Large Scale Analysis of Semantic and Temporal Aspects in
Cultural Heritage Collection’s Search
Yasunobu Sumikawa

Adam Jatowt

Tokyo Metropolitan University
Tokyo, Japansumikawa-yasunobu@tmu.ac.jp

Kyoto University
Kyoto, Japan
adam@dl.kuis.kyoto-u.ac.jp

Antoine Doucet

Jean-Philippe Moreux

University of La Rochelle
La Rochelle, France
antoine.doucet@univ-lr.fr

National Library of France
Paris, France
jean-philippe.moreux@bnf.fr

ABSTRACT
Cultural Heritage (CH) collections store and represent numerous
historical objects such as images and documents, which are often
of unique type and of high cultural and historical value. While themaintained objects are typically well-understood and analyzed, relatively less is known about what type of content is actually interesting
to the searching users and how they find such content. These kinds ofanalyses could help us to understand for what purposes users use
cultural heritage collections and could lead to user intent classification and understanding. In this paper we report the results of alarge-scale exploratory analysis based on a 15-month long snapshotof query logs generated at the online portal of the National Libraryof France. Besides understanding the nature of content search incultural heritage collections and digital libraries, the results of ourstudy can be used for designing content recommendation systems
and could help to improve time-aware search applications.

KEYWORDS
Digital library, digital history, temporal query analysis

1

INTRODUCTION

A rapid increase of text content stored in digital archives is theresult of widespread digitization and content curation initiatives
aiming at preserving our heritage. Millions of newspaper articles,
books, past snapshots of web pages or other document genres andobjects are digitized and made accessible and searchable. Thanks tothe efforts of digital archiving, the number of historical information
one can access is then rapidly increasing. This offers opportunities
for computational studies on explicit references to past events andopens up novel perspectives for the study of collective memories aswell as the pursuit of public history. While computational collective
memory studies have already been conducted on news articles [1, 6],
Wikipedia [10, 11, 13, 15] and microblogs [5, 21], with regard tocultural heritage collections, few projects focused on user interest
towards the type of stored content. In order to be able to successfully
disseminate content of cultural heritage collections and to attract
new users it is imperative to understand user needs, user experience,and search tactics. Query logs offer rich content towards this end.
The distinguishing feature of search in heritage collections andoften also in other constrained collections is the high probability of awithin-collection navigational search intent (also termed as knownitem search), when one uses the well-known Broder’s taxonomy[2]. In other words, rather than learning about some concept or
answering some question as in typical information seeking process,users are more likely to wish to access certain digital artifacts or
documents. Field (zone) based search, commonly associated withthe choice over controlled vocabulary, is also typical for cultural
heritage collections. For example, users input metadata information
for required objects such as author names, creation dates, etc.
Nevertheless, many aspects of search withing CH collections arestill unclear. Among others our study is guided by the following
key questions:(1) What do people search for in digital historical collections
and what kinds of metadata filtering they use?
(2) What is the time horizon of such searches?
(3) How are collective memories expressed in search logs?
We approach these and other related questions by investigating
portions of query logs generated from over the course of one year.
As the underlying dataset we use request logs from the online portal
to the digital library of the National Library of France (Bibliothèquenationale de France) (BnF) called Gallica which has been open
to the public since 19971 . Gallica contains printed materials suchas books, journals, newspapers, printed music, as well as otherdocument types. It also serves graphic materials like engravings,
maps, photographs and sound recordings. At present, Gallica holds
5M documents2 .
Based on the collected user interaction data, we investigate thedistinguishing characteristics of queries from the semantic andtemporal viewpoints. Among the key aspects we research are time
horizons, concerned entities and the prevalence of different typesof result filtering. Particularly, we are not aware of similar studies
of search logs that would elucidate patterns of chronological result
filtering that we perform in this work. As archival artifacts havenatural temporal ordering, this kind of studies are quite importantfor understanding the intents and tactics of users.
Besides being a novel approach towards the general questions
on semantic and temporal aspects of ways in which users searchin cultural heritage collections as well as on how the past relates
1 https://gallica.bnf.fr/accueil/en/content/accueil-en?mode=desktop
2 https://gallica.bnf.fr/GallicaEnChiffres

to our lives, our investigations can be beneficial to several applications. First, the archival appraisal decisions can be supported
thanks to the knowledge of user interest concerning past artifacts.
It is well-known that archives have limited resources and funds forcollecting, storing and managing objects, even, ones of digital character, and there must be a well-thought selection process conducted
for deciding which objects should be included and for how long inarchives. Next, the construction of specialized content detection andrecommendation systems can be informed by the reported results.
The objective of such systems would be to facilitate sharing of historical knowledge. Understanding the types of popular searched
content and the context of sharing can be helpful for designingeffective recommendation systems.
To sum up we make the following contributions in this paper:(1) We undertake analysis of cultural heritage search based ona large scale data collected over a year.
(2) We propose to analyze the search log from entity-oriented
and temporal viewpoints to uncover what types of and how
the entities are searched for as well as how temporal filters
are set by searchers.
(3) We outline novel research directions of analysis and mention
potential applications that can better utilize archival contents
for improving user experience.
The remainder of this paper is structured as follows. In the next
section we present the related work. In Section 3 we describe thedata collection and processing steps. Section 4 provides the findingsof the semantic and temporal analysis. We then include additional
discussions of limitations of our study in Section 5. Finally, the lastsection concludes the paper and outlines our future work.

