期刊
SEMANTIC WEB: LATEST ADVANCES AND NEW DOMAINS
卷 9678, 期 -, 页码 85-100出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-34129-3_6
关键词
Type inference; Datatype properties; RDF triples; Feature grouping and ranking; Entity summarization; Dataset enrichment
Associating meaning with data in a machine-readable format is at the core of the Semantic Web vision, and typing is one such process. Typing (assigning a class selected from schema) information can be attached to URI resources in RDF/S knowledge graphs and datasets to improve quality, reliability, and analysis. There are two types of properties: object properties, and datatype properties. Type information can be made available for object properties as their object values are URIs. Typed object properties allow richer semantic analysis compared to datatype properties, whose object values are literals. In fact, many datatype properties can be analyzed to suggest types selected from a schema similar to object properties, enabling their wider use in applications. In this paper, we propose an approach to glean types for datatype properties by processing their object values. We show the usefulness of generated types by utilizing them to group facts on the basis of their semantics in computing diversified entity summaries by extending a state-of-the-art summarization algorithm.
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