Using Paraphrasers to Detect Duplicities in Ontologies

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00580726" target="_blank" >RIV/67985807:_____/23:00580726 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21240/23:00368478

Result on the web

<a href="https://doi.org/10.5220/0012164500003598" target="_blank" >https://doi.org/10.5220/0012164500003598</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0012164500003598" target="_blank" >10.5220/0012164500003598</a>

Result language

angličtina

Original language name

Using Paraphrasers to Detect Duplicities in Ontologies

Original language description

This paper contains a machine-learning-based approach to detect duplicities in ontologies. Ontologies are formal specifications of shared conceptualizations of application domains. Merging and enhancing ontologies may cause the introduction of duplicities into them. The approach to duplicities proposed in this work presents a solution that does not need manual corrections by domain experts. Source texts consist of short textual descriptions from considered ontologies, which have been extracted and automatically paraphrased to receive pairs of sentences with the same or a very close meaning. The sentences in the received dataset have been embedded into Euclidean vector space. The classification task was to determine whether a given pair of sentence embeddings is semantically equivalent or different. The results have been tested using test sets generated by paraphrases as well as on a small real-world ontology. We also compared solutions by the most similar existing approach, based on GloVe and WordNet, with solutions by our approach. According to all considered metrics, our approach yielded better results than the compared approach. From the results of both experiments, the most suitable for the detection of duplicities in ontologies is the combination of BERT with support vector machines. Finally, we performed an ablation study to validate whether all paraphrasers used to create the training set for the classification were essential.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2

ISBN

978-989-758-671-2

ISSN

2184-3228

e-ISSN

—

Number of pages

10

Pages from-to

40-49

Publisher name

SciTePress

Place of publication

Setubal

Event location

Rome / hybrid

Event date

Nov 13, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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