2 RELATED WORK
2.1 Search Log Analysis in Digital Libraries
A lot of researches focused on analyzing query logs of search engines such as Web search engines [14]. User queries, session information, short-term or long-term search tasks as well as interaction
patterns were studied among others.
Information access in digital libraries and constrained document
collections such as scholarly repositories has been also subject of
research interest. Sfakakis and Kapidakis [20] compared in 2002
the usage of a Digital Library with many different categories ofcollections concluding that the access points the users mostly referto depend heavily on the type of content of the collection, the detailsof the existing metadata and the target user group. The authors
have also found that most users tend to use simple query structures
such as ones containing a single search term, and they tend to dovery few and primitive operations to accomplish their requests.
However, as users get more experienced, they reduce the number ofoperations in their sessions. Another work [4] presented the resultsof a large-scale case-study at the Royal Library of Belgium based ona data set of 83k queries from 29k visits over a year long period of thehistorical newspapers platform BelgicaPress (associated with theState Archives of Belgium). The authors investigated the applicationof simple text mining methods such as query clustering in cultural
heritage settings. However, no other results were given except for
few example data instances.

De Wilde et al. [8] analyzed search queries over 4 years’ long
period against the Historische Kranten corpus which contains overa million articles compiled from 41 Belgian newspapers publishedbetween 1818 and 1972 and written in Dutch, French, and English,that focus on the city of Ypres and its neighbourhood. Based on 10
top most popular queries they found that locations are especiallycommon. Ceccarelli et al. [3] analyzed query log consisting of over1 million queries issued during 6 months in order to find improvements useful for increasing the usability levels of the Europeana
Portal. They found among others that about 20% users use filteringby type, the average query length is 1.86 terms and most of searchsessions finish before 5 minutes. They also proposed to develop aquery recommender system based on the analysis results. Lown etal. [16] through the search log analysis of NC State University’s(NCSU) Libraries have observed that the default search box labeled“All” was used for 73.7 percent of searches while the other specialized sources (e.g., catalog labeled “Books & Media”) were used lessfrequently. Same as in our case D’Alché-Buc et al. [7] have alsostudied Gallica search logs to analyze search patters in cultural
heritage collections. They especially focused on classification ofuser sessions.
Our work differs in several aspects from these works. First, ourfocus is on entities included in queries and on the way in whichusers use temporal filters to constrain search results. We also investigate in detail the usage of metadata filters. Finally, unlike the otherworks we utilize large scale query log compiled over relatively longtime period.

2.2

Named Entity Recognition

Finally, Named Entity Recognition (NER) and Classification (NEC)methods, especially ones related to query parsing, are also related
to our work [12, 19, 22, 23]. The task consists of assigning an entity
type such as a person, location, event or organization to entities
identified from search queries. Many modern web search engines
use information on named entities stored in Knowledge Graphs
to be presented alongside organic result pages when queries areentity-centric [22, 23]. This means the need for recognizing anddisambiguating entities mentioned in queries or entities strongly
related to user queries [9, 22].

3

DATA COLLECTION

Dataset. As mentioned before, we use the interaction log from theonline portal of the National Library of France (BnF) called Gallica
3 . We show the basic statistics of the dataset in Tab. 1 such as thenumbers of records and the numbers of requests. We use records
over the period of 15 months.
Table 1: Dataset statistics.
DurationJan. 2016 ∼ Apr. 2017

Num. of records

2,844,553,550

Num. of requests

1,126,787,556

Num. of requests using temporal filters

35,040,848

Num. of requests using resource filters

333,689,345

Num. of requests using DC filters

26,075,373

Num. of requests using temporal and resource filters

5,665,126

3 https://gallica.bnf.fr/accueil/en/content/accueil-en?mode=desktop

As it is impossible to extract parameters of POST methods fromlog file, all the interaction-related information we focused on was
sent by GET methods, meaning that we analyzed content embedded
in URLs. In Tab. 1, a record indicates any kinds of interactions between a user and the BnF server whereas a request is an interaction
by GET method.
Parameters. We collected the following parameters from eachrequest:(1) query: it includes keywords input by users. This parameter also contains information about sorting, filtering andmatching methods. These three conditions can be used in
the advanced search settings of Gallica. As for the filtering,
Gallica provides several ways, such as resource, temporal,
language, theme, types of documents and so on.
(2) location: shows the name of the country from where thequery is issued(3) timestamp: represents a time when a user issues a query.
Preprocessing. After extraction of keywords from the queryparameter, the following pre-processing steps were performed. First,
all the words were lower-cased. Stopwords were then removed andthe remaining words were subject to stemming. For stop-words
removal and stemming process, we used Python NLTK functions
designed for French language4 .

4

ANALYSIS

In this section, we first investigate characteristic features of requests
focusing on locations and metadata based filtering. We then perform
entity analysis for query words. Finally, we show results from thetemporal analysis of queries based on temporal filters set by users.
At the beginning of each subsection we list in rectangle boxes theguiding questions behind the analysis outlined in the subsection.

4.1

Spatial Analysis

4.2

Metadata based Filtering Analysis

Q. What kind of filters do searchers apply?
Q. What kinds of metadata and in what combinations arecommonly used when searching based on Dublin Core?

