Semantic search can be described as the effort to improve the accuracy of the search process by understanding the context and limiting the ambiguity. Semantic search engines are more likely to try to understand the meanings that are hidden in retrieved documents and users’ queries, by means of adding semantic tags into texts, in order to bring structure into and conceptualise the objects within documents. The primary components of the semantic web, ontologies and knowledge graphs (populated ontologies), are rich sources of domain knowledge. Semantic search systems leverage this domain knowledge to capture query intent and improve the accuracy of the search. For example, for a query such as Rosalind Franklin, Google * returns specific details such as her date of birth, education, and scientific discoveries, (refer image). In addition, the query also yields details about entities that are related to the search request, such as other scientist in the area of biology and chemistry of the same era.

Our research addresses the key aspects of semantic search: (1) creation of the knowledge graphs and (2) efficient retrieval of concepts acrossdistributed and interlinked knowledge graphs. We propose efficient reuse of structured and unstructured resources to build knowledge graphs. The manual method of populating ontologies with instances to build knowledge graphs is laborious. We leverage bootstrapping techniques to reuse unstructured resources for semiautomatic ontology population. In addition, instances were extracted from the structured resources on the linked open data. Our semi-automatic ontology population methodologies have the potential to reduce the time and effort required to build the knowledge graph.

The second key aspect of semantic search is retrieving concepts from a corpus of ontologies. Identifying the right concepts is a critical part of search-improvement techniques such as web page annotation, query intent capture, and web page classification. The scale, duplication and ambiguity makes concept search across ontologies a challenging problem. We propose keyword- based concept search frameworks that balances the relevance and diversity of concept search results.