DOCSLIB.ORG

Provenance-Aware Knowledge Representation: a Survey of Data Models and Contextualized Knowledge Graphs

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Provenance-Aware Knowledge Representation: a Survey of Data Models and Contextualized Knowledge Graphs View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Research Online @ ECU Edith Cowan University Research Online ECU Publications Post 2013 1-1-2020 Provenance-aware knowledge representation: A survey of data models and contextualized knowledge graphs Leslie F. Sikos Edith Cowan University, [email protected] Dean Philp Follow this and additional works at: https://ro.ecu.edu.au/ecuworkspost2013 Part of the Computer Sciences Commons 10.1007/s41019-020-00118-0 Sikos, L. F., & Philp, D. (2020). Provenance-aware knowledge representation: A survey of data models and contextualized knowledge graphs. Data Science and Engineering. https://doi.org/10.1007/s41019-020-00118-0 This Journal Article is posted at Research Online. https://ro.ecu.edu.au/ecuworkspost2013/8373 Data Science and Engineering https://doi.org/10.1007/s41019-020-00118-0 Provenance-Aware Knowledge Representation: A Survey of Data Models and Contextualized Knowledge Graphs Leslie F. Sikos1 · Dean Philp2 Received: 1 April 2019 / Revised: 13 February 2020 / Accepted: 3 March 2020 © The Author(s) 2020 Abstract Expressing machine-interpretable statements in the form of subject-predicate-object triples is a well-established practice for capturing semantics of structured data. However, the standard used for representing these triples, RDF, inherently lacks the mechanism to attach provenance data, which would be crucial to make automatically generated and/or processed data author- itative. This paper is a critical review of data models, annotation frameworks, knowledge organization systems, serialization syntaxes, and algebras that enable provenance-aware RDF statements. The various approaches are assessed in terms of stan- dard compliance, formal semantics, tuple type, vocabulary term usage, blank nodes, provenance granularity, and scalability. This can be used to advance existing solutions and help implementers to select the most suitable approach (or a combination of approaches) for their applications. Moreover, the analysis of the mechanisms and their limitations highlighted in this paper can serve as the basis for novel approaches in RDF-powered applications with increasing provenance needs. Keywords RDF provenance · Contextual knowledge graph · RDF reification alternatives · RDF data model 1 Introduction to RDF Provenance path[?query] [#fragment] used to identify a resource,2 The Resource Description Framework (RDF)1 is a Seman- 2. RDF literals (L), which can be a) self-denoting plain tic Web standard for formal knowledge representation, literals LP in the form "<string>"(@<lang>)?, which can be used to efficiently manipulate and interchange where <string> is a string and <lang> is an optional machine-interpretable, structured data. Its data model is par- language tag, or b) typed literals LT of the form ticularly powerful due to its syntax and semantics; RDF "<string>"^^<datatype>, where <datatype> allows statements to be made in the form of subject-predicate- is an IRI denoting a datatype according to a schema (e.g., object triples, resulting in fixed-length dataset fields that are XML Schema), and <string> is an element of the lex- much easier to process than variable-length fields. Formally ical space corresponding to the datatype, and speaking, assume pairwise disjoint infinite sets of 3. blank nodes (B), i.e., unique anonymous resources that do not belong to either of the above sets. 1. Internationalized Resource Identifiers (IRIs, I), i.e., sets of strings of Unicode characters of the form scheme: A triple of the form (s, p, o) ∈ (I ∪ B) × I × (I ∪ L ∪ B) [//[user:password@]host[:port]] [/] is called an RDF triple, also known as an RDF statement, where s is the subject, p is the predicate, and o is the object. 1 https://www.w3.org/RDF/ The RDF data model is the canonicalization of a directed B Leslie F. Sikos graph, offering compatibility with graph algorithms, such [email protected] as graph traversal algorithms [1]. In addition, the RDF data Dean Philp model inherently supports basic inferences. Being modular, [email protected] it allows fully parallelized data processing and can represent partial information. RDF is one of the primary graph-based 1 Edith Cowan University, 270 Joondalup Drive, Joondalup, data models that are well-utilized in fields that require data WA 6027, Australia 2 Defence Science and Technology Group, Third Ave, Edinburgh, SA 5111, Australia 2 https://tools.ietf.org/html/rfc3987 123 L. F. Sikos, D. Philp fusion and/or aggregation from diverse data sources, such as of the W3C have been kept to a minimum with the shutdown cybersecurity [2]. of the Provenance Working Group in 2013 [22]. RDF has a variety of serialization formats and syntaxes, However, in Semantic Web applications, provenance can including RDF/XML,3 Turtle,4 Notation3 (N3),5 N-triples,6 be seen as a means to develop trust [23], as witnessed N-quads,7 JSON-LD,8 RDF/JSON,9 RDFa,10 and HTML5 by implementations of augmented provenance [24, 25] and