1.5% 1.0%
3.4%
8.7%

9.6%

14.0%

59.8%

Figure 2: Top filters used with query words.
We now look at the distribution of filters used in queries shownin Fig. 2. Gallica’s advanced search not only allows to choose the underlying resources, but also offers several options such as AND/OR
retrieval, setting temporal filtering, specific languages used fordescribing digital items, and so on.
As it can be seen in Fig. 2, dc and arkpress were the main filters
besides the default gallica. The Dublin Core (DC) schema is asmall set of vocabulary commonly used to describe a wide range ofdigital resources (video, images, web pages, etc.). The schema describes metadata vocabularies that include sets of resource classes,
type vocabularies, vocabulary encoding schemes and syntax encoding schemes. Its details are given in the official web site5 . In Gallicasettings, arkpress is used to perform queries within a newspapers
title and subgallica is used to filter the results page (facets search).

Q. What is spatial distribution of originating requests?
50,000,000

Before we start query keywords analysis, we briefly show thespatial distribution of requests. Fig. 1 displays the top-10 places
where users input requests with their corresponding rates. "NULL"
indicates that there is no location information recorded at log. Naturally, France is the top country which is not surprising since data ishosted in the National Library of France. We can however observethat many European countries are included in this figure.

40,000,000
30,000,000
20,000,000
3.2%2.3%
3.8%
10,000,000
4.1%
5.7%

9.4%

2.2% 2.0%
2.2%
2.4%
2.8%
2.9%
3.1%

52.6%

5.2%
8.6%

68.6%

France
U. S.
NULL
Italy
Algeria
Germany
Belgium
Spain
CanadaU. K.

Figure 1: Top-10 countries from which requests originate.
4 https://www.nltk.org/

gallicadc
arkpressnone
subgallica
provenance
text
noticebibliothequeaccess9.8%

gallica all
dc.type all
arkpress all
gallica adj
dc.creator all
dc.title all
subgallica all
dc.contributor all
dc.relation all
provenance all
provenance adj
text alldc.identifier all
bibliotheque adj
notice all
dc.subject all
dc.type adj
dc.format adj
dc.language all
text adj

Figure 3: Top-20 filter–parameter combinations.
Fig. 3 plots more detailed ratios of metadata filtering styles. The
advanced queries also include three options (all, any and adj)
concerning the text matching way that Gallica enables. The firstone indicates the case of matching of all words. The second one
is used to find matches on, at least, one of the words. The third
one is the exact/equal expression (i.e., a phrase like search which is
5 http://dublincore.org/documents/dces/typically available in search engines when using quotes). We cansee that using Gallica with matching of all words occurs over 50%of the times. In addition, there are 3 kinds of selectors (Gallica, dc
and arkpress) whose total ratio amounts to over 80%. In the top-20,
it can be seen that the all parameter (i.e., matching all words) isthe most common followed by the adj parameter (the exact/equal
expression). However, we should keep in mind that a lot of theseparameters, particularly all/adj, are tuned by the Gallica web app
itself. Anyway, the results suggest that users are generally reluctant
to use advance search options.
Table 2: Common combinations of resources in the same
queries.
Rank
1
2
3
4
5
6
7
8
9
10

Resource
dc
dewey
dc
noticegallicagallicagallicagallicadc
dc

Resourcedc
dewey
noticenoticegallicaarkpresssubgallica
dcsubgallica
jeanFinally, Fig. 5 shows top-15 inputs in dc.type. We can see thatthe top-5 choices are related to periodical and books (periodical,
monograph), manuscripts, images or maps. This result confirms thepreviously reported results of user survey and web analysis [18].
periodical (fascicule)monographimage
map
manuscript
score (music)
soundobjectaudio
periodical (periodique)fascicule paris
windows
monograph encyclopedia
ige
part

0

Num
6,450,642
3,252,938
700,619
267,614
396
306
66
66
66
12

2,000

4
6
8
1
1
,000 ,000,000 ,000,000 ,000,0000,000,0002,000,000

Figure 5: Top-15 data used in dc.type filed.

4.3

Entity Analysis

Q. What is the distribution of searched entity types?
Q. What entities are most popular?
In this section, we investigate what kind of entities and whatkind of entity types searchers input in their queries.
300,000

text adj
subgallica all
notice all
gallica all
gallica adj
dewey alldc.type all
dc.title all
dc.title adj
dc.subject all
dc.subject adj
dc.source alldc.language alldc.creator all
dc.contributor all
arkpress all

7.4%
250,000
200,000

8.0%
creatortype
contributor
title
relation

10.4%

150,000
100,000

21.7%

52.5%

50,0000l
dj al
t a lica
tex albg allsu ice alltno lica adjlga lica ll
l aga ey allw
de type all.
dc title adj ll.
dc title ct adj. e
dc subj ct a l. e l
dc subj ce a all.
dc souruage l ll l.
dc lang or a or a
. t t
dc crea ribu
. t
dc con s all.
dc presark

Figure 4: Numbers of filter–parameter combinations used together.
As in the advanced search option Gallica supports multiple
search conditions for each query, we analyze combinations of filters and filters–parameters. First, Tab. 2 shows the numbers ofco-used filters in queries. We can observe that dc and notice arecommonly used together. dewey tends to be used separately. To
better understand why some users use the same types of filterswith different parameters, Fig. 4 plots numbers of co-used filterswith their parameters. We can see that two kinds of parameters,
all and adj, are the most common in this figure. dc.subject all
and dc.subject adj tend to be used together. In addition, notice
all and dc.title all are used with dc.source all, dc.subject
adj, dc.subject all, dc.title adj and dc.title all.

