Metadata-Driven Approach for Keeping Interpretability of
Digital Objects through Formal Provenance Description
Chunqiu Li
Graduate School of Library, Information and Media Studies,
University of TsukubaJapanlichunqiuaa@126.com

ABSTRACT
Metadata about digital objects help users find, understand, use
and reuse those objects. Longevity of digital objects is a vital
issue for digital preservation, which means that the metadata
about digital objects must be maintained as well, so that theircontent and meaning should be maintained over time. Open
Archival Information System (OAIS) defines three metadata
components, which have to be maintained with Digital Object– Representation Information of Digital Object, Preservation
Description Information (PDI) in an Information Package, andthe Content Information given to every Information Package.
Provenance of a digital object, which is one of the fivecategories of PDI, is a crucial record of the history of the object
over its lifecycle. Since metadata are exchanged as digital
objects on the Web, machine-readable and interoperable
provenance description of metadata is required for the longterm maintenance of metadata. This paper presents issues in
the longevity of metadata, especially the issue of metadata
provenance based on the Singapore Framework for Dublin
Core Application Profiles (DCAP), which is well known for
metadata interoperability in the networked information
environment. The paper first briefly discusses features of
metadata as first class objects on the Web. Then, we address
potential risks in affecting interpretability of digital objects
and issues in the consistent maintenance of metadata. Next,
the W3C PROV standard for general provenance description
and Resource Description Framework (RDF) for metadata
exchange on the Web are adopted as the base models forprovenance description of metadata. We developed simple
provenance description models for formal provenancedescription for both structural features and vocabularies of
metadata. The models are designed based on Entities andActivities defined by the W3C PROV in correspondence withprimitive changes of metadata application profiles andmetadata vocabularies, respectively. We also provide formal
provenance description examples corresponding to structuralchanges in a metadata application profile along with semanticchanges in the use of its metadata vocabulary. We discuss
limitations of our proposed models and review provenance

Shigeo Sugimoto
Faculty of Library, Information and Media Science,
University of TsukubaJapansugimoto@slis.tsukuba.ac.jp
related research. Finally, the main findings of this research aresummarized in the conclusions.

KEYWORDS
Metadata, digital object, metadata schema, application profile,
metadata vocabulary, provenance, long-term maintenance

1 INTRODUCTION
Long-term accessibility of digital collections requires
keeping digital objects usable over time and acrosscommunities. Metadata plays an important role in thecontinued accessibility of digital collections and is used in awide range of fields, such as computer science, library andinformation science, archival science, and so forth. Recent
developments in research data sharing (e.g., Research Data
Alliance 1 ) and cultural resources aggregation (e.g.,
Europeana2 and DPLA3) have also increased demands to keep
research data and cultural collections alive over time.
Maintaining metadata over time is important to keep researchdata and cultural contents reusable over time.
This paper discusses a metadata-centric study to keep
digital objects interpretable, focusing on issues such as longterm maintenance of metadata schemas and metadata
vocabularies. Metadata schema defines structural, syntactic
and semantic features of metadata and uses metadata
vocabulary that is a set of metadata terms to describe
metadata record. In the long run, metadata provenance thatrecords revision history of metadata schema and metadata
vocabulary should be consistently recorded, since provenanceis useful to auditing errors, justifying data authenticity, andidentifying invalidated data, etc. In this paper, we developed
simple provenance description models for metadata schemas
and metadata vocabularies, respectively. The models enable
maintainers to formally describe primitive revisions between
two consecutive versions of a metadata schema and a
metadata vocabulary in a machine-processable form, so that
1 https://www.rd-alliance.org/
2 http://www.europeana.eu/portal/en

3 Digital Public Library of America, see https://dp.la/their revision history can be consistently managed andeffectively traced over time.
This paper provides a brief overview of the main results
gained from our earlier research about metadata longevity
conducted by the authors [1,2]. The rest of this paper is
structured as follows. Section 2 analyzes features of metadataon the Web. Section 3 gives an overview of risks affecting
metadata longevity. Section 4 discusses issues of metadata
interpretability, long-term maintenance of metadata schemaand metadata vocabulary, as well as metadata provenance.
Section 5 presents our newly devised models for formal
provenance description of metadata, and Section 6 discusses
the formal provenance descriptions of metadata application
profiles and metadata vocabularies based on our models using
examples for illustration. Section 7 presents limitations of ourmodels and briefly reviews related research on provenancedescription. Section 8 summarizes main findings of this study.

