Document <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/css" href="https://cwrc.ca/islandora/object/cwrc%3A5d215a83-f40d-4df4-8081-362f64951117/datastream/CSS/view"?> <TEI xmlns="http://www.tei-c.org/ns/1.0" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"> <teiHeader> <fileDesc> <titleStmt> <title>Onthology Shift: Cultural Heritage Ontologies in the Time of Linked Open Data </title> <author>M. Cristina Pattuelli</author> <author>Matthew Miller</author> <author>Sarah Ann Adams</author> </titleStmt> <editionStmt> <edition><date>2019-06-28</date></edition> </editionStmt> <publicationStmt> <distributor>Canadian Writing Research Collaboratory</distributor> <address> <addrLine>University of Guelph</addrLine> </address> <availability> <p>Available under a CC-BY-NC license</p> </availability> <date when="2019">2019</date> </publicationStmt> <sourceDesc> <bibl> Title of work, Ontologies for Linked Data in the Humanities, 2019) prepared for the Ontologies for Linked Dara in the Humanities Workshop at the DH2019 conference in Utrecht, sponsored by the <orgName>Alliance for the Digital Humanities Linked Open Data Special Interest Group</orgName>, and organized by <persName>Kim Martin</persName> and <persName>Susan Brown</persName> with committee members <persName>Johanna Drucker</persName>, <persName>Ryan Shaw</persName>, <persName>Joseph T. Tennis</persName>, and <persName>Jasmine Drudge-Willson</persName>….</bibl> </sourceDesc> </fileDesc> <xenoData><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:as="http://www.w3.org/ns/activitystreams#" xmlns:cwrc="http://sparql.cwrc.ca/ontologies/cwrc#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:geo="http://www.geonames.org/ontology#" xmlns:oa="http://www.w3.org/ns/oa#" xmlns:schema="http://schema.org/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#"> <rdf:Description rdf:datatype="http://www.w3.org/TR/json-ld/"><![CDATA[ { "@context": { "dcterms:created": { "@type": "xsd:dateTime", "@id": "dcterms:created" }, "dcterms:issued": { "@type": "xsd:dateTime", "@id": "dcterms:issued" }, "oa:motivatedBy": { "@type": "oa:Motivation" }, "@language": "en", "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#", "rdfs": 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]]></rdf:Description></rdf:RDF></xenoData><profileDesc> <abstract> <p> The linked data paradigm has significantly altered the notion of knowledge representation as well as the role of ontologies as applied in Artificial Intelligence (AI) and in the early stages of the Semantic Web. Recent initiatives such as Wikidata and its underlying software Wikibase have furthered this shift, forcing a reexamination of our approach to how semantic technologies are developed and used. This work provides a brief overview of this transition in the context of cultural heritage and based on the experience of the Pratt Semantic Lab team.</p> </abstract> </profileDesc> </teiHeader> <text> <body> <div type="chapter"> <head><bibl><title>ONTOLOGY SHIFT: CULTURAL HERITAGE ONTOLOGIES IN THE TIME OF LINKED OPEN DATA </title> <lb/><author><persName key="Pattuelli, M. Cristina" ref="http://viaf.org/viaf/316786456/">M. Cristina Pattuelli</persName><note><orgName key="Paris Institute of Arts and Sciences" ref="https://commons.cwrc.ca//cwrc:b4f7853f-c3b4-411c-a572-cad1484f1916">Pratt Institute School of Information</orgName></note></author><lb/> <author><persName key="Miller, Matthew" ref="https://commons.cwrc.ca//cwrc:f26a6102-cd41-41c4-ac32-11c222fff0c2">Matthew Miller</persName> <note>Pratt Institute School of Information</note></author><lb/> <author><persName key="Custom Entry" ref="https://www.linkedin.com/in/sarahannadams">Sarah Ann Adams</persName><note>Pratt Institute School of Information</note></author></bibl></head> <div type="sub-section"> <div type="sub-section"> <head>ABSTRACT</head> <p>The linked data paradigm has significantly altered the notion of knowledge representation as well as the role of ontologies as applied in Artificial Intelligence (AI) and in the early stages of the Semantic Web. Recent initiatives such as <orgName key="Wikidata" ref="http://www.wikidata.org/entity/Q2013">Wikidata</orgName> and its underlying software <orgName ref="http://www.wikidata.org/entity/Q16354758" key="Wikibase" cert="high">Wikibase</orgName> have furthered this shift, forcing a reexamination of our approach to how semantic technologies are developed and used. This work provides a brief overview of this transition in the context of cultural heritage and based on the experience of the <orgName ref="https://semlab.io/" key="Pratt Semantic Lab" cert="high">Pratt Semantic Lab</orgName> team. </p> </div> </div> <div type="sub-section"> <head>BACKGROUND</head> <p>Ontologies are the core of any knowledge representation system and a research focus in the field of AI. They are also a key component of the Semantic Web vision and the cornerstone of the Linked Open