Figure 6: Top-5 metadata fields defined in dc

4.3.1 Entities in DC Fields. At first, we focus on analyzing whattypes of DC fields users tend to use. Fig. 6 shows the distributionof fields related to DC. Three fields, dc.creator, dc.contributor
and dc.title, that can be regarded as entity representing ones aremost prevalent. Approximately 71% queries that are subject to DC
filter use at least one of them. Following, we list the top-20 entities
used with these three fields in Figs. 7, 8 and 9, respectively. Notethat the top-20 analysis provides only a partial picture as it reflects
only the usage of the most common entities in DC fields. We leave
the more extensive analysis of less common entities as future work.
We manually checked the nationalities, birth and death years
as well as the occupations of the persons listed in Fig. 7. Amongthem, 10 are French, 3 are Portuguese6 and 2 are Brazilians 7 . They
all lived around 1800 and all were born before 1860. After counting
6 One Portuguese (Ramalho Ortigã o) taught French at a college in Porto, and another Portuguese
José Feliciano de Castilho Barreto e Noronha studied medicine in France.
7 Henrique de Beaurepaire-Rohan was traveller and explorer of French extraction. Pedro II of Brazil
was elected to the French Academy of Sciences and died in Paris.

duperrey, louis isidore
vasconcelos, joaquim de**Beaurepaire-Rohangalvão, benjamin franklin ramiz
mouchez, ernest
delisle, léopold
monti
wiener, charles
castilla y marquesado, ramón
lavallée, josepharaujo, joaquim dealcantara, dom pedro ii dbastard d’estang, auguste dechassant, alphonseorthigão, ramalho**José Feliciano**Léonel Antoine
belloc, louise swanton
waldor, mélanie
visino, tito
0

20,00040,00060,00080,000100,000

l’ouest-eclair
année universitairel’ouest, le vrai
paris-soirgazette des beaux-arts**Annuaire-alm. du Commerce**L'Auto**J.-r. hebd. de propagande**J. quotidien d' informations
mémorial de sainte-hélena
fonds dominique houdart - jeanne**J. littérature et des beaux-arts
l’ouest-eclair
les français peints par eux-mêmes
**Inventaire-sommairecathédrale notre-dame de parisle mot al-qur'ān
paris-plaisirs
gare saint-lazare
terre de feu-patagonie
0

Figure 7: Top-20 inputs used with dc.creator field. "**" markindicates abbreviated names of entities are: **BeaurepaireRohan (beaurepaire rohan, henrique pedro carlos), **José
Feliciano (noronha, josé feliciano de castilho de barretoe), **Léonel Antonie Feliciano (noronha, josé feliciano decastilho de barreto e)

the number of occupations8 we found that 8 persons worked asuniversity faculty, 4 were politicians and 3 were army officers. There
were also 2 journalists, 2 poets, 2 writers and 1 artist.
**Mondial Photo-presse
buffon, georges-louis leclercrousseau, jean-jacques**Eugène-Emmanuelchateaubriand, françois-renédeproudhon, pierre-josephsainte-beuve, charles-augustin
grétry, andré-ernest-modeste
pays-bas
ouest-france
gossec, françois-joseph
mercier, louis-sébastien
blondel, jacques-françois
huysmans, joris-karl
marat, jean-paulrameau, jean-philippe
bellange, jacques 1575-1616**Antoine-joseph
grasset, eugène-samuel**Jean-antoine-nicolas
0

20,00040,00060,00080,000 100,000

Figure 9: Top-20 inputs used with dc.title field. "**" markindicates abbreviated names of entities are: **Annuairealm. du Commerce (annuaire-almanach du commerce, del’industrie), **L’Auto (l’auto-vélo: automobilisme, cyclisme),**J.-r. hebd. de propagande (le populaire: journal-revue hebd.
de propagande), **J. quotidien d’informations (l’ouest, levrai: journal quotidien d’informations), **J. littérature et desbeaux-arts (l’artiste: journal de la littérature et des beauxarts), ** (inventaire-sommaire des archives départementales
antérieures)
when the documents associated with most of these queries were
published.

10,00020,00030,00040,00050,000Figure 8: Top-20 inputs used with dc.contributor. "**" markindicates abbreviated names of entities are: **Mondial
Photo-presse (agence de presse mondial photo-presse),**Eugène-Emmanuel (viollet-le-duc, eugène-emmanuel),**Antoine-joseph (dezallier d’argenville, antoine-joseph),**Jean-antoine-nicolas (condorcet, jean-antoine-nicolas decaritat)We did the same for Fig. 8 as for Fig. 7. In Fig. 8, 3 entity types
are found: 2 media groups and 17 persons (the main nationality of
persons was French: 14 French, 2 Swiss and 1 Belgium). Similar tothe case of dc.creator, the 19th century is the main period of the
lifetimes of these entities.
Newspaper articles, books, journals and reviews as well as a
drama (True West) are among the most common inputs in dc.title
field that are displayed in Fig. 9. Interestingly, locations Gare SaintLazare, Notre-Dame de Paris and Tierra del Fuego appear in the
ranking, too. Similar to the results of the two metadata fields
creator and contributor, the 19th century is the main period
8 Several persons had more than one occupation at the same time.

4.3.2 Entities in Text Input. Next, we extract entities from within
free text inputs, i.e., from query keywords issued by users which
are not related to any particular metadata field. First, we mapped
all queries into DBpedia to extract entities from texts. We thendetermined their types using DBpedia in order to arrange theminto 5 basic types: places, persons, events, groups and others. Fig. 10shows the distribution of the entity types obtained from the free text
inputs. Places is definitely the most popular entity type in the issued
queries followed by persons. This is somehow natural as people
often recall the past using the names of places where historicalevents happened as they may be interested in documents/objects
related to the histories of locations they live at or are visiting.