2

FEATURES OF METADATA ON THE WEB

Metadata (Greek: meta- + Latin: data “information”) [3] asstructured data is generally defined as “data about data”. In
library domain, a card catalog and its electronic counterpart
are common examples of metadata. Metadata can be an object
in databases or in systems. Metadata in the networked
information environment has features different fromconventional metadata that is primarily designed for use on asingle database or a set of databases. An instance of metadataon the Web is no longer an object enclosed in a database, but
is a digital object which is transferred from one site to anotherand shared among those sites. We call such metadata object asa “first class object”. This paper discusses features of metadataon the Web as follows [4,5].
Structural Features: Metadata is typically structured
according to a scheme. Structural features of metadata areassertions about data structure, mandatory levels, iteration
constraints of description, and so forth. Such assertions
represent attributes and values of resource in machinereadable form.
Syntactic Features: Metadata can be serialized in different
syntaxes, e.g., HTML, XML, RDF/XML, Turtle, JSON, JSON-LD.
Semantic Features: The elementary semantics of metadata
are specified and defined in a metadata vocabulary. The
meaning of each metadata term and relationships between
terms are identified as the semantic features of metadata.
Uniform Resource Identifier (URI) is used as the base scheme
to identify a term in the Linked Open Data (LOD) environment.
Metadata interoperability is crucial not only acrosscommunities but also over time. Metadata standards are thebasis for interoperability of metadata [6]. However, schemes
for metadata interoperability over time are still not welldeveloped. In the long term, metadata need to be kept
interpretable not only by humans but also by machines. We
need to understand the potential risks and develop strategies

to keep metadata instances consistently interpretable overtime.

3 RISKS IN INTERPRETABILITY OF DIGITALOBJECTS
Digital objects are preserved as a sequence of bits. It is ofimportance to ensure that the bits remain intact and correctover time. However, bit preservation alone is not sufficient for
the long-term preservation of digital objects. Digital objects
should be kept interpretable across the changes in many
aspects over time. As OAIS reference model defines three
metadata
components,
Representation
Information,
Preservation Description Information (PDI) and Content
Information, metadata has to be preserved with digital objects.
Those metadata may be stored in a database with thepreserved digital objects as an archival information package(AIP). This means that metadata schemas and vocabularies
used in those metadata have to be maintained over time aswell as those AIPs. In the LOD environment, metadata
schemas and vocabularies are digital objects, which should be
kept usable for long-term as well as the preserved digital
objects.
Preservation of digital objects requires preventing
damages or loss of digital objects. We need to manage risks ofdamages or loss in the preservation process of digital objects.
Risk management for keeping metadata and their schemas
safe is a crucial research issue. In the OAIS reference model,
risk management is an essential part of preservation planning[7]. However, OAIS does not discuss risk management for
metadata and their schemas exchanged on the Web. Therefore,
this paper analyzes the risks in effecting interpretability ofdigital objects from the viewpoint of metadata. The following
risks might cause inconsistency in digital preservation: (1)Metadata schema and metadata vocabulary for a digital object
may be unknown, improperly recorded, lost, changed, orobsolete, (2) Metadata schema and metadata vocabulary for adigital object may be improperly maintained and theirrevision history may not be consistently recorded, (3)Provenance information about the digital objects and their
metadata schemas as well as metadata vocabularies may not
be consistently recorded in machine-processable form, and(4) Resource identifiers of any of those instances may be
inconsistent.
The following functions have to be studied in order toreduce the risks in metadata longevity: (1) preserving the
documents of metadata schema and metadata vocabulary, (2)recording and maintaining metadata schema and metadata
vocabulary along with their revision history, (3) recording
necessary and interoperable provenance of metadata schemaand metadata vocabulary, and (4) creating sustainable
identification mechanisms and schemes for metadata
instances.

4 LONG-TERM MAINTENANCE OF METADATA
This section discusses long-term maintenance of metadata
schemas, long-term maintenance of metadata vocabularies,
and metadata provenance as key aspects of metadata
longevity.

4.1 Metadata Preservation: Keeping
Temporal Interoperability of Metadata
Metadata plays crucial roles in digital collections, e.g.,finding aids, rights management, etc. Therefore, metadatashould be preserved as well as the digital resources in thecollection.
PDI of OAIS has five categories which are Provenance,
Reference, Fixity, Context, and Access Rights Information.
Provenance documents the history of content information,and tells the origin or source of content information, and anychanges that may have taken place since it was originated,
who has had custody of it since it was originated, providing anaudit trial for the content information [8]. Provenanceprovides the credibility information about a preserved
resource to the users in the future as it contributes toevidence supporting authenticity. Provenance describeschange history of a digital object over time and can be viewed
as a special type of context information [9].
This paper focuses on the formal provenance description of
the structural features of metadata and vocabularies definedfor metadata. In this paper, we use the terms Metadata
Schema and Metadata Vocabulary. We define metadata
schema as a description of scheme that defines structuralfeatures of metadata and metadata vocabulary as a controlled
set of terms defined for metadata. For example, Simple Dublin
Core defines a metadata vocabulary composed of 15 metadata
terms and very general structural features where any definedelement is repeatable and optional. The metadata vocabularyof Simple Dublin Core is known as Dublin Core Metadata
Element Set4.
The Singapore Framework for Dublin Core Application
Profile (DCAP) defines the components of metadata schemas
for an application and related components, such as metadata
vocabulary. A DCAP has five components, which are Usage
Guidelines, Syntax Guidelines and Data Formats, Functional
Requirements, Domain Model, and Description Set Profile [10].
The framework is developed based on the Web standards (e.g.,
RDF5, RDF/S6) and is used as a basis for our research todiscuss issues with the longevity of metadata. This is because
the framework separates the structural and semantic featuresof metadata. In particular, we focus on provenance descriptionof Description Set Profile in this paper.

