Open AccessAssessing Large-Scale, Cross-Domain Knowledge Bases for Semantic Search
Author(s) -
Aatif Ahmad Khan,
Sanjay Kumar Malik
Publication year - 2020
Publication title -
mehran university research journal of engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2413-7219
pISSN - 0254-7821
DOI - 10.22581/muet1982.2003.14
Subject(s) - computer science , rdf , information retrieval , semantic search , semantic web , linked data , set (abstract data type) , domain (mathematical analysis) , process (computing) , semantic technology , semantic data model , semantic analytics , semantic computing , data mining , mathematical analysis , mathematics , programming language , operating system
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.