6.0%
8.5%

4.7% 1.3%

Place
Person
Other
Group
Event
79.6%

Figure 10: Ratio of types defined using DBPedia.
Tab. 3 lists also the top-10 entities per each type. Naturally,
French entities occupy many top places (e.g., 4 French persons,
4 cities in France and at least 3 French groups). In the Person column, 2 singers (Larusso and Wallen) and 2 philosophers (Boethiusand Voltaire) are included. We can also see that there are 3 musicgroups in the Group column, which also suggests that musician
profession is a common occupation of sought persons. Interestingly,
after investigating the lifetimes we found that the musicians andmusic bands are rather present entities (their birth or union years
being between 1978 to 2006). For the Group column, in addition
to the 3 music groups there are many corporations (e.g., Renault,Michelin and Commer). Many of the corporations are characterized by rather long histories having been founded in the 19th or
early 20th centuries. In the Place column, among others, there are 4
French cities and 3 cities (Laghouat, Mahanoro and Kabylie) whose
history largely connected with France.
This automatic analysis mapping user queries to DBpedia showsa different trend from a detailed manual analysis of top-1,000 queries[17]. The manual analysis revealed that works (periodicals, monographs, etc.) are actually the most common queried entity type(38%) followed by persons (26%). As automatic mapping of user
queries with work titles and person names is not an easy task, ourautomatic analysis may increase the ratio of places.

temporal span of content stored in Gallica in Tab. 4, from which
we can observe that most of the content comes from the 19th and20th centuries (40% and 46%, respectively).
Next, we collect all time constraints from parameters of requests. As it was shown in Tab. 1 there are over 35 million requests in a dataset for which a time filtering was applied. The
time filtering of search results is implemented by a parameter
gallicapublication_data. We then convert all the collected temporal expressions from the parameter into corresponding durations.
For example, if there is a "1900 ≤ gallicapublication_data ≤1950" in a query’s parameter, the temporal expression is recognizedas [1900, 1950]. In case when no start year is given, we set AD 1 as
the start year. On the other hand, when no end year is specified,
we set the year when the query was posted as the end year.
1914

1914

Start
250,000

1800

200,000

1850 1870

1890

100,000

Table 3: Top-10 entities per type by DBpedia.
Rank
1
2
3
4
5
6
7
8
9
10

Persons
Boethius
Polus
LarussoWallenGabrielEtteilla
Medea
Taurinus
Voltaire
Ptahhotep

Place
Rambouillet
Maizy
Laghouat
Chile
MayotteMahanoro
Goa
Kabylie
Paris
FranceGroup
Renault
SwitchfootMichelin
Massai
SomalisBoer
Babyshambles
Ovni
AngelisAnimaux

Overall, from Figs. 7, 8, 9 and Tab. 3, we can observe that themost popular entities were ones active in the 19th century andthat locations and persons are especially common. When analyzing
persons and groups, we notice that there are many musicians/artists,
writers and scientists as well as music bands or corporations.

4.4

Temporal Analysis

Q. What dates are commonly used for filtering results?
Q. How long are on average the filtering time spans?
Q. How similar are queries issued for different centuries?

Table 4: Temporal coverage of Gallica content.
Century
2nd
10th
11th
12th
13th
14th
15th

Num. of contents
759 (0.02%)
1,010 (0.03%)
1,128 (0.03%)
2,150 (0.05%)
3,324 (0.08%)
4,900 (0.13%)
11,903 (0.30%)

Century
16th
17th
18th
19th
20th
21st

Num. of contents
39,239 (1.00%)
81,781 (2.09%)
229,609 (5.87%)
1,603,188 (40.97%)
1,821,245 (46.54%)
112,706 (2.88%)

We next analyze the way in which users filter the results bytime. First, to provide background context, we start by showing the1700

01840 1860 1880 1900 1920 1940 1960

1400

150,000
1300
100,000

1600
1750

1200
50,0001900

1500

200,000

1000

1100

0
800

1000

1200

1400

1600

1800

2000

Figure 11: Distribution of start years in filters using
gallicapublication_data.

4.4.1 Start Dates in Filtered Time Spans. First, Fig. 11 plots the distribution of start years of the gallicapublication_data. Looking
at the figure (and also at the zoomed in plot in the inner graph), we
can see that the number of start years is usually rapidly increasing
towards the present, yet relatively fewer start dates are set at thelast decades of the 20th century and onwards. In addition, several
significant peaks are visible in Fig. 11. For example, the peaks are
noticed for rounds dates from 1000 to 1900 which denote the start of
new centuries. We can also see peaks on years starting new decades
which are especially common in the 19th century (e.g., the zoomed
plot in the inner graph shows such peaks from 1850 to 1900).
Also, it can be observed that on average non-round dates like1938 or 1911 are more common in the 20th century than in any
other century. Anyway, in general, the recent past is referred tomore than the distant past. This is intuitive and correlates withthe previous study conducted on news articles related to different
countries [1]. Finally, we can see that there are two peaks on years
1750 and 1914, the latter being the most common start date in theentire dataset. To understand why users use often these two wecollected the top-10 words used with these two filtering choices,and present them in Tab. 5.