In the LOD environment, RDF/S and OWL 7 are basic
schemes to define metadata terms and vocabularies. Eachterm is defined as a resource identified by URI. We discuss
longevity of metadata vocabulary from the viewpoint ofprovenance description of metadata terms. Term mapping
between metadata vocabularies is often done to merge two or
more sets of metadata described on different metadata
vocabularies. Mapping itself is a crucial data resource to keepa record of the merger for future purposes. However, in thispaper, maintaining mapping over time is not discussed
although it is a crucial issue for long-term use of metadata.

4.2 Long-term Maintenance of Metadata
SchemasA metadata schema should be preserved as well as
metadata instances created from the schema. Metadata
schemas are preserved as a document for human readers in aconventional maintenance environment of metadata. The
state of the art environment of the Web provides standards
and models to formally describe metadata schemas in amachine-processable form, e.g., RDF, OWL and the Singapore
Framework for Dublin Core Application Profiles. Metadata
schemas are no longer simple document-like objects, but arecomplex objects transferrable over networks. Therefore, weneed to develop technologies to maintain metadata schemas
and vocabularies over time for the longevity of metadata.
In this paper, we propose a formal model for provenancedescription of structural constraints of metadata schema
based on the Description Set Profile of Singapore Framework.
The changes between two consecutive versions of a metadata
schema should be recorded as provenance description of themetadata schema. The advantage of formal provenancedescription of metadata schemas over conventional changelogs is automated auditing to help find errors andinconsistencies between the different versions of themetadata schema.

4.3 Long-term Maintenance of Metadata
Vocabularies
It is recommended to reuse existing and well-known
metadata vocabularies to improve semantic interoperabilityof metadata. This means that application metadata schemas
rely on standard metadata vocabularies, in particularmaintenance of the definition of metadata terms. For example,
changes in descriptions of meanings of a term may affectapplication metadata schemas that use the term. However,many metadata vocabularies have no clear policy regarding
their change documentation [11], and hence monitoring
metadata vocabularies is of importance. Maintenance agenciesof standard vocabularies should have policies for maintaining
the vocabularies for their sustainability.

4 http://www.dublincore.org/documents/dces/
5 http://www.w3.org/TR/rdf11-primer/
6 http://www.w3.org/TR/rdf-schema/

7 http://www.w3.org/TR/owl-features/

A metadata vocabulary is comprised of a set of terms andrelationships between terms. When a newly defined version ofa metadata vocabulary is published, there may be somechanges from its previous version, e.g., the meaning of a term
can be changed, relationship between terms can be revised, acomposite term can be split to single terms, or single terms
can be merged into one composite term. The changes to a
metadata vocabulary should be also consistently recorded asprovenance of the metadata vocabulary.

4.4 Metadata Provenance
Provenance (from the French provenir, “to come from”) is
the description of the history of an object. Provenance is usedin several fields, such as identifying authorship of art works,
justifying trustworthiness of data, reproducibility of scientificresearch, etc. Provenance of a digital object describes how thedigital object came to the current state since its origination.
For long-term preservation of digital objects, we need torecord provenance of the digital objects in a digital form,
which we call digital provenance in this paper. Digitalprovenance is included in well-known preservation standards,
such as the OAIS reference model and the PREMIS standard
for preservation metadata. Digital provenance typicallydescribes agents responsible for the custody and stewardshipof digital objects, key events that occur over the course of thedigital object’s life cycle, and other information associatedwith the digital object’s creation, management andpreservation [8,9,12]. The OAIS reference model is a
generalized package-based model but is not oriented to the
Web environment. PREMIS is continually maintained and its
OWL ontology as well as XML schema are being revised.
However, metadata longevity is not well explored in either ofthese standards and is still a new research topic.
Metadata provenance is a record that typically describes
responsible agents, influencing actions, associated events andother related information about metadata over its lifecycle.
Both provenance of metadata schemas and provenance of
metadata vocabularies are metadata provenance. W3C PROV
standard8 is a Web-oriented provenance standard for generalprovenance description and provenance interchange. In the
Web environment, machine-processable, interoperable andtraceable provenance is required. The following sections
present formal provenance description of metadata proposedby the authors which has its basis in W3C PROV standard andin RDF.

