section 3.1). Semantic Wikis [88,101]
are a great resource for historians to collaboratively
build such ontologies.
We find a first category of such models in the formof (typically XML-encoded) taxonomies for histori-

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Paper title

Knowledge
modelling

Text processing& mining

Search &RetrievalHacking History via Event Extraction [97]

◦

X

X

Exploiting Semantic Web Technologies for Intelligent Access to Historical Documents [50]

◦

X

X

Historical Ontologies [51]X

Semantic
interoperability◦
X

Virtual Knowledge in Family History. Visionary Technologies, Dreams and Research Agendas [56]Past, present and future of historical information science
[16]

◦

◦

◦

Proposed category system for 1960-2000 Census occupations [68]◦X

The Comparability of Occupations and the Generation ofIncome Scores [102]◦X

Challenges and Methods of International Census Harmonization [31]◦X

Making Sense of Census Responses: coding complex variables in the 1920 PUMS [39]◦X

Semantic Networks and Historical Knowledge Management: Introducing New Methods of Computer-based Research [54]

X

X

◦

◦

X

◦

X

X

◦

X

Digital Hermeneutics: Agora and the Online Understanding of Cultural Heritage [129]

◦

X

X

Visualizing an Historical Semantic Web with Heml [89]X

Exploring Historical RDF with Heml [90]X

LODifier: Generating Linked Data from Unstructured
Text [7]◦X

CLIO - A Databank Oriented System for Historians [125]

X

◦

X

◦

CensSys - A system for analyzing census-type data [73]

◦

◦

X

A discursive analysis of itineraries in an historical and regional corpus of travels: syntax, semantics, and pragmatics in a unified type theoretical framework [69]

◦

X

◦

A Comparison of Knowledge Extraction Tools for the Semantic Web [37]X

X

Queries in Context: Access to Digitized Historic Documents in a Collaboratory of the Humanities [126]Converting a Historical Architecture Encyclopedia into a
Semantic Knowledge Base [141]Historical documents as monuments and as sources [27]X
X

Table 1
Reviewed papers. The Xand ◦ signs indicate a strong and a medium
relationship, respectively, between the contributions (rows) and thetasks (columns).

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Project name

Knowledge
modelling

Text processing& mining

Search &Retrieval

Semantic
interoperability

Agora [2]◦X

X

BRIDGE [19]

X

X

◦

CHoral - access to oral history [24]

X

X

◦

Historical Timeline Mining and Extraction (HiTiME) [46]◦LINKing System for historical family reconstruction(LINKS) [59]X

SCRipt Analysis Tools for the Cultural Heritage(SCRATCH) [96]

◦

X

X

FDR Pearl Harbor Project [85]

X

X

X

X

North Atlantic Population Project (NAPP) [70]

◦

◦

X

X

◦

◦

Circulation of Knowledge and Learned Practices in the
17th-century Dutch Republic (CKCC) [25]X

X

Voyage of the Slave Ship Sally [93]

◦

X

◦

Multilingual Access to Large Spoken Archives (NSFITR/MALACH) [61]

◦

◦

X

H-BOT [42]

◦

X

X

Clergy of the Church of England Database (CCEd) [21]X

Armadillo: Historical Data Mining [6]X

Historical Event Markup and Linking (HEML) [44]X

SAILS [92]X

CLARIN-Verrijkt Koninkrijk [131]X

Historical International Standard Classification of Occupations (HISCO) [45]X

Historical Sample of the Netherlands (HSN) [48]◦

CEDAR [22]X

Linking History in Place [58]◦Table 2

◦
X

X

X

X

◦

X

X

X
X

X

X

X

X
X

◦

X

X

X

X

X

X

Reviewed projects. The Xand ◦ signs indicate a strong and a medium relationship, respectively, between the contributions (rows) and the tasks(columns).

cal research. A taxonomy is a collection of controlled
vocabulary terms organized into a hierarchical structure, in general with less expressivity than an ontology.
The first important example of such knowledge organization is the CLIO system, a databank oriented system for historians [125] appeared in 1980. CLIO included a tag/content representation for historical datathat could be structured in complex hierarchies, supporting the recoding of material with doubtful semantics. CLIO remained as the system for organizing historical knowledge until the inception of the Web.
More recently, the Semantic Web for Family History [82] exposes a set of genealogy markup languages
based on XML to semantically tag genealogical information on sources containing that kind of histori-

cal data. In the context of the Text Encoding Initiative [119] (TEI) there is an important discussion aboutbuilding the bridge between XML (taxonomies) andOWL (ontologies) in historical data. SIG: Ontologies[78] contains a full log on contributions on how to
use ontologies with TEI formats; namely, how TEIXML encoded documents can refer to historical concepts and properties that have been previously formalized in an external OWL ontology.
The Historical Event Markup and Linking Project[44,90] (HEML) was probably the first project with thegoal of creating a Semantic Web of history. Started in
2001, it explored the use of W3C markup technologies
to encode and visualize historical events on the Web.
Although in the beginning XML was the selected lan-

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Resource name

Knowledge
modelling

Text processing& mining

Search &Retrieval

Semantic
interoperability

Semantic Web approaches in Digital History: an Introduction [75]X

X

Fawcett: A Toolkit to Begin an Historical Semantic Web
[76]

X

X

X

X

Spatial cyberinfrastructures, ontologies, and the humanities [77]