Table 5: Top-10 words used with peaks of 1750 and 1914.
We translated some of French words into English whenthe meaning was not immediately clear. "*" mark indicates words translated into English which are: *anatomy(anatomie), *National Library of France (Bibliothèque nationale de France), *Gallipoli Campaign (1915) (Dardanelles,
expédition des 1915), *aviator (aviateur), *fatherhood (paternitè), *recognition (reconnaissance).
Rank
1

1750 (Freq.)
roquefort (25,152)

2
3
4
5
6
7
8
9

fromage (25,017)
thomery (7,914)
dictionnaire (4,668)
tomery (4,041)
cb32693529c_date (2,832)*anatomy (2,772)
allcoll (1,216)
cb328131247_date (939)10jean de l’ours (864)1914 (Freq.)
appartient à l’ensemble documentaire : abcdaire1 (94,529)*National Library of France (94,016)*Gallipoli Campaign (1915) (55,210)*aviator (24,048)
portrait (10,289)
cb34378481r_date (8,369)*fatherhood (4,749)*recognition (4,749)
officiel de la République française
(3,262)
1914–1918 (3,066)

In 1914, for example, we can see two WW1 related entities:
Dardanelles and 1914–1918. The Dardanelles is a location in northwestern Turkey where the Britain–France military attacked the
Ottoman Empire on 1915 in the event known as the Battle of Gallipoli. The second one (1914–1918) is related to a book whose author
analyzed the causes, course and impact of the WW1.
Furthermore, "cb32693529c" and "cb328131247" are periodicals
covering post 1750 periods, while "cb34378481r" is the ID of Journal
officiel de la République française. "allcoll" is an internal keyword("all collections") "fatherhood recognition" is probably a query performed by users in Journal officiel de la République française to findacknowledgement of paternity. Finally, "ABCdaire1" is a keyword
which gives access to a thematic collection (childhood illustrated
books).
400,000 400,000
350,000

200,000

300,000
250,000End

300,000

1900

1920
1914

1918

100,000
0
1600
1910 1915 1920 1925 1930 1935 1940

200,0001700 1800

150,000
1550
1400 1500

100,000
50,000
0
1000

1200

1400

1600

1800

2000

Figure 12: Distribution of end years in filters using
gallicapublication_data.

4.4.2 End Dates in Filtered Time Spans. Next, Fig. 12 plots a distribution of end years of the gallicapublication_data. Similar to

Table 6: Top-10 words used with peaks of 1918 and 1920.
We translated some of French words into English whenthe meaning was not immediately clear. "*" mark indicates words translated into English. These are: *aviator(aviateur), *National Library of France (Bibliothèque nationale de France), *Chinese Brest (chinois brest), *Chinese(chinois), *news bulletin (bulletin presse), *Gallipoli Campaign (1915) (Dardanelles, expédition des 1915), *Chinese
Dunkerque (Chinois Dunkerque), *car (voiture).
Rank
1
2
3
4
5
6
7
8
9
10

1918 (Freq.)*aviator (24,048)*National Library of France(24,044)*Chinese Brest (21,057)*Chinese (11,862)
cb32732912f_date (9,149)
quotidien presse étrangère(8,879)*news bulletin (7,121)
cb34355551z_date (4,917)
cb34378481r_date (4,318)
marquis de givenchy (3,366)

1920 (Freq.)*National Library of France(50,014)*Gallipoli Campaign (1915)(49,876)
portrait (15,168)
boutiques parisiennes (14,649)*Chinese (13,569)*Chinese Saint Yorre (11,682)*Chinese Dunkerque (9,459)
navigation (8,556)
cb34519208g_date (8,097)*car (6,480)

the previous distribution over start years, we see that the numberof the set end years is usually increasing towards the present, especially they are common from the 19th century to the 20th century.
In addition, several significant peaks are visible on years initiating
new centuries within the period spanning from 1400 to 1900. Similar to Fig. 11 we observe that non-round dates in the 20th century(but not so much in the other centuries) are commonly used as
end filtering constraints. Looking at the zoomed out plot in the
inner graph, we can also notice 3 peaks on eventful years 1914, 1918
and 1920. Finally, somewhat interestingly, the dates within the 21st
century (e.g., 2000, 2016 or 2017) are actually not so commonly used.
Instead, 1900 is the most popular end date. This is understandable
if we consider that Gallica mainly holds public domain material.
Tab. 6 shows the top-10 words used with the two peak years
1918 and 1920. For example, as 1918 is the end year of WWI, we
can notice few war related words (aviateur that means pilot, china
and press) are ranked in the top-10 words on this year.
4.4.3 Lengths of Filtered Time Spans. We then analyze how longtime periods users tend to use for result filtering. Fig. 13 plots
the distribution of the lengths from the start to end years of thegallicapublication_data. The numbers of set periods tend to
decrease along with their duration, meaning that relatively shorttime spans are more commonly used than long durations. Indeed,
many users set filters spanning a single year. A single year is actually
an interval being associated with the highest number of queries(the interval of 100 years being the 2nd most common). Similar to
the above two results presented in Figs. 11 and 12, there are peaks
on ’round lengths’ such as 100, 200, 300, and to lesser extent on
500 and 1,000. Interestingly, we can see that four peaks occur on
1788, 1800, 1829 and 1850. The reason of the occurrence of suchlong-spans is because users sometimes do not input start or end
years; they just input only one of them as the filtering date.
As we looked at start and end years in the above analyses, we now
check which start and end dates in particular are often used together.