5 FORMAL PROVENANCE DESCRIPTION OF
METADATA
Provenance description of metadata can be in various
forms, such as natural language, RDF, etc. This study records
8 http://www.w3.org/TR/2013/NOTE-prov-overview-20130430/

provenance description of metadata in RDF, which is widelyused for metadata exchange on the Web. Formal provenancedescription in RDF holds advantages over semi-formal andinformal provenance description.

5.1 W3C PROV for Metadata Provenance
The Provenance Working Group at W3C has published the
PROV family of documents, including the PROV Data Model(PROV-DM), PROV Ontology (PROV-O), and so forth. The
working group aims at the interoperable interchange ofprovenance information in heterogeneous environments suchas the Web. PROV-DM is a conceptual data model, which
defines a set of concepts and relations to represent
provenance [13]. PROV-O defines a set of classes andproperties as an OWL2 ontology allowing mapping PROV-DM
to RDF [14].
An Entity is a physical, digital, conceptual, or other kinds ofthing. An Activity is something that occurs over a period oftime and acts upon or with Entities. Activity is used torepresent how an Entity comes into existence and how
attributes of an Entity change to become a new Entity. W3C
PROV defines relationships between Entities, relationships
between Entities and Activities, relationships between
Activities, and other relation types. To describe metadata
provenance, we classified Entities and Activities affecting
revisions to metadata application profiles and metadata
vocabularies, respectively. The following two sections will
briefly address provenance description of metadata
application profiles and metadata vocabularies.

5.2 Model for Formal Provenance Descriptionof Metadata Application Profiles
This section shows the DSP-PROV model with functions to
describe deletion, addition and revision of structural featuresof metadata schema. The classified Activities are generalized
into three change types – Addition, Deletion and Revision. The
classified Entities are Description Set Profile (DSP) and its
components, which are named as structural schema instancesin this paper.
DSP defines structural constraints of metadata. There are
two levels of templates in a DSP, i.e., Description Template(DT) and Statement Template (ST). DTs contain “statement
templates that apply to a single kind of description as well as
constraints on the described resource”. STs contain “all theconstraints on the property, value strings, vocabulary
encoding schemes, etc. that apply to a single kind of statement”[10]. Structural Constraints (SCs) contain mandatory levels,
iteration constraints and other constraints on properties andproperty values defined in statement templates.

Fig. 1 depicts the DSP-PROV model using UML Class
diagram: (1) Generalization is represented with a hollow
triangle on super-classes (i.e., Entity and Activity), (2)Aggregation is represented with a diamond on containingclasses (for example, DSP, RevsionOnDSP), and (3) Associationis represented by a line with an arrow that describes therelationship between Entity and Activity. The DSP-PROVmodel uses the properties from PROV-O when applicable.
PROV Invalidation and PROV Generation respectively
represent the deletion and addition of structural schema
instances. PROV Derivation, PROV Invalidation, PROV
Generation and PROV Usage together describe the revision of
structural schema instances. If applicable, DSP-PROV can also
describe relationships between Activities in the cases wherean Activity used an Entity generated by another Activity.
wasInformedBy

activities – Deletion, Addition and Revision acted upon its
contained Entity (e.g., a DT of the DSP). DT changes caused by
DeletionOnDT, AdditionOnDT and RevisionOnDT will result in
DSP changes caused by RevisionOnDSP. Therefore,
RevisionOnDSP has sub-activities, i.e., DeletionOnDT,
AdditionOnDT and RevisionOnDT. Similarly, we can get thefollowing two conclusions: RevisionOnDT has sub-activities,
i.e.,
DeletionOnST,
AdditionOnST
andRevisionOnST;
RevisionOnST
has
sub-activities,
i.e.,
DeletionOnSC,
AdditionOnSC and RevisionOnSC. This paper uses property
dcterms:hasPart that is recommended for modeling subactivities [15].
RevisionOnDSP
dcterms:hasPart

dcterms:hasPart

dcterms:hasPart

wasDerivedFrom

DeletionOnDTRevisionOnDTAdditionOnDT

used
EntityActivity

wasGeneratedBy and/or wasInvalidatedBy
wasGeneratedBy
RevisionOnDSP
wasInvalidatedBy
0…*
0…*
0…*

DeletionOnDTAdditionOnDTDeletionOnST

DescriptionSetProfile
(DSP)
0…*

wasInvalidatedBy
wasGeneratedBy
wasGeneratedBy

Description
Template(DT)