X

X

X

X

SIG Ontologies [78]X

CultureSampo - Finnish Culture on the Semantic Web 2.0:
Thematic Perspectives for the End-user [79]

X

X

X

Text Mining for Historical Documents: Topics and Papers
[80]

◦

X

◦

RDF vocabularies for historic placenames and relations
between them [81]X

The Semantic Web for Family History [82]

X

X

X

Data portal for Social Sciences. Open data with SPARQL
endpoint [83]

X

X

X

X
X

X

Table 3
Online resources. The Xand ◦ signs indicate a strong and a medium relationship, respectively, between the contributions (rows) and the tasks(columns).

guage to provide tagging and markup for describing
historical events, the project later experimented with
RDF to model and visualize them [89]. This transition
was also happening in the whole historical ontologies
community, as researchers better understood RDF and
its differences with XML.
The modelling and representation of events, often
defined as persons doing an activity in a certain place
and time, has received a lot of attention in the development of historical ontologies, and most practical results show that the concept of the event is at the coreof historical knowledge modelling. Van Hage et al.
[130] design the Simple Event Model [110] (SEM), intended to model events in the domains of history, cultural heritage, multimedia and geography. Similarly,
the Event Ontology [33], inspired in the musical domain, models the representation of events as combinations of persons, places, moments in time, and factors.
Finally, LODE: An ontology for Linking Open Descriptions of Events [60] is especially intended for the
publication of historical events as Linked Data. Interestingly, these ontologies have a great overlap in theirconceptual modelling of events even coming from different domains. On the other hand, some studies pointout specific modelling needs for different historical domains, stressing that historical ontologies should reflect how a particular time frame influences the definitions of concepts [51].

Another big focus in historical ontologies is given
to geographical modelling. Owens et al. [84] describe
a geographically-integrated history, and stress the importance of dynamics and semantics in Geographic Information Systems (GIS). They set an agenda for historical GIS systems that includes important semantic modelling tasks involving ontologies and geography for historical analysis. Moot et al. [69] depict
the interesting crossroad between text analysis, historical semantics and geography in a work that structures
geographical knowledge from a historical corpus ofitineraries. Vocabularies for historical place names areunder discussion [81]. Although not intended for historical research, the GeoNames ontology [38] is thereference for geographical modelling in the Semantic
Web.
Since entities like places, persons or events change
their over history and time, there is work raising theimportance of a change-aware modelling in ontologies [35,64,66]. In historical research and the Semantic Web this is especially true for geographical names,
places and regions [49], but also for demographical,
social and economical indicators, such as occupations[45].
4.1.2. Linking historical data
By understanding the use and advantages of semantic technologies, practitioners and researchers of his-

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Tool, ontology, lexical resource

Knowledge
modelling

Text processing& mining◦X

NLP2RDF [105]Search &Retrieval

SIMILE/Timeline [106]◦X

Gapminder [107]X

X

TokenX [108]◦X

TAPoR [109]X

X

Semantic
interoperability

X

SEM event model [110]X◦

OpenCYC [111]X

X
X

XCES [112]Dublin Core [28]

X
◦

X
X

GATE [113]WordNet [114]◦X

Framenet [115]X

X

SUMO [116]X

MILO [117]X◦◦

AskSam [118]X

TEI (Text Encoding Initiative) [119]X

X

SGML [120]X

X

TACT [121]X

Wordcruncher [122]X

Atlas.ti [123]X

NLTK [124]X

FRED [36]X

X

WAHSP and BILAND [139]

◦

X

◦

◦

The Event Ontology [33]X◦

LODE [60]X◦

Semantic MediaWiki (SMW) [101]X

Europeana Data Model (EDM) [32]X
Table 4

X

X◦

Tools, ontologies, and lexical resources. The Xand ◦ signs indicate a strong and a medium relationship, respectively, between the contributions(rows) and the tasks (columns).

torical data can not only connect their own data sources
but moreover, also disseminate their data into the Semantic Web and integrate it with other data sources
which were previously not possible or cumbersome.
The approaches reviewed in this section match thehistorical data problem of the relationships between
sources (see Section 3.3.2). In most cases, the use of
semantic technologies solves it.
If one side of knowledge modelling stresses the importance of ontologies and formalization of the semantics of historical domains, the other side pursues theusage of such ontologies to interlink related historical data on the Web. Some researchers in history havecentered their interest in how semantics can help relating and linking historical sources and entities: historical, semantic networks are a computer-based method
for working with historical data. Objects (e.g., people, places, events) can be entered into a database andconnected to each other relationally. Both qualitativeand quantitative research could profit from such an approach [54]. Linking historical datasets appropriately
is an old and very well known problem in historicalresearch [16]. The landscape on current projects linking historical data (typically extracted from unstructured sources) shows a tendency on publishing more
and more historical Linked Data in RDF.

