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LEOnto+: a scalable ontology enrichment approach

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Abstract

Distributional semantic models like the Latent Dirichlet Allocation (LDA) model Guo et al. (Concurr. Comput.: Pract. Exper. 29(3), 319–343 2016) consist of defining similar representation of words according to their similar context. LDA has been originally used to model documents and extract topics in Information Retrieval. In recent years, LDA has become a hot topic among ontology learning because of the exponential increase of the number of documents and textual data not only on the web but also in digital libraries. LDA-based approaches have proven to provide the best result. However, they suffer of several limitations related to concept and relation extraction, as well as handling the corpus evolution and maintaining. In order to cope with these problems, we propose in this paper LEOnto+, an extended version of LEOnto (Tissaoui et al. 2020, Tissaoui et al. SN Comput. Sci. J. 1: 336 2020), to provide a new approach for automatic ontology enriching from textual corpus. In LEOnto+, LDA is used to provide dimension reduction and to identify semantic relationships between topic-document and word-topic using probability distributions. Here, we provide several experiments conducted using several evaluation techniques (Evaluation based criteria, Gold standard evaluation, Expert evaluation, Task-based evaluation and Corpus-based evaluation). We also compare the results of LEOnto+ with two existing methods using their respective datasets. The evaluation results show that LEOnto+ outperforms the aforementioned methods (particularly in terms of precision). We also compare our approach using two large corpus in order to demonstrate its scalability.

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Notes

  1. This difficulty in capturing the knowledge required by knowledge-based systems is called “knowledge acquisition bottleneck”.

  2. LEOnto is limited to extracting concepts and few relations.

  3. https://elmcip.net/platformsoftware/rita

  4. https://wordnet.princeton.edu/

  5. https://spider.sigappfr.org/research-projects/corpus-journal-www/

  6. https://git.gesis.org/open-data/solis-sofis/blob/18389fb2b6cecd7fe8d5b6b4e89d40eda33a7191/readme.md

  7. https://www.nlm.nih.gov/bsd/pmresources.html

  8. https://paperswithcode.com/datasets

  9. https://www.nlm.nih.gov/portals/researchers.html

  10. https://www.w3.org/TR/sparql11-query/

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  1. VPNC Lab., FSJEGJ, University of Jendouba, Avenue Maghreb Arabe, 8189, Jendouba, Tunisia

    Salma Sassi & Anis Tissaoui

  2. Univ Pau & Pays Adour, E2S UPPA, LIUPPA, EA3000, Anglet, France

    Richard Chbeir

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  1. Salma Sassi
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Sassi, S., Tissaoui, A. & Chbeir, R. LEOnto+: a scalable ontology enrichment approach. World Wide Web 25, 2347–2378 (2022). https://doi.org/10.1007/s11280-021-00997-x

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