Semantic Extension for the Linked Data Based on Semantically Enhanced Annotation and Reasoning

Autor: Gu Peipei, Pu Li, Wu Fenglong, Ma Junxia, Yuncheng Jiang, Lujuan Deng, Zhifeng Zhang
Rok vydání: 2019
Předmět:
Popis: Linked Data, a new form of knowledge representation and publishing described by RDF, can provide more precise and comprehensible semantic structures. However, the current RDF Schema (RDFS) and SPARQL-based query strategy cannot fully express the semantics of RDF since they cannot unleash the implicit semantics between linked entities, so they cannot unleash the potential of Linked Data. To fill this gap, this chapter first defines a new semantic annotating and reasoning method which can extend more implicit semantics from different properties and proposes a novel general Semantically-Extended Scheme for Linked Data Sources to realize the semantic extension over the target Linked Data source. Moreover, in order to effectively return more information in the process of semantic data retrieval, we then design a new querying model which extends the SPARQL pattern. Lastly, experimental results show that our proposal has advantages over the initial Linked Data source and can return more valid results than some of the most representative similarity search methods.
Databáze: OpenAIRE