RevisionOnDT0…*0…*

DeletionOnST

wasInvalidatedBy
wasInvalidatedBy
wasGeneratedBy

AdditionOnST

wasGeneratedBy
0…*

0…*

Statement
Template(ST)

RevisionOnST
wasInvalidatedBy
0…*
0…*
0…*

0…*

wasInvalidatedBy

DeletionOnSC
wasGeneratedBy

AdditionOnSC
wasGeneratedBy

dcterms:hasPart

Structural
Constraint(SC)

RevisionOnSC
wasInvalidatedBy

Figure 1: Overview of the DSP-PROV model.
Fig. 2 shows the classified Activities to describe structuralchanges of metadata schema. The naming convention of the
Activities in this paper is “Activity Type + On + Abbreviation of
structural schema instance”. For instance, Revision Activity
that acted upon a DT and led it to a new DT is named as an
Activity instance of RevisionOnDT. Fig. 2 also indicates therelationships between classified Activities. The Revision
Activity acted upon the containing Entity (e.g., a DSP) has sub-dcterms:hasPart

DeletionOnSC

dcterms:hasPart

RevisionOnST
dcterms:hasPart

RevisionOnSC

dcterms:hasPart

AdditionOnST
dcterms:hasPart

AdditionOnSC

Figure 2: Activity relationships in the DSP-PROV model.

5.3 Model for Formal Provenance Descriptionof Metadata Vocabularies
Fig. 3 shows Vocab-PROV model with functions to describe
primitive changes of a metadata vocabulary and its metadata
terms. The approach for building the Vocab-PROV model issimilar to DSP-PROV model. We classified the Activities andEntities to describe the changes of metadata vocabularies.
Vocabulary, Term and Term Definition are classified as
three subtypes of PROV Entity to describe the provenance of
metadata vocabularies. As illustrated above, a Term can be aconcept or a class or a property. In the case of a concept, its
definition may include its narrower term(s), broader term(s),association/related term(s), and other information. In thecase of a class, its definition may include a description of its
meaning, a label(s), a URI, super-class(es), sub-class(es), usedproperty(ies), and other information. In the case of a property,
its definition may include a description of its meaning, alabel(s), a URI, super-property(ies), sub-property(ies), domain,
range, expected value and other information. To describe theprovenance of metadata vocabularies, Activities acting on thepreviously classified Entities are categorized into the following
types, i.e., Revision, Addition, Deletion and Replacement. The
Replacement is defined to describe the change cases such as
term split and term merge.

A revision of a vocabulary is caused by a revision of its
terms. The revision of a term may be a revision of the term as
an instance, or a revision of the documentation for the term.
For example, replacement of a single term by a set of terms isa revision of an instance, and replacement of a title text is arevision of the term definition.
wasInformedBy

ReplacementOnTerm

DeletionOnTermdcterms:hasPart

wasDerivedFrom

dcterms:hasPart

wasGeneratedBy

RevisionOn
Vocabularydcterms:hasPart

RevisionOnTermDefinition

EntitywasGeneratedBy and/or wasInvalidatedBy

RevisionOnTerm

AdditionOnTermdcterms:hasPart

used
Activity

dcterms:hasPart

Vocabulary
DeletionOn
TermDefinition

wasInvalidatedBy

dcterms:hasPart
ReplacementOn
TermDefinition

dcterms:hasPart
AdditionOn
TermDefinition

0…*
0…*

RevisionOnTerm

0…*

DeletionOn
Term

0…*

ReplacementOn
Term

0…*

AdditionOn
Term

6

wasInvalidatedBy
wasGeneratedBy

0…*

wasInvalidatedBy

Term

wasGeneratedBy

RevisionOnTermDefinition

0…*

DeletionOn
TermDefinition0…*

ReplacementOn
TermDefinition

0…*

AdditionOn
TermDefinition0…*
wasInvalidatedBy

wasGeneratedBy
wasInvalidatedBy

Figure 4: Activity relationships in the Vocab-PROV model.

TermDefinition

wasGeneratedBy

Figure 3: Overview of the Vocab-PROV model.
Fig. 4 shows the relationships between the classified
Activities to describe provenance description of metadata
vocabularies. A RevisionOnVocabulary is comprised of zero or
more than one RevisionOnTerm and RevisionOnTermDefinition.
Similarly, RevisionOnTerm has sub-activities that are
AdditionOnTerm, DeletionOnTerm and ReplacementOnTerm;
RevisionOnTermDefinition has sub-activities that are
AdditionOnTermDefinition, DeletionOnTermDefinition andReplacementOnTermDefinition.