A. Meroño-Peñuela et al. / Semantic Technologies for Historical Research: A Survey

There is a wide variety of project types looking forthat structure, though not doing so solely (or explicitly) in RDF. For instance, the Circulation of Knowledge and Learned Practices in the 17th-century Dutch
Republic [25] (CKCC project) studies the epistolary
network for circulation of knowledge in Europe in the
17th century, extracting all entities and links from thecorrespondence of scientific scholars of that time. The
LINKing System for historical family reconstruction(LINKS) project [59] reconstructs the links between
individuals of historical families across several registries. The CCed [21] project follows a similar approach with clerical careers from the Church of England Database. While these projects mine the historical sources for important historical personalities andtheir relationships, other approaches, such the SAILS[92] project, dive into more concrete historical eventsand links various World War I naval registries together.
The common goal in these initiatives is to produce asemantic network of historical data containing objects
like people, places and events connected to each other,which clearly matches the intended purpose of historical ontologies (see Section 4.1.1), but also the general
mission of the Semantic Web [13] and Linked Data
[43].
Many other projects expose their domain specifichistorical datasets using RDF. These datasets facilitate
their linkage to others using existing ontologies (seeSection 4.1.1), achieving shared goals with the old taskof historical record linkage. For instance, the Agora
project [2] aims at formally describing museum collections and linking their objects with historical context using the SEM [110] (Simple Event Model). Historical events are found elsewhere in historical data.
The FDR Pearl Harbor project links events, persons,dates, and correspondence found on government letters and memoranda on the surroundings of the Pearl
Harbor attack on 1941 between the US and Japanese
governments. All these entities are represented in RDF
to model a graph of historical knowledge about thatparticular event. From a more socio-historical point ofview, the Verrijkt Koninkrijk [131] project links RDF
concepts found on a structured version of De Jong’s
studies on pillarization of the Dutch society after theWorld War II. More focused on media, the Poli Media
project [86] mines the minutes of the general state debates to link historical entities to the archives of historical newspapers, radio bulletins and television programs. The goal is to create a unified historical search
environment, facilitating a cross-media analysis [55].

15

Some general purpose tools facilitate the creation ofhistorical Linked Data. The Fawcett toolkit [76] andthe Armadillo project [6] are good examples. The latter
exports RDF from any unstructured historical source,
producing an RDF graph of historical knowledge that
encodes the historical entities and their relationships
expressed in that source. Other tools like Open Refine[74] or TabLinker [104] are tailored to produce such
Linked Data from structured sources like tables (seeSection 3.2).
4.2. Text processing and mining
In text processing and mining we revise work that
deals with processing unstructured text. Textual resources play an important role in history research.
We especially survey work on automatically extracting historical entities (such events or persons) via Natural Language Processing (NLP) techniques. The purpose of NLP is to enable computers to derive meaningfrom human, natural, or unstructured language input(see Section 3.2).
Structuring historical information from textual resources for further analysis is the bottom line ofmany research projects. The interesting differences
come usually from the various source materials theseprojects mine. The general public-aimed Agora project[2] enriches museum collections with historical knowledge in order to help users place museum objects
in their historical contexts. To this end, Agora employs information extraction techniques from statistical natural language processing to extract named entities (actors, locations, times, event names) from textual resources such as Wikipedia and collection catalogues which are used to populate SEM [110] (seeSection 4.1.1) instances. From the object descriptions,also relevant historical entities are extracted which
can be linked to the events. To formalize this workflow, Segers et al. [97] present a prototype extraction
pipeline for extracting events and their properties fromtext using off-the-shelf natural language processing
tools such as named entity recognition and patternbased approaches. The main problem they encounteris that the notion of events is still ill-defined in NLP
research, and as such tools are not yet readily available.
Textual encoding of the media have also been
the source to extract historical knowledge in several projects. The Bridge project [19] aims at bringing more cohesion into Dutch television archives byfinding relevant links between the official archives
maintained at the Netherlands Institute for Sound and16

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Vision and other information sources such as program guides and broadcasting organizations websites.
It is thus focused on improving access to televisionarchives for media professionals. In order to do so, relevant entities are extracted from archives by using statistical NLP techniques. Furthermore, they will detect
interesting events in television archives by detecting
redundant stories, utilising the structure of the archive
to identify links between different entities [20]. The
Poli Media project [86] mines the text of minutes of
the general state debates to extract and link historicalentities from the archives of historical newspapers, radio bulletins and television programs.
The Historical Timeline Mining and Extraction(HiTiME) project [46] is aimed at detecting and structuring biographical events. To this end they analyze
biographies of persons from the Dutch union history
to create timelines that tell the life story of these persons, and social networks of the persons they interacted with. Van de Camp and Van den Bosch [128] describe an approach to build networks of historical persons by mining biographies for person names and relationships between persons. They use standard namedentity recognition tools and utilise the inherent structure of biographies (the topic of the biography is a particular person, and any persons mentioned in this biography should have something to do with this person)
to detect interpersonal relations.
Many ehumanities and ehistory projects are exploring document summary techniques or document enrichment techniques from NLP to aid search in theirarchives. One of these techniques is topic modelling,which can be used to add topic indicators to a document, which may help cluster search results or create
more fine grained indexes of archive records. Wittek
and Ravenek [142] explore the state of the art in topic
modelling techniques to index 19,000 letters of correspondences between 16th and 17th century Dutch scientists.
Other high-level text analysis methods, such as
frequency-based corpus analysis to compare e.g. workfrom different authors or investigation of other stylometry characteristics, are also popular in the ehumanities domain [30]. These methods are not domaindependent and fit more easily into the ehumanities researcher search-based toolbox.
The spectrum of tools to extract knowledge from
unstructured historical data is wide. Important contributions are essentially domain-independent [7], thus
not particularly focused on historical text processing.
Gangemi [37] presents a recent and complete com-

