Semantic search seeks to improve search accuracy by understanding searcher intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching and natural language queries to provide relevant search results. Major web search engines like Google and Bing incorporate some elements of semantic search.
Commonly used semantic searching methodologies
1. RDF Path Traversal - traversing the net formed by a graph of information that uses the RDF data model.
2. Keyword to Concept Mapping
3. Graph Patterns - used to formulate patterns for locating interesting connecting paths between resources. Also commonly used in data visualization.
4. Logics - by using inference based on OWL
5. Fuzzy concepts, fuzzy relations, and fuzzy logics