FORMAL PROVENANCE DESCRIPTION
EXAMPLES

A metadata application profile usually uses terms definedin existing metadata vocabularies. However, the metadata
application profile may use the term meaning, which may benarrowed from the original meaning for better fit of themeaning to the application. The terms included in an existing
vocabulary are usually defined within the namespace of the
vocabulary without version information. Therefore, in thissection, we do not take into account the versions of the terms
and we focus on the changes of term meaning defined in themetadata application profiles.
Section 6.1 provides an example of semantic change andstructural change that we found from the documents of DPLA
MAPs. Section 6.2 and 6.3 discuss the provenance description
about the changes using RDF graphs based on our proposed
Vocab-PROV model and DSP-PROV model, respectively. In
Section 6.4, we briefly present the relationships between thesemantic change and structural change in the given change
examples.

6.1 Example for Semantic Change along with
Structural Change
Digital Public Library of America Metadata Application
Profile (DPLA MAP) 9 defines structural constraints ofmetadata, which include property, usage, obligation, range
and others information in tabular form. DPLA MAP uses
classes and properties from existing vocabularies, such as
EDM, ORE, DC, DCTERMS, DCMITYPE, Geo vocabulary, etc.
Three versions of DPLA MAP have been released, i.e., V3, V3.1,
V4. DPLA MAP does not provide exact meaning and definition
for its classes and properties. The value of the “Usage” column
9 https://dp.la/info/developers/map/

provides the kind of information related to meaning anddefinition of a term (i.e., class and property), which is written
as the value of “Term Meaning” in the table titled “comparison
between V3.1 and V4” of Fig. 5. DPLA MAP V3.1 provides
changes from V3 to V3.1 and DPLA MAP V4 provides changes
from V3.1 to V4 in natural language.
Fig. 5 shows change examples from DPLA MAP V3.1 to V4
that includes both structural change in DPLA MAP andsemantic change in its metadata vocabulary. Both DPLA MAP
V3.1 and DPLA MAP V4 define property edm:object for classore:Aggregation to describe “object”. The meaning of
edm:object in DPLA MAP V3.1 and V4 are “Unambiguous URL
to the DPLA content preview” and “The URL of a suitablesource object in the best resolution available on the website of
the Data Provider from which edm:preview could be generated
for use in a portal”, respectively. Fig.5, Fig.6 and Fig. 8 use ashort expression of the two definitions, i.e., “Unambiguous…”
and “The URL…”.

line). The new meaning represented in the lower dottedrectangle was derived from the meaning represented in theupper dotted-rectangle. The newly defined meaning was
generated and the previously defined meaning became
invalidated through the same Activity instance of
mv:ReplacementOnTermDefinition.
edm:object
in V3.1“Unambiguous
URL…”

skos:definition

rdf:type

rdf:type

mv:Term
Definition

edm:object
in V4

skos:definition

rdf:type
prov:wasInvalidatedBy
Replacement

prov:Entity

rdf:type
prov:wasDerivedFrom

prov:Activity

activity instance

rdf:type

prov:wasGeneratedBy
rdf:type

“The
URL…”

mv:ReplacementOn
TermDefinition

Change to

Usage
Unambiguous URL…

merge toFigure 6: Provenance description for the above semanticchange using RDF graphs.

DPLA MAP V4DPLA MAP V3.1
Obligation

Usage

Obligation

1

The URL…

0-1

merge tocomparison between V3.1 and V4

Labelproperty

Term Meaning

Minimum
Occurrence

Objectedm:object

Unambiguous URL…

1

Objectedm:object

The URL…

0

Semantic Change

Structural Change

Formal provenance description

Figure 5: Example of semantic change along withstructural change.

6.3 Formal Provenance Description for
Structural Change of Structural Constraint
As shown in Fig. 5, the minimum occurrence of edm:object
has been changed from “1” to “0”. The change was caused by arevision activity, which is an instance of the classdspprov:RevisionOnSC (note: “dspprov” is the prefix for theclasses of the DSP-PROV model [1]). Fig. 7 shows RDF graphsof the provenance description about the structural constraintchange.
ST instancein V3.1“1”

owl:minQualified
Cardinality

rdf:type

rdf:type
dspprov:
Structural
Constraint

prov:Entity

rdf:type
prov:wasDerivedFrom

rdf:type

prov:Activity
rdf:type
prov:wasInvalidatedBy
Revision
activity instanceprov:wasGeneratedBy
rdf:type

6.2 Formal Provenance Description for
Semantic Change of Metadata Term
As shown in Fig. 5, the meaning of the term edm:object has
been changed. The change was caused by a replacement
activity,
which
is
an
instance
of
the
class
mv:ReplacementOnTermDefinition (note: “mv” is the prefix forthe classes of the Vocab-PROV model [2]). According to our
proposed Vocab-PROV model, we can formally describe theprovenance description including the derivation of term
definition as RDF graphs depicted in Fig. 6.
The meaning of the term edm:object is expressed as theliteral value of property skos:definition in a rectangle (solid