parison of generic knowledge extraction tools for the
Semantic Web, which will aid historical researchers
working in the phases 2 (enrichment) and 3 (editing) of
the historical information life cycle (see Section 3.1).
4.3. Search and retrieval
In search and retrieval we include systems that exploit semantic formalisms as a new way of indexing,
querying and accessing historical data, instead of relying on the traditional text-based or keyword-basedalgorithms. This task matches the phase 4 (retrieval)of the life cycle of historical information (see Section
3.1).
It is not a coincidence that a high number of contributions that aim at extraction of structured entities
from historical data also point at some desired systemable to improve search and retrieval of such entities.
Indeed, by means of constructing a semantic graphof historical knowledge, search and retrieval of thatknowledge, as well as indexing systems that give exact
pointers to the source in which particular historical entities are mentioned, can be easily built and improved.
The Agora [2] (museum collections), BRIDGE [19](historical TV metadata), CHOoral [24] (historical audio metadata), Historical Timeline Mining and Extraction (HiTiME) [46] (biographical events), Verrijkt
Koninkrijk [131] (Dutch post-war social clusters concepts) and FDR Pearl Harbor [85] (historical events
around Pearl Harbor attack on 1941) projects are all
good examples of this tendency. Once the knowledge
is successfully extracted from the historical sources
and formalized appropriately, entities structured thisway can be used for a graph-based search and retrieval,for instance through SPARQL queries (see Section2.2), although most systems use specific access methods [50]. Other projects, like the H-BOT [42] project,
use a natural language interface instead of a query system for retrieval of such historical structured knowledge.
Indexing of historical contents is another way of improving search and retrieval of historical data. Indexing and historical data storage systems have a long tradition in historical research [16]. CLIO [125] is a traditional example of such a system, nowadays indexing
is performed by XML annotation-oriented approaches,such as described by Robertson [90]. These initiatives
should consider the emerging RDFa, microformats andmicrodata technologies (see Section 2.2) to study theways they fit in the vast domain of historical text annotation systems.

A. Meroño-Peñuela et al. / Semantic Technologies for Historical Research: A Survey

4.4. Semantic interoperability
In this section we analyze to what extent contributions consider the problem of data integration anduse the Semantic Web to deal with it. The specificproblems encountered are data model mismatching,schema incompatibilities and disparate source formats. Semantic interoperability has much to do withdata integration, namely, how to commonly queryand uniformly represent data that come from multiple
sources (i.e. fitting several, probably non-compatible
data models).
Semantic heterogeneity of historical sources is especially present on social history projects. The North
Atlantic population project [70] (publication of microdata of several Atlantic countries) has this problem ofdata harmonization, in which heterogeneity of sources
requires an intense work on resolving data model inconsistency between datasets.
The source material for the Historical Sample of theNetherlands [48] (HSN) database consists mainly of
the certificates of birth, marriage and death, and of
the population registers. From those sources the life
courses of about 78.000 people born in the Netherlands
during the period 1812-1922 have been reconstructed.
Stored in a database and downloadable as files, this information forms a unique tool for research in Dutch
history and in the fields of sociology and demography.
As in the case of the HSN this type of sources is usually stored in archives, and, for the majority from amore remote past, not yet machine readable and noteasy to analyse with NLP techniques. There is one
major pitfall in linking this kind of data: extracting
data about persons, events, institutions, locations is
one thing, but linking to their different instantiations(for instance different name spellings, or persons withthe same name) and keeping good documentation is
the real challenge [62].
The CEDAR project [22], located in the crossroadsof the Semantic Web, statistical analysis and social history, exposes the Dutch historical census data in the
Semantic Web. Censuses are a great source of nonbiased socio-historical information, but they present
complex problems in both internal (i.e. between thetime series) and external (i.e. other datasets) interlinking [67].
The work developed by Sieber et al. [100] provides
a deep analysis of how semantic heterogeneity canbe addressed exclusively with semantic technologies,
and describes how to achieve success in environmentswith very disparate data models. In the history-related

17

domain of geographic information systems (GIS), already discussed in Section 4.1.1, Manso and Wachowicz [4] provide an extensive review on current issues ininteroperability.
4.4.1. Classification systems
Multiple publications in classification systems [31,39,68,102] are especially aimed at solving interoperability problems in historical data. Classification systems provide a standard mechanism to compare suchdata, but their specific implementation and effectiveness depends on the orientation towards source orgoals of the historical data (see Section 3.2.3) createdin phase 1 of the historical data life cycle (see Section
3.1).
When dealing with vast amounts of historical data,classification systems are a necessity in order to organize and make sense of the data. The main goal ofa classification system is therefore to put things intomeaningful groups [10]. This entails an allocation ofclasses which are created according to certain relations
or similarities. The main issue with historical classification systems is that they are not consistent overtime, making comparative historical studies problematic. Historical census data is a typical example of thisproblem [22,65]. Census data is the only historical dataon population characteristics which are not strongly
distorted and yields an extremely valuable source of
information for researchers [91].
However, major changes in the classification andcoding of the different censuses, have hindered comparative historical research in both past and present efforts [87]. Researchers are forced to create their own
classifications systems in order to answer their research question; however, this process often results indisparate systems, which are not comparable, containa lot of expert knowledge, different interpretations of
the data and could not be easily (re)used by other researchers. The fact that many of the modelling techniques are destructive in nature (we cannot go back tothe source) makes it even more cumbersome to comprehend these sources. In order to deal with the changing classifications and vast differences at both national
and international level, we need to connect the gaps
between the datasets and conform to certain standard
classification systems.
Currently several significant efforts have been madein this direction. The Integrated Public Use Microdata Series (IPUMS) project [52] for example faces
the problem of bridging 8 different occupational classification systems and a total of 3200 different cat-

