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Contextual Propagation of Properties for Knowledge Graphs. A Sentence Embedding Based Approach

Abstract : With the ever-increasing number of RDF-based knowledge graphs, the number of interconnections between these graphs using the owl:sameAs property has exploded. Moreover, as several works indicate, the identity as defined by the semantics of owl:sameAs could be too rigid, and this property is therefore often misused. Indeed, identity must be seen as context-dependent. These facts lead to poor quality data when using the owl:sameAs inference capabilities. Therefore, contextual identity could be a possible path to better quality knowledge. Unlike classical identity, with contextual identity, only certain properties can be propagated between contextually identical entities. Continuing this work on contextual identity, we propose an approach, based on sentence embedding, to find semi-automatically a set of properties, for a given identity context, that can be propagated between contextually identical entities. Quantitative experiments against a gold standard show that our approach achieved promising results. Besides, the use cases provided demonstrate that identifying the properties that can be propagated helps users achieve the desired results that meet their needs when querying a knowledge graph, i.e., more complete and accurate answers.
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https://hal-cnam.archives-ouvertes.fr/hal-03818256
Contributor : Ibrahima SOUANE Connect in order to contact the contributor
Submitted on : Monday, October 17, 2022 - 5:14:58 PM
Last modification on : Friday, December 2, 2022 - 3:10:46 AM

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Pierre-Henri Paris, Fayçal Hamdi, Nobal Niraula, Samira Si-Said Cherfi. Contextual Propagation of Properties for Knowledge Graphs. A Sentence Embedding Based Approach. ISWC 2020. 19th International Semantic Web Conference, Nov 2020, Athens, Greece. pp.494-510, ⟨10.1007/978-3-030-62419-4_28⟩. ⟨hal-03818256⟩

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