Selecting Optimal Background Knowledge Sources for the Ontology Matching Task

Tigrine, Abdel Nasser; Bellahsène, Zohra; Todorov, Konstantin

doi:10.1007/978-3-319-49004-5_42

Cited by 7 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, commonly used external knowledge sources, such as WordNet, fail to provide the semantic information that is needed to correctly discover the correspondences between domain specific concepts. More precisely, it has been shown in many publications (Aleksovski et al, 2006;Faria et al, 2013;Tigrine et al, 2015;Tigrine et al, 2016;Annane et al, 2018) that the ontology matching process can take benefit from the use of Background Knowledge. Our alignment method of a given food vocabulary with the FoodOn ontology can be viewed as an ontology matching problem using a background knowledge, encoded here in the LanguaL thesaurus itself included in FoodOn.…”

Section: Why Designing a New Food Sources Alignment Method?mentioning

confidence: 99%

How to Manage Incompleteness of Nutritional Food Sources?

Buche

Cufi

Dervaux

et al. 2021

International Journal of Agricultural and Environmental Information Systems

View full text Add to dashboard Cite

In order to correctly assess the nutritional quality of a raw or manufactured food product, the first step is to obtain the associated nutritional values. Food composition databases (FCDBs) managed at national level provide values for nutrients of foods. Unfortunately, values associated with some nutrients of interest may be lacking in the FCDB of the country in which the nutritional quality must be assessed and finding values associated with nutrients for similar foods in other FCDBs is a way to deal with incompleteness. An additional issue arises because the vocabulary used to denote a given food in a given FCDB is usually different from the one used in others. In this paper, the authors address the problem of retrieving the nutritional value of foods by querying different FCDBs through FoodOn used as pivot ontology. The article presents a new food source alignment method between two FCDBs. The method has been evaluated on the French and United States food nutritional evaluation. The proposed solution for the incompleteness management task has been assessed with a real use case.

show abstract

Section: Why Designing a New Food Sources Alignment Method?mentioning

confidence: 99%

How to Manage Incompleteness of Nutritional Food Sources?

Buche

Cufi

Dervaux

et al. 2021

International Journal of Agricultural and Environmental Information Systems

View full text Add to dashboard Cite

show abstract

“…As there are often multiple potentially beneficial sources of background knowledge available for ontology matching, some authors propose heuristics to determine the benefit of a background knowledge source in order to select one before performing the match operation. Nasser et al [330] define four criteria to automatic background knowledge selection:…”

Section: Background Knowledge Selection In Ontology Matchingmentioning

confidence: 99%

Background knowledge in ontology matching: A survey

Portisch

Hladik

Paulheim

2022

View full text Add to dashboard Cite

Ontology matching is an integral part for establishing semantic interoperability. One of the main challenges within the ontology matching operation is semantic heterogeneity, i.e. modeling differences between the two ontologies that are to be integrated. The semantics within most ontologies or schemas are, however, typically incomplete because they are designed within a certain context which is not explicitly modeled. Therefore, external background knowledge plays a major role in the task of (semi-) automated ontology and schema matching. In this survey, we introduce the reader to the general ontology matching problem. We review the background knowledge sources as well as the approaches applied to make use of external knowledge. Our survey covers all ontology matching systems that have been presented within the years 2004–2021 at a well-known ontology matching competition together with systematically selected publications in the research field. We present a classification system for external background knowledge, concept linking strategies, as well as for background knowledge exploitation approaches. We provide extensive examples and classify all ontology matching systems under review in a resource/strategy matrix obtained by coalescing the two classification systems. Lastly, we outline interesting and yet underexplored research directions of applying external knowledge within the ontology matching process.

show abstract

“…Tigrine et al [34] incorporated the problem into an information retrieval paradigm. Ontologies and BKs are compared in terms of content and structure.…”

Section: Bk Ontology Selectionmentioning

confidence: 99%

Multimatcher Model to Enhance Ontology Matching Using Background Knowledge

et al. 2021

View full text Add to dashboard Cite

Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between the ontologies. Generic BK approaches use a single matcher to discover correspondences between entities from different ontologies. However, the Ontology Alignment Evaluation Initiative (OAEI) results show that not all matchers identify the same correct mappings. Moreover, none of the matchers can obtain good results across all matching tasks. This study proposes a novel BK multimatcher approach for improving ontology matching by effectively generating and combining mappings from biomedical ontologies. Aggregation strategies to create more effective mappings are discussed. Then, a matcher path confidence measure that helps select the most promising paths using the final mapping selection algorithm is proposed. The proposed model performance is tested using the Anatomy and Large Biomed tracks offered by the OAEI 2020. Results show that higher recall levels have been obtained. Moreover, the F-measure values achieved with our model are comparable with those obtained by the state of the art matchers.

show abstract

Selecting Optimal Background Knowledge Sources for the Ontology Matching Task

Cited by 7 publications

References 18 publications

How to Manage Incompleteness of Nutritional Food Sources?

How to Manage Incompleteness of Nutritional Food Sources?

Background knowledge in ontology matching: A survey

Multimatcher Model to Enhance Ontology Matching Using Background Knowledge

Contact Info

Product

Resources

About