Microdata.11 These make it possible to express RDF state- semantic provenance [26, 27] in application areas such as ments differently for applications that require compatibility eScience [28–32] and scientific data processing [33], work- with XML, the most compact representation possible, define flow management, bioinformatics [34], laboratory informa- property values in website markup attributes, and so on [3]. tion management [35], digital media archives [36], rec- RDF can be used to provide the uniform representation ommender systems [37], query search result ranking [38], of knowledge for processing data from diverse web sources and decision support for cybersecurity and cyber-situational via syntactic and semantic interoperability. Moreover, RDF awareness [39, 40]. facilitates the partial or full automation of tasks that otherwise This paper is a critical review of alternate approaches to would have to be processed manually [4, 5]. capture provenance for RDF, demonstrates their use in, and These benefits make RDF appealing for a wide range provides their quantitative comparison for, the cybersecu- of applications; however, RDF has shortcomings when it rity domain (which is known to be reliant on provenance) comes to encapsulating metadata to statements. With the from various perspectives. Section 2 covers extensions of the proliferation of heterogeneous structured data sources, such standard RDF data model, state-of-the-art annotation frame- as triplestores and LOD datasets, capturing data prove- works, and purpose-built knowledge organization systems nance,12 i.e., the origin or source of data [7], and the (KOS), including controlled vocabularies15 and ontolo- technique used to extract it, is becoming more and more gies,16 typically written in RDFS17 and OWL,18 respectively. important, because it enables the verification of data, the Section 3 discusses the requirements of RDF triplestores, assessment of reliability [8], the analysis of the processes quadstores, and graph databases to be suitable for stor- that generated the data [9], decision support for areas such ing provenance-aware RDF data, and Sect. 4 details how as cybersecurity [10, 11], cyberthreat intelligence [12], and RDF provenance can be queried. Section 5 reviews the most cyber-situational awareness [13], and helps express trustwor- prominent software tools for manipulating RDF provenance. thiness [14, 15], uncertainty [16], and data quality [17]. Yet, Section 6 describes common application domains. Finally, the RDF data model does not have a built-in mechanism to Sect. 7 demonstrates how application-specific implemen- attach provenance to triples or elements of triples.13 Con- tations can outperform general-purpose RDF provenance sequently, representing provenance data with RDF triples is techniques. a long-standing, non-trivial problem [19]. While the World Wide Web Consortium (W3C) suggested RDF extensions in 2010 to support provenance in the upcoming version of the 2 Formal Representation of RDF Data standard [20, 21], none of these have been implemented in the Provenance next release in 2014, namely in RDF 1.1.14 Related efforts RDF reification19 refers to making an RDF statement about another RDF statement by instantiating the rdf: 3 https://www.w3.org/TR/rdf-syntax-grammar/ 15 4 https://www.w3.org/TR/turtle/ A controlled vocabulary is a finite set of IRI symbols denoting concept names or classes (atomic concepts), role names, properties, 5 https://www.w3.org/TeamSubmission/n3/ and relationships (atomic roles), and individual names (entities), where 6 https://www.w3.org/TR/n-triples/ these three sets are pairwise disjoint. 7 https://www.w3.org/TR/n-quads/ 16 Ontologies are formal conceptualizations of a knowledge domain 8 https://www.w3.org/TR/json-ld/ with complex relationships, and optionally complex rules, suitable for 9 https://www.w3.org/TR/rdf-json/ inferring new statements, thereby making implicit knowledge explicit. 17 10 https://www.w3.org/TR/rdfa-primer/ RDF Schema, an extension of RDF’s vocabulary for creating vocab- ularies, taxonomies, and thesauri; see https://www.w3.org/TR/rdf- 11 https://www.w3.org/TR/microdata/ schema/ for reference. 12 Note that provenance is a complex term, and its usage varies greatly 18 Web Ontology Language (intentionally abbreviated with the W and O in different contexts [6]. swapped as OWL), a fully featured knowledge representation language 13 This can be circumvented by using alternate knowledge representa- for the conceptualization of knowledge domains with complex property tions, such as considering entities as embeddings in a vector space, for restrictions and concept relationships; see https://www.w3.org/OWL/ example [18], however, none of the alternate representations share all for reference. the strengths of RDF. 19 https://www.w3.org/TR/2004/REC-rdf-primer-20040210/# 14 https://www.w3.org/TR/rdf11-new/ reification 123 Provenance-Aware Knowledge Representation: A Survey of Data Models... Statement class and using the rdf:subject, rdf: Listing 2 Shorthand notation for RDF reification predicate, and rdf:object properties of the stan- <?xml version="1.0"?> dard RDF vocabulary20 to identify the elements of the <rdf:RDF xmlns:rdf="http://www.w3.
