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Endpoint for Semantic Knowledge (ESK)

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Automatic Processing of Natural-Language Electronic Texts with NooJ (NooJ 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 667))

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Abstract

This work arises from the evaluation of the existing methods used to process knowledge stored in on-line repositories and databases.

Such methods represent an attempt to improve the techniques of Knowledge Extraction (KE) and Representation (KR) to guarantee a more accurate answer to users’ information request.

Different research groups are nowadays developing various endpoint systems, as for instance Virtuoso, which is devoted to run queries against online Knowledge Bases (KBs), mainly DBpedia.

We present an Endpoint for Semantic Knowledge (ESK), a system which integrates NooJ Linguistic Resources (LRs) in an environment suitable for a semantic search engine. ESK is structured as a SPARQL endpoint, which applies a deep semantic analysis, based on the development of a matching process between a set of machine semantic formalisms and a set of Natural Language (NL) sentences.

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Notes

  1. 1.

    This work was carried out while the author was affiliated with the Dept. of Political, Social and Communication Sciences - University of Salerno, Fisciano (SA), Italy.

  2. 2.

    Translation by the editor. (…) – une grammaire G rationnelle ou hors contexte qui définit un langage plus grand que le langage contextual qu'on veut décrire – une série de contraintes qui excluent certaines sequences reconnues par G pur ne garder que les séquences qui appartiennent véritablement au language contextual voulu [2].

  3. 3.

    As reported in the Web site for the Semantic Web, “A SPARQL endpoint enables users (human or other) to query a knowledge base via the SPARQL language. Results are typically returned in one or more machine-processable formats. Therefore, a SPARQL endpoint is mostly conceived as a machine-friendly interface towards a knowledge base. Both the formulation of the queries and the human-readable presentation of the results should typically be implemented by the calling software, and not be done manually by human users”. Source: http://semanticweb.org/wiki/SPARQL_endpoint.html.

  4. 4.

    http://dbpedia.org/sparql.

  5. 5.

    Other samples of endpoints, which allow to access DBpedia KB, are indicated at http://www.w3.org/wiki/SparqlEndpoints. Anyway, due to the spread of several KBs, different endpoints are available. For more information on current alive SPARQL endpoints, see: http://www.w3.org/wiki/SparqlEndpoints.

  6. 6.

    Indeed, as [9] states, LG considers the simple sentence as the base unit of analysis, which means for LG: A lexicon-grammar is constituted of the elementary sentences of a language. Instead of considering words as basic syntactic units to which grammatical information is attached, we use simple sentences (subject-verb-objects) as dictionary entries. Hence, a full dictionary item is a simple sentence with a description of the corresponding distributional and transformational properties.

  7. 7.

    Sentence is considered as an ensemble organisé (organized set) in which mots (words) are constituants (constituent elements). A word in a sentence is not isolated as in the dictionary, due to the fact that we can perceive connexions (connections) between such word and its neighbors.

  8. 8.

    For more information, see: http://en.wikipedia.org/wiki/Europeana and http://en.wikipedia.org/wiki/Simple_Knowledge_Organization_System.

  9. 9.

    Result derived from the analysis of Wikipedia entry “Peter Levi”. https://en.wikipedia.org/wiki/Peter_Levi.

  10. 10.

    This LRs are based on the Italian Linguistic Module, created by [14] and maintained by the team of the Laboratory of Computational Linguistics “Maurice Gross” of University of Salerno.

  11. 11.

    It is worth noticing that in such example we do not use edm:year in order to tag birth/death date, due to the fact that such property refers to an event in the life of the original analogue or born digital object. Therefore, edm:year property is not applicable to the class Agent.

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia

    Maria Pia di Buono

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  1. Maria Pia di Buono
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Correspondence to Maria Pia di Buono .

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Editors and Affiliations

  1. UniversitĂ  degli Studi di Salerno , Fisciano, Italy

    Linda Barone

  2. UniversitĂ  degli Studi di Salerno , Fisciano, Italy

    Mario Monteleone

  3. Université de Franche-Comté , Paris, France

    Max Silberztein

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di Buono, M.P. (2016). Endpoint for Semantic Knowledge (ESK). In: Barone, L., Monteleone, M., Silberztein, M. (eds) Automatic Processing of Natural-Language Electronic Texts with NooJ. NooJ 2016. Communications in Computer and Information Science, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-55002-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-55002-2_19

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  • Online ISBN: 978-3-319-55002-2

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