Data initiative—the most recent manifestation of the Semantic Web. In this new data environment, ontologies have morphed into lightweight knowledge tools with minimal or no formalization. Even changes in the lexicon, where ontologies and vocabularies have become interchangeable terms, reflect the more relaxed notion of the semantics they now carry. </p> <p>The need to represent and interconnect large amounts of heterogeneous data in an open and distributed information space like the web makes formal ontological constraints a potential barrier to interoperability and broad adoption. This is especially true when it comes to the cultural heritage sector, a key player in linked data development. Cultural heritage is a complex and inherently interconnected field, populated by infinite entities and properties with various degrees of granularity. Its domain is typically less structured and more ambiguous when compared to domains such as medicine or the hard sciences. With their multifaceted and often abstract nature, arts and humanities tend to defy high formalization, making it difficult to ground their domain specifications into formal ontological commitments. </p> <p>All of this makes the cultural heritage domain well suited for linked data ontologies whose semantics is relatively light and relies on rather basic constructs. For example, properties are not constrained logically by axioms and rules and their restrictions are, for the most part, semi-formal, usually consisting of a range, domain, and data type. Most properties are defined even more informally through natural language descriptions. </p> <p>Agreement is typically achieved through the use of shared ontologies. While <orgName key="Resource Description Framework" ref="http://www.wikidata.org/entity/Q54872">RDF</orgName>, the data model for linked data, is effective in addressing syntactic heterogeneity, the need to reconcile semantic differences is handled through the mechanism of linking classes and </p> <p>instances via properties expressing equivalence or similarity. This provides a dynamic approach to information sharing as no pre-coordination is needed to stipulate meaning. This is a way for multiple and heterogeneous data sources to interoperate and for multiple points of view to coexist in a shared representation space. </p> <p>A pragmatic approach drives the development and use of ontologies in the linked data environment, a slight departure from the original spirit of the Semantic Web (Pattuelli, <persName key="Provo" ref="http://library.nyu.edu/people/alexandra-provo/">Provo</persName> and <persName key="Custom Entry" ref="https://www.linkedin.com/in/hilary-thorsen-50ba3a16">Thorsen</persName>, <date when="2015" cert="high"><date when="2015" cert="high">2015</date></date>). This is evident from even a coarse analysis of how rarely the knowledge representation languages that lie at the heart of the Semantic Web (e.g., OWL <note place="bottom"><ref target="https://www.w3.org/2001/sw/wiki/OWL#specs">https://www.w3.org/2001/sw/wiki/OWL#specs</ref></note> and its variants) are employed in today’s linked data ecosystem. <persName key="Vetere" ref="https://www.researchgate.net/profile/Guido_Vetere">Vetere</persName> (<date when="2019" cert="high">2019</date>) offers some insights on the reasons why highly formalized conceptual models have not taken off in distributed information systems, where sharing conceptual models is key. </p> <p>Yet, this lighter approach has its limitations. For example, one drawback of using representation languages that are not highly formalized is the limited capability to detect contradictions that can raise issues of consistency. There is a tradeoff between expressivity—including the ability to support inferencing and thus derive new knowledge—and computational tractability. Simplification is favored for the sake of interoperability and ease of use. </p> <p>As a common linked data practice, a good deal of data modeling is also performed through an application profile approach that consists of reusing terms from existing RDF ontologies. A key tenet of linked data is semantic shareability, and linked open data principles enable great flexibility in mixing and reusing classes and properties from suitable sources without the need of any centralized agreement, as mentioned earlier. One of the criteria for selecting candidate terms, especially predicates, is the frequency of use as a way to foster consistency in data modeling. RDF ontologies are easy to extend. As a result, these models can expand as representation needs evolve with little impact on systems. Flexibility has been recognized as a key factor in the exponential growth of the linked data cloud (<persName ref="http://richard.cyganiak.de/foaf.rdf#cygri" key="Cyganiak" cert="high" type="real">Cyganiak</persName> & <persName ref="https://www.wikidata.org/wiki/Q30766768" key="Jentzsch" cert="high" type="real">Jentzsch</persName>, <date