Load more

Recommended publications
  • Semantic Web: a Review of the Field Pascal Hitzler [email protected] Kansas State University Manhattan, Kansas, USA
    Semantic Web: A Review Of The Field Pascal Hitzler [email protected] Kansas State University Manhattan, Kansas, USA ABSTRACT which would probably produce a rather different narrative of the We review two decades of Semantic Web research and applica- history and the current state of the art of the field. I therefore do tions, discuss relationships to some other disciplines, and current not strive to achieve the impossible task of presenting something challenges in the field. close to a consensus – such a thing seems still elusive. However I do point out here, and sometimes within the narrative, that there CCS CONCEPTS are a good number of alternative perspectives. The review is also necessarily very selective, because Semantic • Information systems → Graph-based database models; In- Web is a rich field of diverse research and applications, borrowing formation integration; Semantic web description languages; from many disciplines within or adjacent to computer science, Ontologies; • Computing methodologies → Description log- and a brief review like this one cannot possibly be exhaustive or ics; Ontology engineering. give due credit to all important individual contributions. I do hope KEYWORDS that I have captured what many would consider key areas of the Semantic Web field. For the reader interested in obtaining amore Semantic Web, ontology, knowledge graph, linked data detailed overview, I recommend perusing the major publication ACM Reference Format: outlets in the field: The Semantic Web journal,1 the Journal of Pascal Hitzler. 2020. Semantic Web: A Review Of The Field. In Proceedings Web Semantics,2 and the proceedings of the annual International of . ACM, New York, NY, USA, 7 pages.
    [Show full text]
  • Resource Description Framework (RDF)
    Semantic Web Technologies Resource Description Framework (RDF) Heiko Paulheim Semantic Web – Architecture here be dragons... Semantic Web Technologies (This lecture) Technical Foundations Berners-Lee (2009): Semantic Web and Linked Data http://www.w3.org/2009/Talks/0120-campus-party-tbl/ 09/13/19 Heiko Paulheim 2 Overview • A brief history of RDF • Encodings of RDF • Semantics and principles of RDF • Embedding RDF in HTML – RDFa, Microdata, Microformats • RDF Tools • Examples for RDF in the wild 09/13/19 Heiko Paulheim 3 History: Metadata on the Web • Goal: more effective rating and ranking of web contents, e.g., by search engines • Who has created this page? • When has it been changed the last time? • What is its topic? • Which is the content's license? • How does it relate to other pages? 09/13/19 Heiko Paulheim 4 Metadata on the Web: Dublin Core • Developed in 1995 at a workshpo in Dublin, Ohio • 15 predefined tags • A widely accepted standard (ISO 15836:2009) • May be embedded into HTML: <html> <head profile="http://dublincore.org/documents/2008/08/04/dc-html/"> <title>Semantic Web</title> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" > <meta name="DC.publisher" content="University of Mannheim" /> <meta name="DC.subject" content="Semantic Web" /> <meta name="DC.creator" content="Heiko Paulheim" /> <meta name="DC.relation" content="http://www.w3.org/2001/sw/" /> ... </head> <body> ... </body> </html> 09/13/19 Heiko Paulheim 5 Metadata on the Web: Dublin Core • Identifier • Creator • Format • Publisher • Type • Contributor
    [Show full text]
  • Overview of Provenance on the Web by the W3C Provenance Incubator Group
