Cat2Type: Wikipedia Category Embeddings for Entity Typing in Knowledge Graphs
Published: 2021 Dezember
Buchtitel: Cat2Type: Wikipedia Category Embeddings for Entity Typing in Knowledge Graphs
Verlag: ACM DL
Organisation: 11th on Knowledge Capture Conference
The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated gen- eration. Entity Typing is the task of assigning or inferring the semantic type of an entity in a KG. This paper introduces an ap- proach named Cat2Type which exploits the Wikipedia Categories to predict the missing entity types in a KG. This work extracts information from Wikipedia Category names and the Wikipedia Category graph which are the sources of rich semantic information about the entities. In Cat2Type, the characteristic features of the entities encapsulated in Wikipedia Category names are exploited using Neural Language Models. On the other hand, a Wikipedia Category graph is constructed to capture the connection between the categories. The Node level representations are learned by opti- mizing the neighbourhood information on the Wikipedia category graph. These representations are then used for entity type predic- tion via classification. The performance of Cat2Type is assessed on two real-world benchmark datasets DBpedia630k and FIGER. The experiments depict that Cat2Type obtained a significant improve- ment over state-of-the-art approaches.
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DOI Link: 10.1145/3460210.3493575