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egories, containing the richest source of quantitative
information on the American population. The North
Atlantic Population Project [70] (NAPP) project provides a machine-readable database of nine censuses
from several countries. The main focus of the NAPP
project is to harmonize these data sets and link individuals across different censuses for longitudinal andcomparative analysis. Their linking strategy involves
the use of variables which do not change over time. In
this process records are only checked if there is an exact match for some variables, such as race and state ofbirth. Other variables like age and name variables are
permitted to have some variations. Another significant
historical classification system is the Historical International Standard Classification of Occupations [45]
(HISCO). As occupations are one of the most problematic variables in historical research, HISCO aims
to overcome the problem of changing occupational terminologies over time and space. It encodes historicaloccupations gathered from different historical sources
coming from different time periods, countries and languages, and classifies tens of thousands of occupational titles, linking these to short descriptions and images.
4.5. Transversal approaches
Finally, there are few but key contributions we haveclassified as being transversal, because they cover awide spectrum of the list of overlapping tasks between
the Semantic Web and historical research. They also
influence almost every phase in the historical information life cycle (see Section 3.1).
The CLIO system [125], a databank oriented system
for historians, is the first of such contributions. CLIO
was, for decades, the system for creating, enriching,organizing and retrieving historical knowledge from
historical data in the pre-Web era. Although not using
Semantic Web technologies (see Section 2.2), it had
a strong emphasis on semantics as key for structuringhistorical knowledge.
In the Linked Data universe, the Agora project [2] is
one of such transversal contributions. It generates historical RDF of events extracted using NLP techniquesfrom unstructured texts, uses it for enhanced searchand retrieval, improves semantic heterogeneity andgives context by linking to other datasets. Similarly,
the Verrijkt Koninkrijk [131] and Multilingual Accessto Large Spoken Archives (NSF-ITR/MALACH) [61]projects perform these tasks in their particular domains(see Section 4.1.2). The FDR Pearl Harbor project [85]also contributes on this line, but additionally opening
the very promising field of historical knowledge inference through the formalization and usage of historical OWL ontologies. All these are good examples on
how historical data get much richer when their semantics are explicitly expressed and they are interlinked
through standard vocabularies and ontologies.
Regarding tools, the Armadillo architecture of Semantic Web Services [6] and the Fawcett toolkit [76]contain the generic plot behind all these contributions, and cover the whole pipeline of semantic historical data management. The latter extracts RDF eventoriented triples from unstructured texts, and additionally allows historians to install a full semantic toolbox
with widgets to experiment with their data. Open Refine [74], in combination with its RDF-export plugin,
allows the extraction, transformation, modelling andpublishing of historical Linked Data when the sourcescome in tabular format.
Additionally, the theoretical study by Boonstra et al.
[16] envisages possibilities on how the Semantic Web
can enhance research by historians. It constitutes, besides, a major work on the evolution of historical computing, ehistory and historical information science, andgives a deep intuition on how computer science canhelp to solve ancient problems in historical research.

5. Solving historical problems
In this section we point to the open historical data
problems revisited in Section 3.3 which are addressedor solved by the Semantic Web contributions reviewed
in Section 4 as tasks. The mapping between the open
problems and the tasks is shown in Table 5.
The first interesting result is that some of the problems identified in historical sources (Section 3.3.1) aremostly solved by the approaches we review in historical ontologies (Section 4.1.1). Concretely, our perception is that the structuring of historical data andthe development of historical data models have beena success due to the creation of standard vocabularies
and ontologies. These ontologies aid historians to describe, at least, the baseline historical entities and relations in historical domains: events are combinationsof persons, places and moments in time when something historically relevant happened. The large numberof projects exposing historical Linked Data on the Web
using these ontologies (see Section 4.1.2) prove theirusefulness and success. There is space, though, for improvement. Although it is commonly agreed that cur-

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A. Meroño-Peñuela et al. / Semantic Technologies for Historical Research: A Survey
Open historical data
problems
Historical(3.3.1)sources

Relationships
between sources (3.3.2)
Historical(3.3.3)

Historical
ontologies(4.1.1)

Linkinghistorical data
(4.1.2)X◦

Text processing
and mining
(4.2)

X

Classification
systems (4.4.1)

Transversal
approaches
(4.5)

X
◦

analysisPresentation
sources (3.3.4)

Search andretrieval (4.3)◦

ofTable 5Mapping between the open problems of historical data (see Section 3.3) and the surveyed contributions in historical Semantic Web (see Section
4). The sign Xindicates that the problem is directly addressed in the Semantic Web task. The sign ◦ indicates that the problem is indirectly or
partially addressed in the Semantic Web task.