    Overview of Provenance on the Web by the W3C Provenance Incubator Group Semantic Web Activity World Wide Web Consortium http://www.w3.org/2005/Incubator/prov/wiki Special thanks to contributing group members: Chris Bizer, James Cheney, Sam Coppens, Kai Eckert, Andre Freitas, Irini Fundulaki, Daniel Garijo, Yolanda Gil, Jose Manuel Gomez Perez, Paul Groth, Olaf Hartig, Deborah McGuinness, Simon Miles, Paolo Missier, Luc Moreau, James Myers, Michael Panzer, Paulo Pinheiro da Silva, Christine Runnegar, Satya Sahoo, Yogesh Simmhan, Raphaël Troncy, Jun Zhao W3C Provenance XG http://www.w3.org/2005/Incubator/prov/wiki November 30, 2010 1 Outline Need for provenance on the web W3C Provenance Group: What is known about provenance in the community • Definition of provenance • Key dimensions of provenance • Use cases: Requirements for provenance • State-of-the-art and existing provenance vocabularies Towards a standard for provenance W3C Provenance XG http://www.w3.org/2005/Incubator/prov/wiki November 30, 2010 2 Provenance is Key in Open Information Systems (such as the Web) DB Local paper Blog Web site Blog <LaBlanca Headquartered-in Abbu-Dabi> <LaBlanca Exports-to-Syria> Human assessment <LaBlanca Sponsor Demonstration-Downtown-Kabul> <LaBlanca Headquartered-in Kabul> Provenance questions useful for information integration: • Who created that content (author/attribution)? • Was the content ever manipulated, if so by what processes/entities? • Who is providing that content (repository)? • What is the timeliness of that content? • Can any
    [Show full text]
  • GVP LOD: ONTOLOGIES and SEMANTIC REPRESENTATION Vladimir Alexiev, Data and Ontology Group, Ontotext Corp
    GVP LOD: ONTOLOGIES AND SEMANTIC REPRESENTATION Vladimir Alexiev, Data and Ontology Group, Ontotext Corp CIDOC Congress, Dresden, Germany 2014-09-05: International Terminology Working Group 2014-09-09: Getty special session full version (HTML), short version (PDF, slideshare). Press O for overview, H for help. Proudly made in plain text with reveal.js, org0-reveal, org-mode and emacs. ONTOTEXT SCOPE OF WORK http://vocab.getty.edu Ontology development: http://vocab.getty.edu/ontology Contribution to ISO 25964 ontology (latest thesauri standard) Complete mapping specification Help with R2RML conversion scripts, contrib to RDB2RDF (Perl), rrx:languageColumn extension GraphDB (OWLIM) repository. Enterprise Edition (clustered) Sem app dev (customized Forest UI), tech consulting SPARQL 1.1 endpoint: http://vocab.getty.edu/sparql Documentation (100 pages): http://vocab.getty.edu/doc Lots of sample queries, incl charts, geographic, etc Per-entity export files, explicit/total data dumps Help desk / support Presentations, scientific papers SEMANTIC RESOLUTION & CONTENT NEGOTIATION All GVP, AAT and TGN URLs resolve, returning human or machine readable content through content negotiation (303 redirect). Eg about the ontology: http://vocab.getty.edu/ontology semantic URI, content-negotiated http://vocab.getty.edu/ontology.html page (application/xhtml+xml) http://vocab.getty.edu/ontology.rdf application/rdf+xml http://vocab.getty.edu/ontology.ttl text/turtle Eg about an AAT subject http://vocab.getty.edu/aat/300011154 semantic URI, con-neg http://vocab.getty.edu/aat/300011154.html page (application/xhtml+xml) http://vocab.getty.edu/aat/300011154.rdf application/rdf+xml http://vocab.getty.edu/aat/300011154.ttl text/turtle http://vocab.getty.edu/aat/300011154.nt NTriples GVP VOCABULARY DATA Scope includes: Subjects: Concepts but also non-concepts Obsolete subjects (and dct:isReplacedBy) Terms: plain (SKOS) & rich (SKOS-XL).
