OECM: A Cross-Lingual Approach for Ontology Enrichment

Ibrahim, Shimaa; Fathalla, Said; Yazdi, Hamed Shariat; Lehmann, Jens; Jabeen, Hajira

doi:10.1007/978-3-030-32327-1_20

Cited by 4 publications

(5 citation statements)

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“…We propose a fully automated ontology enrichment approach in order to create multilingual ontologies from monolingual ones using cross-lingual matching. We extend our previous work [14] by: (1) using the semantic similarity to select the best translation of class labels, (2) enriching the target ontology by adding new classes in addition to all their related subclasses in the hierarchy, (3) using ontologies in non-Indo-European languages (e.g., Arabic), as the source of information, (4) building multilingual ontologies, and (5) developing a fully automated approach. OECM comprises six phases: (1) translation: translate class labels of the source ontology, (2) pre-processing: process class labels of the target and the translated source ontologies, (3) terminological matching: identify potential matches between class labels of the source and the target ontologies, (4) triple retrieval : retrieve the new information to be added to the target ontology, (5) enrichment: enrich the target ontology with new information extracted from the source ontology, and (6) validation: validate the enriched ontology.…”

supporting

confidence: 70%

“…In our experiments, we consider English ontologies as target ontologies to be enriched from German and Arabic ontologies. Our evaluation has three tasks: (1) evaluating the effectiveness of the crosslingual matching process in OECM compared to the reference alignment provided in the MultiFarm benchmark, (2) comparing OECM matching results with four state-of-the-art approaches, in addition to our previous work (OECM 1.0) [14], and (3) evaluating the quality of the enrichment process.…”

Section: Discussionmentioning

confidence: 99%

“…Most of the existing work in ontology enrichment focus on enriching English ontologies from English sources only (monolingual enrichment) [23]. To the best of our knowledge, only our previous work [1,14] has addressed the crosslingual ontology enrichment problem by proposing a semi-automated approach to enrich ontologies from multilingual text or from other ontologies in different natural languages.…”

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confidence: 99%

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From Monolingual to Multilingual Ontologies: The Role of Cross-Lingual Ontology Enrichment

Ibrahim

Fathalla

Yazdi

et al. 2019

Lecture Notes in Computer Science

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While the multilingual data on the Semantic Web grows rapidly, the building of multilingual ontologies from monolingual ones is still cumbersome and hampered due to the lack of techniques for crosslingual ontology enrichment. Cross-lingual ontology enrichment greatly facilitates the semantic interoperability between different ontologies in different natural languages. Achieving such enrichment by human labor is a time-consuming and error-prone task. Thus, in this paper, we propose a fully automated ontology enrichment approach using cross-lingual matching (OECM) approach, which builds a multilingual ontology by enriching a monolingual ontology from another one in a different natural language using a cross-lingual matching. OECM selects the best translation among all available translations of ontology concepts based on their semantic similarity with the target ontology concepts. We present a use case of our approach for enriching English Scholarly Communication Ontologies using German and Arabic ontologies from the Multi-Farm benchmark. We have compared our results with the results from the Ontology Alignment Evaluation Initiative (OAEI 2018). Our approach has higher precision and recall in comparison to five state-of-theart approaches. Additionally, we recommend some linguistic corrections in the Arabic ontologies in Multifarm which have enhanced our crosslingual matching results.

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confidence: 70%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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From Monolingual to Multilingual Ontologies: The Role of Cross-Lingual Ontology Enrichment

Ibrahim

Fathalla

Yazdi

et al. 2019

Lecture Notes in Computer Science

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“…XMap [21] didn't achieve satisfactory results due to many internal exceptions. Surprisingly, we found seven new alignments, which didn't exist within the gold standard, when matching Conferencede with Ekawen, [25].…”

Section: Literature Surveymentioning

confidence: 92%

Cross Lingual Ontology Matching Based on Fuzzy Syntactic Matching

Shrivastava

Mathur

Joshi

2020

ijarcs

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Ontologies bolsters data disclosure, sharing and reuse among people and enable semantic interoperability between PC based structures. To develop correspondences between data thoughts addressed in Ontologies. Once in a while the correspondence between the client and PC is in various language, which is extremely hard to comprehend for both. Ontology matching is at the center of overseeing Cross Lingual on the semantic web. In this paper, we present a way to deal with take care of the issue of multilingualism on the semantic web, in view of Syntactic matching . To determine linguistic issue, two Ontologies (one in English and one in Hindi) of same space, interior portrayal, number of matching algorithm dependent on Syntactic method (Edit distance (Levenshteindistance LD)), and Machine Translator.

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“…Ali et al [32] proposed a multi-agent architecturebased cross-lingual ontology enrichment approach to enrich ontologies from multilingual text or ontologies. Ibrahim et al [33], [34] proposed a fully automated ontology enrichment approach based on cross-lingual matching that creates a multilingual ontology by enriching a monolingual one from another in a different language. They used lexical similarity (Jaccard) and semantic similarity (based on WordNet) to filter the equivalent classed.…”

Section: A Cross-lingual Matching Approachesmentioning

confidence: 99%

Toward the Multilingual Semantic Web: Multilingual Ontology Matching and Assessment

et al. 2023

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The amount of multilingual data on the Web proliferates; therefore, developing ontologies in various natural languages is attracting considerable attention. In order to achieve semantic interoperability for the multilingual Web, cross-lingual ontology matching techniques are highly required. This paper proposes a Multilingual Ontology Matching (MoMatch) approach for matching ontologies in different natural languages. MoMatch uses machine translation and various string similarity techniques to identify correspondences across different ontologies. Furthermore, we propose a Quality Assessment Suite for Ontologies (QASO) that comprises 14 metrics, out of which seven metrics are used to assess the quality of the matching process and seven metrics are used to evaluate the quality of the ontology. We present an indepth comparison of different string similarity techniques across various languages to get the most effective similarity measure(s) between multilingual terms. To illustrate the applicability of our approach and how it can be used in different domains, we present two use cases. MoMatch has been implemented using Scala and Apache Spark under an open-source license. We have compared our results with the results from the Ontology Alignment Evaluation Initiative (OAEI 2020). MoMatch has achieved significantly high precision, recall, and F-measure compared to five state-of-the-art matching approaches.

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OECM: A Cross-Lingual Approach for Ontology Enrichment

Cited by 4 publications

References 5 publications

From Monolingual to Multilingual Ontologies: The Role of Cross-Lingual Ontology Enrichment

From Monolingual to Multilingual Ontologies: The Role of Cross-Lingual Ontology Enrichment

Cross Lingual Ontology Matching Based on Fuzzy Syntactic Matching

Toward the Multilingual Semantic Web: Multilingual Ontology Matching and Assessment

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