diff

600,000

600,000
400,000

500,000

200,0000

400,000

0

50

100

150

200

250

100,000
1829

300,000

80,0001788
60,000200,000

40,00020,000100,000

0
1700

1750

1800

1850

1900

1000

1250

1500

0
0

250

500

750

1750

2000

4.4.4 Time Filter Combination Analysis. Next, we analyze the combined time signals. Fig. 16 plots three lines corresponding to dc.creator,
dc.contributor and gallicapublication_data. To plot a line ofgallicapublication_data, we collected years used with the filtering conditions. On the other hand, to plot the other two lines, weused years input in two fields: dc.creator and dc.contributor.
After collecting all the temporal inputs, we converted them to probability distributions over their corresponding timespans using year
level granularity. For a given time input (e.g., 1960s) with tb denoting its start year (1960) and te indicating its end year (1969) we
set the probability distribution with zero values for t < tb and fort > te (e.g., before 1960 and after 1969) and with non-zero values
for tb ≤ t ≤ te that sum to 1 (e.g., 1/10 for each year from 1960 to1969). We then combined for every year all the computed probability distributions based on all the time signals. The formal definitionof the probability distribution for a year y is given in Eq. 1.

30,00020,00010,0000

Figure 14: Correlation between decades representing thestart and end years of gallicapublication_data.
50,000

2010s
2000s
1990s
1970s
1960s
1950s
1940s
1930s
1920s
1910s
1900s
1890s
1880s
1870s
1860s
1850s
1840s
1830s
1820s
1810s
1800s
1790s
1780s
1700s
1690s
1630s
1600s
1550s
1500s
1400s

40,00030,00020,00010,000

0
s
40
1930s
1920s
1910s
1900s
1990s
1880s
1870s
1860s
1850s
1840s
1830s
1820s
1810s
1800s
1890s
1780s
1760s
1750s
1700s
1750s
1600s
1600s
1550s
1400s
1400s
1300s
1200s
11 00s100s

For this we map all years from gallicapublication_data into
decade-level granularity, e.g., 1988 is mapped into 1980s. Fig. 14
shows the numbers of co-used start and end years mapped into
decade-level granularity. In this figure, x and y axes indicate thestart and end decades, respectively. In addition, Fig. 15 presents
the selection of the most commonly used decades in which only
the top-20 most common decades are shown either as a starting or
ending decade.
We can observe three straight lines: a) from 1780s/1790s to 1930s/
1940s, b) first column of 0s, and c) the most upper row of 2010s.
The first line indicates that users set filter durations to be relatively
short; i.e., users tend to set years within 30 years from 1820s to
1840s, 40 ∼ 60 years from 1850s to 1920s. After reexamining Figs.
7 and 8, we believe that users may search for works of writers,
artists, scientists (and other similar occupations) who were alivefrom 1820s to 1920s. The remainder two lines mean that searchers
sometimes set the start date as years on decade 0s or the end dateas years falling into 2010s.

40,000

2010
2000
1990
1980
1970
1960
1950
1940
1930
1920
1910
1900
1890
1880
1870
1860
1850
1840
1830
1820
1810
1800
1790
1780
1770
1760
1750
1740
1730
1720
1710
1700
1690
1680
1670
1660
1650
1640
1630
1610
1620
1600
1590
1580
1570
1560
1550
1540
1530
1520
1510
1500
1490
1480
1470
1460
1450
1440
1430
1420
1400
1350
1320
1300
1290
1280
1250
1240
1200
1150
1110
1100
1050
1000
900
870
800
500
400
300
250
200
180
140
100
40
10
0

Figure 13: Distribution of lengths of chosen time scopes
based on gallicapublication_data. The x axis represents
the lengths in years whereas the y axis represents the numbers of queries associated with a given time duration.

50,000

2010
2000
1990
1980
1970
1960
1950
1940
1930
1920
1910
1900
1890
1880
1870
1860
1850
1840
1830
1820
1810
1800
1790
1780
1770
1760
1750
1740
1730
1720
1710
1700
1690
1680
1670
1660
1650
1640
1630
1620
1610
1600
1590
1580
1570
1560
1550
1540
1530
1520
1510
1500
1490
1480
1460
1450
1440
1430
1420
1400
1370
1350
1330
1320
1300
1280
1250
1200
1100
1000
900
870
800
700
500
450
200
140
10
0

Figure 15: Top-20 decades representing the start and end
years of gallicapublication_data.

S(y)

=

Õ
[tb ,t e ]∈Tδ (y, [tb , te ]) ∗1
t e − tb + 1(1)

where T includes all the time inputs, and the function δ returns 1
if the first argument is included in the second argument; otherwise,
it returns 0.
Looking at the lines shown in Fig. 16, we can say that present
data is more popular than past data as all the lines are increasing
towards the present time. Indeed, Figs. 7 and 8 show that mostcommon persons are related to the 19th century.
4.4.5 Query vs. Time Span Analysis. Finally, Fig. 17 shows the similarities between query terms associated with different centuries.
We calculated the similarities by Jaccard Coefficient computed byNumber of filtered years

the queries associated with the present tend to be more diverse.
This could be due to the larger size and more diversity of content
from more recent history. Finally, we list top-20 common queries fordifferent centuries in Tab. 7 which were collected from dc.creator,
dc.contributor, gallica and free text input. To save space, Tab.
7 shows the data for only the top-4 centuries.

gallicapublication
creatorcontributor

50,00040,00030,00020,0005
10,000
0
1500

1600

1700 1800
Year

1900

2000

Figure 16: Distribution of time references in filters withgallicapublication_data and ones in texts of dc.creatorand dc.contributor fields.
1.0

21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1

0.8

0.6

0.4

0.2

1 2 3 4 5 6 7 8 9 101112131415161718192021

Figure 17: Jaccard similarity between centuries basedon their associated queries. The x and y axes represent centuries associated with queries that havegallicapublication_data filtering.