ST instancein V4

owl:minQualified
Cardinality“0”dspprov:Revision
OnSC

Figure 7: Provenance description for the above structuralchange using RDF graphs.
The minimum occurrence constraint defined in theStatement Template (ST) instance that defines all thestructural constraints on the property edm:object is expressedas the literal value of property owl:minQualifiedCardinality.
The new Structural Constraint (SC) represented in the lower

dotted-rectangle was derived from the previous constraint in
the upper dotted-rectangle. The newly defined minimum
occurrence constraint was generated and the previously
defined minimum occurrence constraint became invalidated
through the same Activity instance of dspprov:RevisionOnSC.

6.4 Connection between Semantic Changewith Structural Change
In general, a metadata application profile uses terms frommetadata vocabularies. Semantic changes of the terms used ina metadata application profile may be synchronized withstructural changes of the metadata application profile. Thissection shows linkage of semantic change on a term andstructural change in a metadata application profile. Figs. 6 and7 show the provenance description about the semantic change
and structural change in the examples given in Section 6.1. As
shown in Fig. 8, the connection between Figs. 6 and 7 is theproperty constraint in the Statement Template (ST), which isexpressed as the resource value of owl:onProperty.

skos:definition

edm:object
in V3.1

owl:onProperty

mv:Term
Definition

prov:wasDerivedFrom

rdf:type

edm:object
in V4

skos:definition

ST instancein V3.1“The URL…”

owl:minQualified
Cardinality“1”

rdf:type
dspprov:
Structral
Constraint

prov:wasDerivedFrom

rdf:type
owl:onProperty

ST instancein V4

7.1 Limitation of the Proposed Models
The DSP-PROV model is designed based on Description Set
Profile of Dublin Core Application Profile. However, there are
no worldwide standards for the development of metadata
application profile as not all metadata application profiles
follow the same structure. This means the DSP-PROV model
may not be applicable to all existing metadata application
profiles.
The Vocab-PROV model generalizes primitive changes in a
metadata vocabulary. In practice, the changes to a metadata
vocabulary may be complex especially when considering
temporal information over a long period. The Vocab-PROVmodel may not be applicable to complex changes. The
maintenance of semantics of terms is a challenging issue andsemantic interoperability of terms over time is difficult toachieve.

7.2 Provenance Description Models andVocabularies“Unambiguous URL…”

rdf:type

PROV model and Vocab-PROV model. In Section 7.2 and 7.3,
we review the current state of provenance description.

“0”
owl:minQualified
Cardinality

Figure 8: Connection between semantic change andstructural change.

7 DISCUSSION
In Section 7.1, we briefly discuss the limitation andimplication of our proposed provenance models, i.e., DSP-

In our earlier research, we conducted survey of existing
provenance description models and vocabularies [16]. There
are already a wide range of models, ontologies andvocabularies that can be used for provenance description,such as Open Provenance Model (OPM), Open Provenance
Model Vocabulary (OPMV), Open Provenance Model OWL
Ontology (OPMO), Open Provenance Model (OPM) for
Workflows (OPMW), Provenance Vocabulary (PRV),
Vocabulary for Data and Dataset provenance (Voidp),
Provenance, Authoring and Versioning Ontology (PAV), W7
Model, Provenir Ontology, BBC Provenance Ontology, W3C
PROV standard and others. The CIDOC Conceptual Reference
Model (CRM) in the museum community has also been
extended to model provenance information of digital objects[17].
Provenance description is required in both conventionaland Web environment. Recording provenance in a form
interpretable by both computers and humans is required.
However, existing technologies and standards are notspecialized for metadata schema and metadata vocabulary.
Specially, models for formal provenance description of
metadata are not sufficiently explored. In the Web
environment, there is a need to develop models for formal
metadata provenance description. It is because that formal
provenance description of metadata in machine-readable andinteroperable form supports automated and effective
metadata maintenance. In this study, we have developedmodels for provenance description of metadata application
profiles and metadata vocabularies, respectively.