when="2010" cert="high">2010</date>). While the relative simplicity of RDF ontologies eases data integration and interlinking, a number of challenges have yet to be addressed. For instance, as <persName key="Dijkshoorn, Chris" ref="http://viaf.org/viaf/92155707114422412928/">Dijkshoorn</persName>, <persName key="Aroyo" ref="https://www.semanticscholar.org/author/Lora-Aroyo/1745337">Aroyo</persName>, <persName key="Ossenbruggen, J.R. van (Jacco Ronald)" ref="http://viaf.org/viaf/286626876/">Ossenbruggen</persName>, and <persName key="Custom Entry" ref="https://www.semanticscholar.org/author/Guus-Schreiber/40248369">Schreiber</persName> (2018) point out, alignment issues exist among different conceptualizations of cultural heritage domains. </p> </div> <div type="sub-section"> <head>CULTURAL HERITAGE ONTOLOGIES</head> <p>A number of RDF-based ontologies, both general and domain-specific, have been developed for the cultural heritage sector. <orgName ref="http://www.wikidata.org/entity/Q624005" key="CIDOC Conceptual Reference Model" cert="high">CIDOC Conceptual Reference Model (CIDOC- CRM)</orgName> <note place="bottom"><ref target="http://www.cidoc-crm.org/">http://www.cidoc-crm.org/</ref></note> and the <orgName ref="https://pro.europeana.eu/page/edm-documentation" key="Europeana Data Model" cert="high">Europeana Data Model</orgName> (EDM) <note place="bottom"><ref target="https://pro.europeana.eu/resources/standardization-tools/edm-documentation">https://pro.europeana.eu/resources/standardization-tools/edm-documentation</ref></note> are among the most mature efforts, providing a general framework for representing, publishing and aggregating cultural data. CIDOC-CRM is a semantic reference tool designed to support and facilitate the interoperability of cultural heritage data widely used in museums. EDM is an upper-level model originally developed to describe and structure cultural heritage data to support data aggregation by Europeana. With domain-specific extensions and in combination with other models, including <orgName key="Dublin Core" ref="http://www.wikidata.org/entity/Q624610">Dublin Core</orgName> and CIDOC-CRM, EDM has been used by several individual institutions (e.g., the <placeName key="Amsterdam Museum" ref="http://dbpedia.org/resource/Amsterdam_Museum">Amsterdam Museum</placeName>). </p> <p>It is interesting to note that these reference and top-level ontologies have shifted the barycenter from the traditional object-centric approach to an event-centric approach. While the bibliographic (or documentalist) paradigm has focused on objects and on the cluster of static properties describing them (e.g., creator, object type, title, date of creation, format, etc.), centering on events allows a more dynamic representation of culture, making it possible to better express dimensions such as temporality, provenance and various contexts of cultural production. Advantages and disadvantages of current ontologies in cultural heritage domains such as museums and libraries have been addressed in the literature (<persName key="Custom Entry" ref="https://orcid.org/0000-0001-7508-7428">Alexiev</persName>, 2018; <persName key="Zeng" ref="https://orcid.org/0000-0003-0151-5156">Zeng</persName> and <persName key="Mayr" ref="https://orcid.org/0000-0002-6656-1658">Mayr</persName>, 2018). </p> </div> <div type="sub-section"> <head>WIKIDATA AND WIKIBASE </head> <p>New community-driven and rather intuitive knowledge representation tools are also making strides in the linked open data community. First and foremost is Wikidata<note place="bottom"><ref target="https://www.wikidata.org/wiki/Wikidata:Main_Page">https://www.wikidata.org/wiki/Wikidata:Main_Page</ref></note>, the free and collaborative knowledgebase created by the <orgName ref="http://www.wikidata.org/entity/Q180" key="Wikimedia Foundation" cert="high">Wikimedia</orgName> community, which has rapidly become a major data hub for linked data development. The most recent Wikimedia foundation project that has gained traction is Wikibase,<note place="bottom"><ref target="http://wikiba.se/">http://wikiba.se/</ref></note> the free software that powers Wikidata. Wikibase holds great promise to provide an open and collaborative technical infrastructure for a growing community of users in the cultural heritage sectors (a selection of Wikibase projects is available at http://wikiba.se/projects/). As our Semantic Lab <note place="bottom"><ref target="https://semlab.io/">https://semlab.io/</ref></note> team begins to rethink its research infrastructure—specifically where to host different project streams and manage old and new linked datasets—we have started experimenting with Wikibase to explore the modeling capabilities and utilities it offers (Miller, 2018). </p> <p>The data model Wikibase employs mirrors the Wikidata data structure. Wikidata is essentially a collection of entity pages