rent historical ontologies model the core semantics ofhistorical research, authors also agree that they are still
scarce and need further development [51,84].
As part of the problems in historical sources, provision of background historical knowledge has beensuccessful only partially. The infrastructure (Linked
Data cloud, SPARQL endpoints on historical data) is
set up and running. But the amount of historical data
available is still too low to give good support to any
historian creating historical datasets in the beginningof the life cycle (see Section 3.1). Consequently, little background knowledge can help today these historians in solving e.g. errors or inconsistencies at thatphase. Similarly, the generic infrastructure for provenance publishing and retrieval in the Semantic Web
is very mature and extensively used in other domains[133], but scarce or non existing in the historical domain although being identified as a very important requirement (see Section 3.3.1). The provision of suchprovenance on historical datasets needs to be guaranteed in projects using semantic technologies to publish
historical data.
Solutions to the problem of relationships between
sources are probably the greatest achievement of theapplication of semantic technologies to historical research. The large number of projects linking historical
data we survey in Section 4.1.2 proves that the Semantic Web delivers working solutions to the problem ofconnecting isolated historical data sources. The usageof developed ontologies and vocabularies has been key
to this end. Additionally, the existence of classification
systems (Section 4.4.1) helps on data comparability in
the Semantic Web. Because we see that the body of historical knowledge in the Semantic Web is still small,
we expect the problem of finding related links between

historical entities and datasets to grow in the future, although the Semantic Web has generic solutions for this[99].
The problems in historical analysis and presentation of sources (see Sections 3.3.3 and 3.3.4) are only
partially addressed in approaches we have classified
as transversal. These works cover a wide spectrum of
the life cycle of (semantic) historical data, including
analysis and presentation (phases 5 and 6, Section 3.1).
Consequently, they deal with some analyses and visualizations. However, there is a lack of contributions
tackling directly the problem, or considering explicitly
historical research requirements with respect to analysis and visualization. The transversal tools are hence
very generic, and they could be inappropriate for somehistorians. Therefore, it is very important to distinguish what analysis requirements are specific to historical research, and which ones are domain-independent.
Our hypothesis is that these problems overlap only partially with the goals of the Semantic Web (i.e. representing and linking meaning on the Web). However, historians could benefit from analysis and visualization tools for historical semantic data, not as specific as project-oriented, but not as generic as domainindependent.
In Table 5 all open problems have Semantic Web
tasks providing solutions, but not all tasks are mapped
to some historical open problem. Concretely, the tasksof text processing and mining (Section 4.2) and searchand retrieval (Section 4.3) do not seem to solve anyof the identified problems. Why do we find contributions on these areas? First, although not being identified by historians as primary problems, they constitute secondary problems that need to be solved
when representing and linking semantic historical

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data. These problems are not exclusively historical,
but they needed to be reimplemented in the Semantic
Web realm (e.g. natural language processing for extracting historical RDF triples, SPARQL to query historical semantic data on the Web). Secondly, the goals
these tasks aim at were quite well solved in historicalresearch before the inception of semantic technologies(e.g. manual input of historical data, SQL queries inhistorical relational databases), and thus historians did
not consider them into the primary problem space.

6. Open challenges
The use of semantic technologies has contributed
significantly to solving the open problems of historical data (see Section 5). However, there is a lot ofroom for improvement. The open problems are being
addressed as shown, but they are far from being solved
until they get additional attention. The scarce amountof historical data on the Semantic Web is a good example. Other problems, some more specific, some moregeneric, could be also tackled with semantic solutions.
In this section we explore some aspects of the Semantic Web that have not been used yet or could be furtherly exploited in historical research.
6.1. Semantics of time, change, language, uncertainty
and interpretation
Classifications and ontologies in history do exist,
but not for all areas, not in Semantic Web languages
and not always agreed upon. Although several historical ontologies have been developed (see Section 4.1.1),
these models are insufficient for the vast amount andvariety of historical data that still has to be publishedin the Semantic Web, especially when key issues forhistorians like interpretations or evidences need to bemodelled and conveniently linked. Historical ontologies and vocabularies have been a reality in recent
approaches. Ontologies describing classes and properties of some historical concern, such as concepts
around the Pearl Harbor attack in 1941 [50], are an
exciting modelling exercise for researchers but also anecessary step for better structuring historical information in the Web. Ontologies and vocabularies offer
a way of controlling the predicates, classes, properties and terms that the community uses as a standard
for describing factual and terminological knowledge
about history. Designing good ontologies for historical domains is also an area with plenty of challenges:

how can ontologies comprise the many conceptionsof history depending on the temporal dimension of
events described [51]? Moreover, how can differencesin meaning and relations between concepts be traced,as time and historical realities change these concepts[140]? To what extent these meaning differences relate to the complexity of the language (e.g. Latin, Middle languages) and uncertainty (e.g. fuzzy dates andlocations)? These questions, which comprise semantic technologies, knowledge acquisition and knowledge modelling techniques, are not yet completely understood and are a significant challenge in semantic
historical research. On the other side, over the centuries, dictionaries, thesaurus, classification systems
have been developed. How to mount those specificallygrown ordering principles to the Web in a way thatmakes them explorable and linkable to other ontologies is one interesting challenge which requires a close
collaboration between historians, knowing and designing those specific tools, and computer scientists, often
relying on much broader and generic ontologies.
6.2. Reasoning
From the point of view of Linked Data, ontologies
and vocabularies are designed in order to control theterms in which datasets may express data, as well as
the data model in which these data are represented.
However, in a more Semantic Web perspective, one
may expect these ontologies and vocabularies to facilitate new knowledge discovery; that is, to make explicit
some implicit fact that was not trivial to deduce for thehuman eye, especially in big knowledge bases.
Reasoning is one of the key mechanisms of the
Semantic Web still to be used in historical research.
The absence of specific methods and tools for automatic historical inference, so that new, implicit historical knowledge can be derived, is another issue. We
claim that reasoning could be fundamental for historical analysis 3.3.3 and tasks in the phase 5 (analysis) of
the historical information life cycle (see Section 3.1).
Historical ontologies can be used to facilitate historical knowledge discovery using reasoners. Assuming that a particular domain is completely formalized
as historical ontologies, then it is possible to run areasoner on these ontologies to produce derived, implicit rules and facts that were not present in the original model as explicit knowledge (i.e. specifically encoded in the ontology), but that were there as underlying knowledge. For instance, if an ontology describes,
on the one hand, the fact that a letter was sent from one