first measuring how many common words queries related to any
two centuries are shared and then by normalizing the obtained
number by the sizes of the two word sets. The more the two centuries have common words, the more similar they are. To plot thefigure, we first collected queries that have start and end years as thegallicapublication_data filtering. We then determined which
centuries are associated with each query. In particular, we counted
how many years of each century are included within the intervaldelimited by the start and the end years. The centuries having thelargest overlap were then assigned to the query. The algorithm isbased on the following equation:
Century(d)=freq({map(y)|y ∈ d})(2)

where freq returns elements that are the most frequent elements inthe list of given arguments and map is a function taking a year andreturning its century. For example, to a query with the start dateof 1690 and the end date of 1950, the 18th and 19th centuries are
assigned.
From Fig. 17, we can see that century-to-century similarities tend
to decrease towards the present. This result indicates that users
may tend to retrieve similar data from very distant past whereas

LIMITATIONS

Input Words vs. Clicks In this work, we have not investigated
how the queries were input. Some of the queries may originate
as a result of users clicking on recommended items displayed on
the front page of the Gallica portal (this could explain why somequeries seem bit ’artificial’, e.g., being too long or having numbers
mixed with characters). "ABCdaire1" (cf. Tab. 5) query is such an
example of prewired queries in Gallica. Furthermore, Gallica provides various APIs which could be the reasons for some of suchqueries. We left their determination and analysis as a future work.
Data Collection Time Span. We note that the data collection
we used is a portion (15 months) of all logs published in Gallica.
Subsequent work should investigate portions of data collected overdifferent time frames in order to verify which of the results are specific to the particular time frames of data generation and which areof general character. Once we can obtain data generated in different
durations, we plan to perform such comparative investigation.
Different Languages. As Gallica is an online portal, anyone
can use it. In this paper, we implicitly made an assumption that all
queries are in French and hence we have applied tools dedicated toFrench language. While the large majority of queries are indeed in
French, naturally, there are ones in other languages, too. Further
exploration should then involve different languages (e.g., English,
Italian) that Gallica supports as well as the cross-comparison of theobtained results. Also, geographical analysis is another interesting
exploration.

6

CONCLUSIONS & FUTURE WORK

Cultural heritage collections are commonly used for storing, preserving as well as finding and learning information related to thepast. This wealth of data is then source of interaction and searchfrom many ordinary users.
In this paper we have studied how users search content in cultural heritage collections based on data collected from Gallica portalof the French National Library. The data we used is of large scale
spanning 15 months. We mainly concentrated on entity analysis
looking into the types of entities in queries and on the way inwhich temporal filters are associated with search queries. We havealso analyzed which metadata filters and their combinations arecommonly used.
Our analysis provides observations that can complement existing
investigations of CH collection search thanks to the novel focus
on semantic (entity) and temporal angles. It can also lead towards
search experience improvement. For example, the examination of
searched entities suggests which entity types tend to be popular,
and so, the results related to these kinds of entities could be thenrecommended to visitors. We have also observed several temporal
landmarks in temporal filtering (e.g., ones every 100, 50 or 10 years
depending on a century). This might indicate that users first viewa big picture concerning the results of their queries and likely doTable 7: Top-20 queries of dc.creator, dc.contributor and free text. We translated some of French words into English when themeaning was not immediately clear. "*" mark indicates words translated into English. These are: *tale (conte), *Peru (Pérou),*impiety (impiété), *America (Amérique), *Algeria (Algérie), *housewife (ménagère), *laundres (blanchisseuse), *wash house(lavoir), *sex education (éducation sexuelle), *Comoros (Comores), *peyoye (peyotl).
Rank
1
2
3
4
5
6
7
8
9
10

17th
*tale (471,900)
boussac (314,700)*America (270,300)
petite fleur bleue (260,700)
alpilles (251,100)
tristan l’hermite (245,210)
Luther (194,760)*Peru (182,100)*impiety (170,415)
buffon, georges-louis leclerc (160,202)

18th*impiety (649,200)*tale (471,900)
guinguette (368,640)
boussac (314,700)
tunnel (272,400)*America (270,300)
petite fleur bleue (260,700)
alpilles (251,100)
buffon, georges-louis leclerc (202,504)
thomery (199,359)

not have sufficient knowledge on how to setup the correct dates.
It might also be a user tactic to avoid returning of too large result
list. 9 Hence, for clear and concrete queries, it might be helpful toautomatically support setting temporal filtering. For example, givena name of a searched entity such as an artist, the search engine
could recommend filtering years when he/she mainly worked orwhen remained active.
Future work will investigate in more detail user behavior: how
many times and what words the same user inputs, how the users
change parameters (e.g., temporal filtering) during search sessions,what are characteristics of failed/successful search sessions and soon. Due to the time and space constraints, we have skipped the user
(session)-focused analysis in this work; however, they are interesting directions and should be explored to better understand user
search patters for potential improvement of user experience, creating recommendation/support systems, and for other objectives.
Finally, we wish to undertake the study of how external eventsaffect the choice of search keywords in CH search as well as we will
investigate in detail how user queries correlate with the content
provided in the collections and with the users’ knowledge of thiscontent.
Acknowledgments. This work has been supported by the European
Union’s Horizon 2020 research and innovation programme under grant770299 (NewsEye), in part by MEXT Grant-in-Aid (17K12792, 17H01828,
18H03243) and by MIC SCOPE (171507010).

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