SKOS Concepts and Natural Language Concepts: an Analysis of Latent Relationships in KOSs

Mastora, Anna and Peponakis, Manolis and Kapidakis, Sarantos SKOS Concepts and Natural Language Concepts: an Analysis of Latent Relationships in KOSs. Journal of Information Science, 2017, vol. 43, n. 4, pp. 492-508. [Journal article (Paginated)]

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English abstract

The vehicle to represent Knowledge Organisation Systems (KOSs) in the environment of the Semantic Web and linked data is the Simple Knowledge Organisation System (SKOS). SKOS provides a way to assign a Uniform Resource Identifier (URI) to each concept, and this URI functions as a surrogate for the concept. This fact makes of main concern the need to clarify the URIs’ ontological meaning. The aim of this study is to investigate the relationship between the ontological substance of KOS concepts and concepts revealed through the grammatical and syntactic formalisms of natural language. For this purpose, we examined the dividableness of concepts in specific KOSs (i.e. a thesaurus, a subject headings system and a classification scheme) by applying Natural Language Processing (NLP) techniques (i.e. morphosyntactic analysis) to the lexical representations (i.e. RDF literals) of SKOS concepts. The results of the comparative analysis reveal that, despite the use of multi-word units, thesauri tend to represent concepts in a way that can hardly be further divided conceptually, while subject headings and classification schemes – to a certain extent – comprise terms that can be decomposed into more conceptual constituents. Consequently, SKOS concepts deriving from thesauri are more likely to represent atomic conceptual units and thus be more appropriate tools for inference and reasoning. Since identifiers represent the meaning of a concept, complex concepts are neither the most appropriate nor the most efficient way of modelling a KOS for the Semantic Web.

Item type: Journal article (Paginated)
Keywords: atomic concepts; composite concepts; dividableness; Knowledge Organisation Systems (KOSs); morphosyntactic analysis; Natural Language Processing (NLP) techniques; relations; Semantic Web; Simple Knowledge Organisation System (SKOS)
Subjects: I. Information treatment for information services > IC. Index languages, processes and schemes.
I. Information treatment for information services > IL. Semantic web
Depositing user: Manolis Peponakis
Date deposited: 05 Oct 2017 05:51
Last modified: 05 Oct 2017 05:51


[1] Antoniou G, Van Harmelen F. A Semantic Web Primer. 2ed ed. Cambridge, Mass: MIT Press, 2008.

[2] W3C. SKOS Simple Knowledge Organization System Reference, (2009, accessed 1 March 2014).

[3] Baker T, Bechhofer S, Isaac A, et al. Key choices in the design of Simple Knowledge Organization System (SKOS). Web Semantics: Science, Services and Agents on the World Wide Web 2013; 20: 35–49.

[4] Giunchiglia F, Maltese V, Dutta B. Domains and Context: First Steps towards Managing Diversity in Knowledge. Web Semantics: Science, Services and Agents on the World Wide Web 2012; 12–13: 53–63.

[5] Giunchiglia F, Dutta B, Maltese V. From Knowledge Organization to Knowledge Representation. Departmental Technical Report DISI-13-027, Trento: Università di Trento, (2013, accessed 7 February 2015).

[6] Guns R. Tracing the origins of the semantic web. Journal of the American Society for Information Science and Technology 2013; 64: 2173–2181.

[7] W3C. SKOS eXtension for Labels (SKOS-XL) Namespace Document - HTML Variant, (2009, accessed 16 October 2014).

[8] W3C. SKOS Implementation Report, (19 May 2009, accessed 29 September 2015).

[9] Hodge G. Systems of Knowledge Organization for Digital Libraries: Beyond Traditional Authority Files. Washington, DC: Council on Library and Information Resources, (2000, accessed 2 August 2015).

[10] Hjørland B. Concept Theory. Journal of the American Society for Information Science and Technology 2009; 60: 1519–1536.

[11] Egozi O, Markovitch S, Gabrilovich E. Concept-Based Information Retrieval Using Explicit Semantic Analysis. ACM Transactions on Information Systems 2011; 29: 8:1–8:34.

[12] Hjørland B. Concepts, Paradigms and Knowledge Organization. In: Gnoli C, Mazzocchi F (eds) Paradigms and Conceptual Systems in Knowledge Organization: Proceedings of the Eleventh International ISKO Conference, 23-26 February 2010, Rome, Italy. Würzburg: Ergon, 2010, pp. 38–42.

[13] Martínez-González MM, Alvite-Díez M-L. On the evaluation of thesaurus tools compatible with the Semantic Web. Journal of Information Science 2014; 40: 711–722.

[14] European Union, Publications Office. Eurovoc: the EU’s multilingual thesaurus, (accessed 13 July 2015).

[15] Library of Congress. Library of Congress Subject Headings: LC Linked Data Service, (accessed 13 July 2015).

