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- W4205978409 abstract "Entity alignment intends to find equivalence relations between entities that exist in different knowledge graphs but semantically represent the same real-world object. Despite great progress has been made in entity alignment, concept - an indispensable piece in the meaning triangle (real-world object, symbolic entity, and concept) is inadequately addressed by the current approaches. In this study, we adopt the three-way distinction (so called meaning triangle []) in knowledge representation as the semantic foundation for entity alignment, and present a framework called C4EA (Concept for Entity Alignment) to integrate and unify concept and entity into entity alignment. C4EA uses a graph neural network and knowledge representation model to encode entity and entity relationship in a low-dimensional vector, and uses Box Embedding to represent concept as a hyper-rectangle in the vector space. We treat entity as a hyper-rectangle with a range of zero (a point in space), so that entities which are challenging to align through finite equivalent entity links can be drawn closer to each other in the representation space. Our experimental results on three public multilingual entity alignment datasets demonstrate the effectiveness of the proposed solution." @default.
- W4205978409 created "2022-01-26" @default.
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- W4205978409 date "2021-01-01" @default.
- W4205978409 modified "2023-10-18" @default.
- W4205978409 title "Encoding the Meaning Triangle (Object, Entity, and Concept) as the Semantic Foundation for Entity Alignment" @default.
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- W4205978409 doi "https://doi.org/10.1007/978-3-030-90888-1_19" @default.
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