    [Show full text]
  • Metadata Provenance in Europeana and the Semantic Web / Von Kai Eckert
    Humboldt-Universitat¨ zu Berlin Institut fur¨ Bibliotheks- und informationswissenschaft Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft Heft 332 Metadata Provenance in Europeana and the Semantic Web von Kai Eckert Metadata Provenance in Europeana and the Semantic Web von Kai Eckert Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft Begründet von Peter Zahn Herausgegeben von Konrad Umlauf Humboldt-Universität zu Berlin Heft 332 Eckert, Kai Metadata Provenance in Europeana and the Semantic Web / von Kai Eckert. - Berlin : Institut für Bibliotheks- und Informationswissenschaft der Humboldt- Universität zu Berlin, 2012. – VIII, 69 S. : graph. Darst. - (Berliner Handre- ichungen zur Bibliotheks- und Informationswissenschaft ; 332) ISSN 14 38-76 62 Abstract: This thesis gives an overview on various ways and best-practices to handle metadata provenance in the Semantic Web, including the employment of linked data principles, reification, OAI-ORE, and named graphs. It reflects the work of the W3C Provenance Working Group, the W3C RDF Working Group and the DCMI Metadata Provenance Task Group, with a special emphasis on a map- ping between Dublin Core and the upcoming PROV model. Based on these foundations, a proposal for the Europeana Data Model is developed. Zusammenfassung: Diese Arbeit gibt einen Überblick über verschiedene Verfahren und Praktiken, die Provenienz von Metadaten im Semantic Web zu repräsentieren, einschließlich der Anwendung der Linked Data Prinzipien, Reification, OAI-ORE und Named Graphs. Es werden die Arbeiten der W3C Provenance Working Group, der W3C RDF Working Group und der DCMI Metadata Provenance Task Group wiedergegeben, mit einer vertiefenden Betrachtung eines Mappings zwischen Dublin Core und dem gerade entstehenden PROV-Modell. Basierend auf diesen Grundlagen wird ein Vorschlag für das Datenmodell der Europeana entwickelt.
    [Show full text]
  • Provenance-Aware Knowledge Representation: a Survey of Data Models and Contextualized Knowledge Graphs
    Edith Cowan University Research Online ECU Publications Post 2013 1-1-2020 Provenance-aware knowledge representation: A survey of data models and contextualized knowledge graphs Leslie F. Sikos Edith Cowan University Dean Philp Follow this and additional works at: https://ro.ecu.edu.au/ecuworkspost2013 Part of the Computer Sciences Commons 10.1007/s41019-020-00118-0 Sikos, L. F., & Philp, D. (2020). Provenance-aware knowledge representation: A survey of data models and contextualized knowledge graphs. Data Science and Engineering. https://doi.org/10.1007/s41019-020-00118-0 This Journal Article is posted at Research Online. https://ro.ecu.edu.au/ecuworkspost2013/8373 Data Science and Engineering https://doi.org/10.1007/s41019-020-00118-0 Provenance-Aware Knowledge Representation: A Survey of Data Models and Contextualized Knowledge Graphs Leslie F. Sikos1 · Dean Philp2 Received: 1 April 2019 / Revised: 13 February 2020 / Accepted: 3 March 2020 © The Author(s) 2020 Abstract Expressing machine-interpretable statements in the form of subject-predicate-object triples is a well-established practice for capturing semantics of structured data. However, the standard used for representing these triples, RDF, inherently lacks the mechanism to attach provenance data, which would be crucial to make automatically generated and/or processed data author- itative. This paper is a critical review of data models, annotation frameworks, knowledge organization systems, serialization syntaxes, and algebras that enable provenance-aware RDF statements. The various approaches are assessed in terms of stan- dard compliance, formal semantics, tuple type, vocabulary term usage, blank nodes, provenance granularity, and scalability. This can be used to advance existing solutions and help implementers to select the most suitable approach (or a combination of approaches) for their applications.