[16] OCLC. Dewey Decimal Classification, (accessed 13 July 2015).

[17] ISO. ISO 25964-1: Information and documentation Thesauri and interoperability with other vocabularies Part 1: Thesauri for information retrieval. ISO 25964-1:2011(E), International Organization for Standardization (ISO), 2011.

[18] Harper CA. Encoding Library of Congress Subject Headings in SKOS: Authority control for the Semantic Web. In: Proceedings of the 2006 International Conference on Dublin Core and Metadata Applications. Manzanillo, Mexico: Dublin Core Metadata Initiative, (2006, accessed 1 March 2014).

[19] Mitchell JS, Zeng ML, Žumer M. Modeling Classification Systems in Multicultural and Multilingual Contexts. Cataloging & Classification Quarterly 2014; 52: 90–101.

[20] Pinker S. The Language Instinct: How the Mind Creates Language. third edition. New York: Harper Perennial Modern Classics, 2007.

[21] Ganbold A, Farazi F, Giunchiglia F. An experiment in managing language diversity across cultures. Departmental Technical Report DISI - 13 - 032, Trento: Università di Trento, (4 October 2013, accessed 23 April 2014).

[22] Kless D, Lindenthal J, Milton S, et al. Interoperability of knowledge organization systems with and through ontologies. In: Slavic A, Civallero E (eds) Classification & ontology: formal approaches and access to knowledge: proceedings of the international UDC seminar 19-20 September 2011, The Hague, the Netherlands, organized by UDC Consortium, The Hague. Würzburg: Ergon, 2011, pp. 55–74.

[23] Portner PH. What is Meaning?: Fundamentals of Formal Semantics. Malden, MA: Blackwell, 2005.

[24] Manning CD, Schütze H. Foundations of statistical natural language processing. Cambridge, Mass.: MIT Press, 2005.

[25] Murphy GL. The Big Book of Concepts. Cambridge, Mass.: MIT Press, 2002.

[26] Wilks Y. Good and Bad Arguments About Semantic Primitives. In: Ahmad K, Brewster C, Stevenson M (eds) Words and Intelligence I. Springer Netherlands, pp. 103–139, (2007, accessed 29 March 2015).

[27] Haspelmath M. Word Classes and Parts of Speech. In: Baltes NJSB (ed) International Encyclopedia of the Social & Behavioral Sciences. Oxford: Pergamon, pp. 16538–16545, (2001, accessed 29 March 2015).

[28] Helbig H. Knowledge Representation and the Semantics of Natural Language. Berlin: Springer, (2006, accessed 28 April 2015).

[29] Booij G. The Grammar of Words: An Introduction to Linguistic Morphology. 3rd edition. Oxford University Press, 2012.

[30] Haspelmath M, Dryer MS, Gil D, et al. (eds). The world atlas of language structures. New York: Oxford University Press, 2005.

[31] Clark A, Fox C, Lappin S (eds). The Handbook of Computational Linguistics and Natural Language Processing. Wiley-Blackwell, (2010, accessed 3 May 2015).

[32] Prokopidis P, Georgantopoulos B, Papageorgiou H. A suite of NLP tools for Greek. In: Proceedings of the 10th International Conference of Greek Linguistics. Komotini, Greece, (2011).

[33] Katsogiannu M, Efthimiou E (eds). Ellēnikē orologia: ereuna kai epharmoges = Hellenic terminology: research and applications [written in Greek]. Athēna: Kastaniōtēs, 2004.

[34] Jain P, Hitzler P, Yeh PZ, et al. Linked Data is merely more data. In: Brickley D, Chaudhri VK, Halpin H, et al. (eds) Linked Data Meets Artificial Intelligence. California: AAAI Press, pp. 82–86, (2010).

[35] Soergel D, Lauser B, Liang A, et al. Reengineering Thesauri for New Applications: the AGROVOC Example. Journal of Digital Information; 4, (2006, accessed 17 November 2014).

[36] L’Homme M-C, Bernier-Colborne G. Terms as labels for concepts, terms as lexical units: A comparative analysis in ontologies and specialized dictionaries. Applied Ontology 2012; 7: 387–400.

[37] Binding C, Tudhope D. KOS at your Service: Programmatic Access to Knowledge Organisation Systems. Journal of Digital Information; 4, (2004, accessed 30 September 2015).

[38] Andersen H, Barker P, Chen X. The cognitive structure of scientific revolutions. 1. paperback ed. Cambridge: Cambridge Univ. Press, 2013.

[39] Zong N, Lee S, Kim H-G. Discovering expansion entities for keyword-based entity search in linked data. Journal of Information Science 2015; 41: 209–227.

[40] Panzer M, Zeng ML. Modeling classification systems in SKOS: Some challenges and best-practice recommendations. Seoul, Korea: DCMI, pp. 3–14, (2009, accessed 1 October 2015).


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