A. Meroño-Peñuela et al. / Semantic Technologies for Historical Research: A Survey

21

government to another, and on the other hand, the factthat governments have a person responsible of sending and receiving letters, then it may be possible forthe reasoner to infer what concrete persons sent andreceived what letters. As the knowledge base grows,
implicit knowledge is not evident anymore and reasoners can facilitate an enormous work and produce highvalue pieces of historical knowledge.
Since historians have different interpretations andno clear research question when starting an investigation, abductive reasoning (i.e. given the conclusions
and a rule, try to select possible premises that support the conclusion) may be more convenient than deductive reasoning (i.e. deduce true conclusions given
a premise and a rule) in historical research [23,47].
These would revert the order of some phases of thelife cycle of historical information (see Section 3.1),
generating a more bottom-up, data-based generation of
hypotheses supporting evidence. The impact of abductive reasoning in historical research and its relationshipwith the life cycle needs further study and clarification.
The introduction of any kind of reasoning in the life
cycle needs to be done with the goal of supporting,
not replacing, the task of the historian, who must keep
control of the implementation of the different phases.

stance). Suppose that: the occupational census of thedata exposes triples with countings on occupations (forexample, how many men and women worked in a particular occupation in a concrete city), the historicalregister of labour strikes contains countings on how
many people participated in labour strikes (number ofwomen and men, per occupation and city), and thegeneric classification system harmonizes names of theoccupations between both previous datasets (for example, gives a common number for representing occupation names that may vary between census occupations
and labour strike occupations). Then, it is clear thatseveral SPARQL queries can be constructed to givevery meaningful and interesting linked data to the historian. For instance, such a query may return, given acity and an occupation code, which ratio of men andwomen followed a particular well-known labour strike.
Another SPARQL query may return an ordered list of
historical labour strikes by relevance, according to several indicators (strike successfulness ratio, total number of workers on strike, density of people on strike
depending on the location, etc.). It is obvious that thepossibilities increase if we think of more related historical sources to link, like datasets describing historical
weather or historical geographical names and areas.

6.3. Linking more historical data

6.4. Flexibility of data models

We show in Section 4.1.2 that great efforts are being devoted to publish historical Linked Data. However, the amount of structured historical knowledge
available on the Web is still insufficient to aid tasks
that need high amounts and different kinds of historical background knowledge. While many different dataand information sources exist, they are not always interlinked. This isolation of historical data sources hampers that they can be found, but it also inhibits how
they can be further processed and connected.
One of the big claims of linked data is that, by linking datasets, relations established between nodes ofthese datasets highly enrich the information containedin them. That way, browsing datasets is not an isolated
task anymore: by allowing users (and machines) to explore entities through their predicate links, data get
new meanings, uncountable contexts and useful perspectives for historians.
For example, consider a scenario with three different
SPARQL endpoints exposing RDF triples of a census
with occupational data, a historical register of labour
strikes, and a generic classification system for occupations (in the context of one particular country, for in-

It is considered to be a bad practice in historical research not doing the historical data modelling at phase
1 of the historical information life cycle (see Section
3.1). The choice of a particular data model to represent historical data is a critical issue for most historical computing projects. The election of some appropriate data model may seem a good design decision atsome stage of the project. However, new requirements,
research directions or stakeholder priorities may convert that data model into an obstacle more than an aid.
Flexibility of data with respect to the data model used
to represent historical domains is desired to avoid restructuring entire databases. Comparison in historicalresearch requires flexibility of the models to be able tomatch them to one another. At the end, that enforces
historians to make their data selection and processing
dependent of a certain data model that can not be easily
replaced or altered if needed. This happens usually inenvironments with changeable and creep requirements[53].
Applying semantic technologies and Linked Data
principles to historical data may have a major advantage regarding historical data models, providing flexi-