    [Show full text]
  • Understanding Metadata
    Understanding Metadata What is Metadata? .................................................................................................. 1 What Does Metadata Do? ................................................................................ 1 Structuring Metadata ................................................................................. 2 Metadata Schemes and Element Sets ............................................... 3 Dublin Core .....................................................................................................................3 TEI and METS ..............................................................................................................4 MODS .......................................................................................................................5 EAD and LOM......................................................................................................6 <indecs>, ONIX, CDWA, and VRA ..................................................................7 MPEG ..........................................................................................................8 FGDC and DDI ........................................................................................9 Creating Metadata ................................................ 10 Interoperability and Exchange of Metadata ....11 Future Directions .................................... 12 More Information on Metadata ........ 13 Glossary ...................................... 15 Acknowledgements Understanding Metadata is a
    [Show full text]
  • Guideline Semantic Web Guideline
    Semantic web Guideline Semantic web guideline This Guideline is part of the cooperation project between São Paulo State Government and the UK Government SPUK Improving business environment through transparency in São Paulo State Melhoria do ambiente de negócios por meio da transparência no Estado de São Paulo Partners Secretaria de Governo Year 2015 This content is licensed under Creative Commons. Attribution-NonCommercial-NoDerivs BY NC ND CC BY-NC-ND SPUK Improving business environment through transparency in São Paulo State Melhoria do ambiente de negócios por meio da transparência no Estado de São Paulo EXECUTION GOVERNMENT OF THE STATE OF SÃO PAULO Secretariat of Government • Sub-secretariat for Partnerships and Innovation Chief of Staff • Special Advisor for Foreign Affairs Foundation for Administrative Development - Fundap State System for Data Analysis Foundation - Seade Public Adminsitration Transparency Board THE UK GOVERNMENT British Embassy in Brasília THE BRAZILIAN NETWORK INFORMATION CENTER - NIC.br Web Technologies Study Center - CeWeb.br AUTHOR Carlos Laufer COORDINATION General: Roberto Agune - iGovSP Vagner Diniz – CeWeb.br Executive and Editorial: Caroline Burle dos Santos Guimarães - CeWeb.br Helena Pchevuzinske - iGovSP Sergio Pinto Bolliger - iGovSP IDEALIZATION Alvaro Gregório - iGovSP DESIGN Alcione de Godoy - iGovSP - e-books Deblyn Pereira Prado - NIC.br - HTML Ricardo Hurmus - Bulerías Arte & Design - ilustrações summary 1. Introduction...................................................................................................6
    [Show full text]
  • Provenance Description of Metadata Using PROV with PREMIS for Long-Term Use of Metadata
    Proc. Int’l Conf. on Dublin Core and Metadata Applications 2014 Provenance Description of Metadata using PROV with PREMIS for Long-term Use of Metadata Chunqiu Li Shigeo Sugimoto Graduate School of Library, Information Faculty of Library, Information and Media Studies, and Media Science, University of Tsukuba, Japan University of Tsukuba, Japan [email protected] [email protected] Abstract Provenance description is necessary for long-term preservation of digital resources. Open Archival Information System (OAIS) and Preservation Metadata: Implementation Strategies (PREMIS), which are well-known standards designed for digital preservation, define descriptive elements for digital preservation. Metadata has to be preserved as well as primary resource in order to keep the primary resources alive. However, due to the changing technology and information context, not only primary digital resources but also metadata are at risk of damage or even loss. Thus, metadata preservation is important as well as preservation of primary digital resources. Metadata preservation is a rather new research topic but critical for keeping metadata about preserved resources consistently over time. This paper focuses on provenance as an important issue in digital preservation. It discusses provenance description based on two major metadata standards—PROV and PREMIS. The goal of this study is to clarify a model for describing provenance for metadata preservation. This paper first describes some well-known standards—OAIS, PREMIS, PROV, and so forth, and then discusses a novel model of provenance description based on the PROV Ontology (PROV-O) and PREMIS OWL Ontology. The paper gives provenance description examples using PROV-O and PREMIS OWL Ontology respectively.
