An Approach for Automatic Ontology Enrichment from Texts

Mellal, Nassima; Guerram, Tahar; Bouhalassa, Faiza

doi:10.31449/inf.v45i1.2586

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…LDA provided efficient dimension reduction, to capture semantic word-topic and topic-document relations in terms of probability distributions. Mellal et al (2021) proposed a method that is based on Natural Language Processing (NLP) techniques but augmented by a heuristic algorithm that allows reducing extracted sentences to SVO (Subject, Verb, and Object) and identifying relations with those of the existing ontology as well as the placement of new concepts in it.…”

Section: Related Workmentioning

confidence: 99%

Comparative Evaluation of Keyphrase Extraction Tools for Semantic Analysis of Climate Change Scientific Reports and Ontology Enrichment

Katsadaki,

Kokla

2024

AGILE GIScience Ser.

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Abstract. Keyphrase extraction is a process used for identifying important concepts and entities within unstructured information sources to facilitate ontology enrichment, semantic analysis, and information retrieval. In this paper, three different tools for key phrase extraction are compared to evaluate their accuracy and effectiveness for extracting geospatial and climate change concepts from climate change reports: frequency-inverse document frequency (TF-IDF), Amazon Comprehend, and YAKE. Climate change reports contain vital information for comprehending the complexity of climate change causes, impacts, and interconnections, and include wealth of information on geospatial concepts, locations, and events but the diverse terminology used complicates information extraction and organization. The highest scoring keyphrases are further used to enrich and populate the SWEET ontology with concepts and instances related to climate change and meaningful relations between them to support semantic representation and formalization of knowledge.

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Section: Related Workmentioning

confidence: 99%

Comparative Evaluation of Keyphrase Extraction Tools for Semantic Analysis of Climate Change Scientific Reports and Ontology Enrichment

Katsadaki,

Kokla

2024

AGILE GIScience Ser.

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“…The regional ontologies are constructed to offer a general representation of knowledge in the several elds. Based on the classi cations of ontology [52] and the identi cation of ontology features [54], the schema of regional ontology can be represented as…”

Section: Development and Ontology-based Knowledge Modelmentioning

confidence: 99%

Study on Ontological Knowledge Integration of Micromachining for Collaborative Process

Liang

2023

J. Adv. Manuf. Syst.

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In collaborative manufacturing environment, the knowledge integration in collaborative process is very crucial, especially the heterogeneous information distributed among various cooperative suppliers. To tackle the optimization of knowledge consolidation in co-manufacturing, a knowledge integration with ontological framework is proposed in this study. First, by representing the collaborative business activities for cooperative suppliers, an architecture of knowledge integration with ontological schema is generated. The ontological model of knowledge concepts is explored in terms of the analysis of the ontological structure of knowledge. Aiming at the concept, an approach of ontology integration on the basis of ontological similarity is promoted. There are two portions included in this approach, mapping and combination, which are implemented through the analysis of similarity in two ontologies namely universal and regional. To integrate the two ontologies, the relations and restrictions among fields are definitely depicted by the Semantic Web Rule Language. Eventually, the presented framework is illustrated through a case study, and the outcomes satisfy the needs of knowledge combination to assure that the effectiveness of the presented method is confirmed.

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“…They started with a general domain ontology called the GermanNet ontology. There are many other recent works presented to build ontologies from scratch, automatically or semi-automatically such as (Hier & Brint, 2020;Yilahun, Imam & Hamdulla, 2020;Sanagavarapu, Iyer & Reddy, 2021;Mellal, Guerram & Bouhalassa, 2021).…”

Section: Ontology Creationmentioning

confidence: 99%

An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource

Ibrahim¹,

Gauch²,

Salman³

et al. 2021

PeerJ Computer Science

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Background Clear language makes communication easier between any two parties. A layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical terminology which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. Objective Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this paper, we present an automatic method to enrich laymen’s vocabularies that has the benefit of being able to be applied to vocabularies in any domain. Methods Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. Our approach further improves the consumer health vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. The basic GloVe and our novel algorithms incorporating WordNet were evaluated using two laymen datasets from the National Library of Medicine (NLM), Open-Access Consumer Health Vocabulary (OAC CHV) and MedlinePlus Healthcare Vocabulary. Results The results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Furthermore, our enhanced GloVe approach outperformed basic GloVe with an average F-score of 61%, a relative improvement of 25%. Furthermore, the enhanced GloVe showed a statistical significance over the two ground truth datasets with P < 0.001. Conclusions This paper presents an automatic approach to enrich consumer health vocabularies using the GloVe word embeddings and an auxiliary lexical source, WordNet. Our approach was evaluated used healthcare text downloaded from MedHelp.org, a healthcare social media platform using two standard laymen vocabularies, OAC CHV, and MedlinePlus. We used the WordNet ontology to expand the healthcare corpus by including synonyms, hyponyms, and hypernyms for each layman term occurrence in the corpus. Given a seed term selected from a concept in the ontology, we measured our algorithms’ ability to automatically extract synonyms for those terms that appeared in the ground truth concept. We found that enhanced GloVe outperformed GloVe with a relative improvement of 25% in the F-score.

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An Approach for Automatic Ontology Enrichment from Texts

Cited by 9 publications

References 17 publications

Comparative Evaluation of Keyphrase Extraction Tools for Semantic Analysis of Climate Change Scientific Reports and Ontology Enrichment

Comparative Evaluation of Keyphrase Extraction Tools for Semantic Analysis of Climate Change Scientific Reports and Ontology Enrichment

Study on Ontological Knowledge Integration of Micromachining for Collaborative Process

An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource

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