Abstract
Semantic Search refers to set of approaches dealing with usage of Semantic Web technologies for information retrieval in order to make the process machine understandable and fetch precise results. Knowledge Bases (KB) act as the backbone for semantic search approaches to provide machine interpretable information for query processing and retrieval of results. These KB include Resource Description Framework (RDF) datasets and populated ontologies. In this paper, an assessment of the largest cross-domain KB is presented that are exploited in large scale semantic search and are freely available on Linked Open Data Cloud. Analysis of these datasets is a prerequisite for modeling effective semantic search approaches because of their suitability for particular applications. Only the large scale, cross-domain datasets are considered, which are having sizes more than 10 million RDF triples. Survey of sizes of the datasets in triples count has been depicted along with triples data format(s) supported by them, which is quite significant to develop effective semantic search models.
Highlights
Retrieval of concerned specific information from available repositories based on an input query is called search
Data present in Knowledge Bases (KB) needs to be valid across multiple domains for approaching a true web scale semantic search
This study is essentially a prerequisite to model and develop effective semantic search approaches of global scale, as these KBs are the backbone for deriving knowledge in ways machine can understand
Summary
Retrieval of concerned specific information from available repositories based on an input query is called search. With the availability of web scale knowledge repositories such as DBpedia (KB behind Wikimedia Projects), Google Knowledge Graph etc., approaches are being actively developed to exploit this global range of. Data present in KBs needs to be valid across multiple domains for approaching a true web scale semantic search. This is to make sure that knowledge vocabulary for one domain should not collide with another. This study is essentially a prerequisite to model and develop effective semantic search approaches of global scale, as these KBs are the backbone for deriving knowledge in ways machine can understand.
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