7.3 Provenance Description in Different
Domains

Provenance related research has been conducted in a wide
range of domains, such as museum, library and archive (MLA)
community, computer science and e-science, etc. Provenanceinformation can be used to identify authorship, ownership andauthentication of objects, e.g., the Council of European
Research Libraries (CERL) 10 website and the Getty
Provenance Index Databases11 provide search services forprovenance information. International Research into thePreservation of Authentic Records in Electronic Systems(InterPARES) 12 project addresses the importance of
provenance for keeping trustworthiness of digital records.
Provenance in e-science can be used to reproduce researchdata.
The Bodleian libraries at the University of Oxford devised adata model to represent contextual information of research
outputs in the Oxford University Research Archive (ORA) 13 ,which is a long-term data repository for scholarly research
outputs. The model incorporates PROV-DM to describe
activity related to research outputs, e.g., creation activity,
funding activity, and publication activity. Activity-based
description of relationships for a journal article using PROV-O
is given as an example [18].
Capturing the provenance information of electronic
records is a concern for archivists. Conventional provenancein the arrangement of archival records arises from theircreators, for example, individuals, cooperated bodies orfamilies [19]. The scope of provenance for archival records
encompasses to creator history, records history, and custodial
history. The archival standards such as, General International
Standard Archival Description (ISAD(G)), Encoded Archival
Description (EAD), International Standard Archival Authority
Record for Corporate Bodies, Persons and Families(ISAAR(CPF)), and Encoded Archival Context (EAC) define thedescription elements for provenance information. The
recordkeeping metadata standard ISO 23081 provides usreference to capture audit trails in the records management
process [20].
Getty Thesaurus of Geographic Names14 adopts W3C PROVto describe revision history of geographic names. W3C PROV
is used to document the Activity information about therevision of geographic names, e.g., Activity type (Create,
Modify) and temporal information associated with the Activity.
As introduced above, many communities have paid
attention to provenance description, especially the change
history and activity related to objects. However, theseprovenance description elements are designed for specificdomain requirements and not generalized for metadata
provenance. That is, they cannot be directly applied todescribe provenance of metadata application profiles and
10 https://www.cerl.org/resources/provenance/main
11 http://www.getty.edu/research/tools/provenance/search.html
12 http://www.interpares.org/
13 http://ora.ox.ac.uk/

14 http://www.getty.edu/research/tools/vocabularies/tgn/

metadata vocabularies. Therefore, the aim of this research topropose general models for provenance description of
metadata is novel.

8 CONCLUSIONSThis paper addresses issues in longevity of metadata,
especially temporal interoperability of metadata over time.
The main contributions of this paper are: (1) development of
the DSP-PROV model for provenance description of metadata
application profiles, (2) development of the Vocab-PROVmodel for provenance description of metadata vocabularies,and (3) provision of examples of formal provenancedescription, especially considering both the structural
constraint changes of metadata schemas along with thesemantic changes of metadata vocabularies. Due to the space
limitation of this paper, we will not specifically introduce theevaluation and description examples of the two proposedmodels. The details are presented in our other two papers[1,2].
The provenance description of metadata application
profiles and metadata vocabularies together can reveal therevision history of structural features and semantic features of
metadata instances, which can help users to interpret
metadata instances. These descriptions in machineprocessable form on the Web can be traced using Semantic
Web technologies.
The main findings of this article are summarized into thefollowing points: (1) Provenance information is crucialcomponent to keep longevity of digital objects, (2) Provenanceinformation should be consistently recorded in machineprocessable form on the Web, (3) The devised DSP-PROVmodel and Vocab-PROV model enable us to keep track
structural changes of metadata schemas and primitivechanges of metadata terms, respectively, and (4) Formal
provenance description holds advantages over provenancedescription in natural language. For instance, formal
provenance description helps consistent maintenance of
metadata over time; formal provenance description can beused to find errors in semi-provenance description that isrecorded in natural language. In the future, we expect to
explore practical services of provenance use cases in memory
institutions.
Furthermore, implementation of existing provenancemodels with metadata standards (e.g., PREMIS dictionary;controlled vocabularies of Library of Congress) is also anapplicable approach for provenance description of metadata.
Our previous paper used this approach and briefly discussed
provenance description of metadata schemas throughcombining the core of PROV data model with PREMIS data
model [18]. In this paper, we adopted W3C PROV and RDF topropose provenance model for metadata longevity from the
aspects of metadata application profile longevity andmetadata vocabulary longevity.

Metadata provenance is still a new topic. We definedmodels for provenance descriptions. The models rely onseveral infrastructure issues in the practical Web
environment, such as longevity of namespaces and URIs, andlong-term maintenance of widely used vocabularies. This
paper does not discuss these fundamental issues because theyneed to be discussed in the digital preservation communities
and other wider communities due to their importance. We
consider that this paper provides several fundamental issues
for discussion for the future development of digitalpreservation.

ACKNOWLEDGMENTS
We express sincere thanks to professor Atsuyuki Morishima,associate professor Tetsuo Sakaguchi, and assistant professor
Mitsuharu Nagamori for their contributions to this researchthrough discussions. We thank Mr. Tsunagu Honma and all
students at our laboratory for their kind help. We express our
special thanks to Dr. Bhuva Narayan at University ofTechnology Sydney for her help to elaborate this paper. This
work was partially supported by JSPS Kaken Grant-in-Aid for
Scientific Research (A) (Grant No.: 16H01754).

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