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A. Meroño-Peñuela et al. / Semantic Technologies for Historical Research: A Survey

bility at the historical data modelling phase. Two different approaches regarding historical data modelling
have been followed traditionally in historical computing: the source-oriented representation, and the modeloriented (also known as goal-oriented) representation(see Section 3.2.3). Do semantic technologies allow a
flexible representation of historical data? Can the Semantic Web found a new standard on a source-andgoal, hybrid approach? RDF databases (see Section2.2) can store the middleware representation of further views on the data [65]. These views can be modelled as close to any particular historical interpretation
as needed. This way, the decision of what data modelsuits better the historical source can be postponed until the very end of the life cycle (see Section 3.1), or
adopted as early as necessary.
Moreover, additional questions arise when considering the traditional perspectives on data modelling: theconceptual, logical and physical data models. These
perspectives help in detaching data management technology, like relational databases or RDF triplestores,
from conceptual schemas (i.e. the semantics of a domain). While conceptual data models are currently
shared on the Web as e.g. historical ontologies (seeSection 4.1.1), the flexibility of the whole modellingstack towards semantic changes needs to be better understood.
6.5. Non-destructive data transformations
The non-flexibility of data models (see Section 6.4)
is related to the non-flexibility of historical data transformations. Historical data are modified in the life cycle of historical information (see Section 3.1). But if
update, enrichment, analytic and interpretative operations are not controlled, these transformations leadto different historical data representations which canhardly be related to each other any more, nor in terms
of provenance nor in terms of relatedness.
Another issue is supporting data transformations under two constraints: (a) without modifying source data
(so the originals stay intact); and (b) with tracking ofchanges. Consequently, destructive updates are a major concern when selecting, aggregating and modifying historical data. On the one hand, modifications to
specific encodings (CSV, spreadsheets, XML) do notsupport non-destructive updates, and version control
systems are necessary to retrieve previous states. On
the other hand, relational databases can be inefficientwhen querying all recorded transformations, edits andmanipulations.

Non-destructive updates are well supported by current Semantic Web technology like SPARQL (see Section 2.2). SPARQL CONSTRUCT allows the construction of RDF triples according to the supplied graphpattern, facilitating data transformations without altering consistency of previous states in the knowledge
base. SPARQL SELECT selects, according to somegraph pattern, the desired data in the RDF graph, anddisposes them according to any desired view format(for example, columns matching some interesting historical variables). However, standardization on how semantic technologies cover data transformations in allphases of the historical data life cycle (see Section 3.1)
is needed.

7. Discussion and conclusions
In this paper we present a general overview of semantic technologies applied to historical research. We
describe a general approach to historical research andthe Semantic Web, and motivate why the combinationof the two is an interesting field of research. We introduce core elements of historical research, such as
the life cycle of historical information, several classifications for historical data, and the open problems
shared by historians and computer scientists. Then, we
overview contributions to the young historical Semantic Web in form of papers, projects and tools, articulating the work into several tasks and trends within thesetasks. We provide a mapping to see to what extent thework on these tasks is helping to solve the open problems of historical data and historical research. Finally,
we dig out a list of interesting open challenges for the
future, like working out the semantics of critical aspects for historians, such as interpretation and time,and encouraging reasoning in the historical SemanticWeb.
It is interesting to observe the sparsity in Tables 1,
2, 3 and 4. There is a significant difference in the number of empty spaces (i.e. specificity of the contributions) between Tables 1 and 4 (papers and tools, ontologies), and Tables 2 and 3 (projects and online resources). While the former set has essentially lots ofholes, the latter has lots of complete lines. The reason for this is probably the specificity researchers thinkresearch papers and useful tools need. Usually written by computer scientists, papers and tools need to
be grounded and tackle a very concrete problem tobe worth written or implemented. On the other hand,projects (Table 2) are written in a very generic way

A. Meroño-Peñuela et al. / Semantic Technologies for Historical Research: A Survey

covering all tasks, with probably intensive participation of historians and clear aims to solve the whole
pipeline. In practice, though, these goals are materialized in very concrete research contributions. This leads
us to think that Semantic Web solutions need very specific requirements in order to be correctly deployed inhistory. They need to be applied to historical data in acomplex, layered and properly adapted pipeline. Good
practices and standards, and their relationship with thelife cycle of historical information, are still needed forthe field to continue evolving.
We show how the Semantic Web and history communities understand the need for representing inner semantics implicitly contained in historical sources, andhow these semantics can be conveniently identified,formalized and linked. With the appropriate pipelines,
algorithms can extract entities from digital historicalsources and transform these occurrences into RDF
triples, according to some historical ontology or vocabulary. These entities can be linked between themand with other historical Linked Data, contributing toan open, world wide, online persistent graph of historical knowledge: an historical Semantic Web. The work
presented in this survey contributes in one phase or another in this graph-building pipeline. We leave to thereader if this historical Semantic Web building pipeline
is, in fact, the Semantic Web version of the life cycleof historical information.
The challenge of the realisation of a historical Semantic Web meeting as many requirements as possible may bring new facilities for a number of stakeholders. On the one hand, humanities researchers, also outside history, will be able to integrate the historical Semantic Web to their own information life cycle. They
will be able to search, retrieve and compare historicalknowledge and use it for the construction of their narratives, still the final outcome of historical research. On
the other hand, practitioners will be able to search newdata sources to develop history-aware applications for
public institutions, private companies and citizens.

Acknowledgements
The work on which this paper is based has been supported by the Computational Humanities Programmeof the Royal Netherlands Academy of Arts and Sciences, under the auspices of the CEDAR project(see http://ehumanities.nl), the European
Union’s 7th Framework Programme via the News-

23

Reader Project (ICT-316404), and by NWO’s CATCH
programme via the Agora project, grant 640.004.801.
This work has been supported by the Dutch national
programmes COMMIT.
The authors want to thank all contributing colleagues, especially the interviewees Onno Boonstra,Peter Doorn, Jan Kok, Henk Laloli and Dirk Roorda.

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