    [Show full text]
  • PROV-O: the PROV Ontology
    The University of Manchester Research PROV-O: The PROV Ontology Link to publication record in Manchester Research Explorer Citation for published version (APA): Lebo, T., Sahoo, S., McGuinness, D., Belhajjame, K., Cheney, J., Corsar, D., Garijo, D., Soiland-Reyes, S., Zednik, S., & Zhao, J. (2013). PROV-O: The PROV Ontology. (W3C Recommendation). World Wide Web Consortium. http://www.w3.org/TR/2013/REC-prov-o-20130430/ Citing this paper Please note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscript or Proof version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version. General rights Copyright and moral rights for the publications made accessible in the Research Explorer are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Takedown policy If you believe that this document breaches copyright please refer to the University of Manchester’s Takedown Procedures [http://man.ac.uk/04Y6Bo] or contact [email protected] providing relevant details, so we can investigate your claim. Download date:04. Oct. 2021 PROV-O: The PROV Ontology http://www.w3.org/TR/2013/REC-prov-o-20130430/ PROV-O: The PROV Ontology W3C Recommendation 30 April 2013 This version: http://www.w3.org/TR/2013/REC-prov-o-20130430/ Latest published version: http://www.w3.org/TR/prov-o/ Implementation
    [Show full text]
  • PAV Ontology: Provenance, Authoring and Versioning
    Ciccarese et al. Journal of Biomedical Semantics 2013, 4:37 http://www.jbiomedsem.com/content/4/1/37 JOURNAL OF BIOMEDICAL SEMANTICS RESEARCH Open Access PAV ontology: provenance, authoring and versioning Paolo Ciccarese1,2*†, Stian Soiland-Reyes3†, Khalid Belhajjame3, Alasdair JG Gray3, Carole Goble3 and Tim Clark1,2,3 Abstract Background: Provenance is a critical ingredient for establishing trust of published scientific content. This is true whether we are considering a data set, a computational workflow, a peer-reviewed publication or a simple scientific claim with supportive evidence. Existing vocabularies such as Dublin Core Terms (DC Terms) and the W3C Provenance Ontology (PROV-O) are domain-independent and general-purpose and they allow and encourage for extensions to cover more specific needs. In particular, to track authoring and versioning information of web resources, PROV-O provides a basic methodology but not any specific classes and properties for identifying or distinguishing between the various roles assumed by agents manipulating digital artifacts, such as author, contributor and curator. Results: We present the Provenance, Authoring and Versioning ontology (PAV, namespace http://purl.org/pav/): a lightweight ontology for capturing “just enough” descriptions essential for tracking the provenance, authoring and versioning of web resources. We argue that such descriptions are essential for digital scientific content. PAV distinguishes between contributors, authors and curators of content and creators of representations in addition to the provenance of originating resources that have been accessed, transformed and consumed. We explore five projects (and communities) that have adopted PAV illustrating their usage through concrete examples. Moreover, we present mappings that show how PAV extends the W3C PROV-O ontology to support broader interoperability.
    [Show full text]
  • Managing and Using Provenance in the Semantic Web
    Managing and Using Provenance in the Semantic Web Renata Queiroz Dividino Institute for Web Science and Technologies University of Koblenz-Landau June 2017 Vom Promotionsausschuss des Fachbereichs 4: Informatik der Universit¨atKoblenz-Landau zur Verleihung des akademischen Grades Doktor der Naturwissenschaften (Dr. rer. nat.) genehmigte Dissertation. Datum der wissenschaftlichen Aussprache: 14 Dezember 2016 Vorsitz des Promotionsausschusses: Prof. Dr. Hannes Frey Gutachter: Prof. Dr. Steffen Staab Gutachter: Prof. Dr. York Sure-Vetter Gutachter: Prof. Dr. Luc Moreau Abstract The Web contains some extremely valuable information; however, often poor quality, inac- curate, irrelevant or fraudulent information can also be found. With the increasing amount of data available, it is becoming more and more difficult to distinguish truth from specula- tion on the Web. One of the most, if not the most, important criterion used to evaluate data credibility is the information source, i.e., the data origin. Trust in the information source is a valuable currency users have to evaluate such data. Data popularity, recency (or the time of validity), reliability, or vagueness ascribed to the data may also help users to judge the validity and appropriateness of information sources. We call this knowledge derived from the data the provenance of the data. Provenance is an important aspect of the Web. It is essential in identifying the suitability, veracity, and reliability of information, and in deciding whether information is to be trusted, reused, or even integrated with other information sources. Therefore, models and frameworks for representing, managing, and using provenance in the realm of Semantic Web technologies and applications are critically required.
    [Show full text]