that include statements. Each statement is made up of a claim in the form of a property-value pair and optional qualifiers and references. The building blocks of a statement are items and <hi rend="italic">properties</hi>, comparable to what RDF defines as entities (subject or <hi rend="italic">object</hi>) and predicates. A richer model than RDF, Wikidata provides a way to further refine claims through qualifiers. These qualifiers specify the context in which a claim is valid (e.g. <placeName key="New York City New York United States" ref="http://geonames.org/5128581">New York City</placeName> (item Q60) has population (property P1082) of 8,175,133 at point in time (qualifier, property P585) 2010). In addition, a claim can be annotated with references, anything from websites to datasets, providing a verifiable source for a claim (e.g., stated in (P248) 2010 <placeName key="United States United States" ref="http://geonames.org/6252001">United States</placeName> Census is inclusion hT. (Q523716) for value particular is of provenance information as a source of evidence to assertions of In the context of our data, for example, where social networks research. cultural heritage are built based on mentions in digitized text, having each passage directly tied to the would make it possible to conduct deeper analysis and data statements RDF corresponding Wikidata excessively cumbersome RDF graphs. ation without having to deal with manipul does not rely on a mature ontology yet. Core modeling features such as class (Q16889133), entity (Q35120), Wikidata metaclass (Q19361238), instance of (P31), and subclass of (P279) <note place="bottom"><ref target="https://www.wikidata.org/wiki/Wikidata:WikiProject_Ontology/Modelling">https://www.wikidata.org/wiki/Wikidata:WikiProject_Ontology/Modelling#cite_note-7</ref></note> have been defined, while predicates are still in flux and added as needed through a consensus-based editorial process.<note place="bottom"><ref target="https://www.wikidata.org/wiki/Wikidata:Property_proposal">https://www.wikidata.org/wiki/Wikidata:Property_proposal</ref></note></p> <p>From a technical perspective, Wikibase provides a unified infrastructure where data, along with the tools and applications that generate, curate, store and consume it, sit together in one system. Data from different areas—from content to administrative data, including revision tracking and history—are integrated to give context and to convey multiple perspectives to graphs and datasets. We use an instance of Wikibase which makes it possible to work in a live system while retaining control of the data. This provides unprecedented flexibility during the modeling process. Through an intuitive editor, statements are created collaboratively by assigning identifiers to items and properties. Legacy data can be batch loaded. Mapping to corresponding Wikidata entities and properties is performed whenever possible to facilitate the process of later transferring the data to Wikidata. In this phase, however, there is no urgency to make binding decisions on the types of predicates to employ. These decisions can be deferred to the time when the data will be shared publicly, exported to Wikidata or exposed as RDF graphs, providing a way to tailor the semantics to different information contexts. Because Wikidata properties are generally mapped to equivalent RDF predicates via equivalent property (P1628), we intend to capitalize on this ‘parallel’ modeling feature to add multiple views, whenever suitable and consistent, to the representation of our domain of interest. This will enable us to exploit this semantic enrichment at the query time. Our work with Wikibase is still in its initial phase, but we can already see the potential. As we continue to experiment with this new linked data infrastructure, we hope to be able to share use cases and lessons learned and contribute to a new wave of linked open data development. </p> </div> <div type="sub-section"> <head>REFERENCES</head> <bibl> <author>Alexiev, V.</author> (<date>2018</date>). <title level="a">Museum Linked Open Data: Ontologies, Datasets, Projects.</title> <title level="j">Digital Presentation and Preservation of Cultural and Scientific Heritage</title>, <biblScope>8, 19-50. </biblScope></bibl> <bibl><author>Dijkshoorn, C.</author>, <author>Aroyo, L.</author>, <author>Van Ossenbruggen, J.</author>, & <author>Schreiber, G.</author> (<date>2018</date>). <title level="j" ref="http://dx.doi.org/10.3233/AO-180201" key="Modeling cultural heritage data for online publication." cert="high">Modeling cultural heritage data for online publication.</title> <title level="j">Applied Ontology</title>, <biblScope>13(4), 255-271. </biblScope></bibl> <bibl><author>Miller, M.</author> (<date>Mar 9, 2018</date>). <title level="a">Wikibase for Research Infrastructure — Part 1</title> [Blog post]. 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