With the continuous growth in web content, the results achieved by traditional search engines by query specific keywords to content has resulted in m
With the continuous growth in web content, the results achieved by traditional search engines by query specific keywords to content has resulted in markedly high recall and low precision. Semantic information retrieval can enhance the relevancy of search results by understanding search intention and the contextual meaning of terms as they are entered by the user.
The goal of semantic search (From Wikipedia)
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.
Rather than using ranking algorithms such as Google’s PageRank to predict relevancy, semantic search uses semantics, or the science of meaning in language, to produce highly relevant search results. In most cases, the goal is to deliver the information queried by a user rather than have a user sort through a list of loosely related keyword results. However, Google itself has subsequently also announced its own Semantic Search project.
The need for Ontology
• Ontology is about the exact description of things and their relationships.
• For the web, ontology is about the exact description of web information and relationships between web information.
• Ontologies are the next emerging generation of database concepts and technology.
OWL stands for Web Ontology Language
• OWL was designed for processing information.
• OWL was designed to provide a common way to process the content of web information (Either by displaying it in diagrams or making it available in the web processes).
• OWL was designed to be read by computer applications (instead of humans).
• OWL contains RDF, but OWL is a stronger language with greater machine interpretability and uses RDF language elements.
• OWL comes with a larger vocabulary than RDF. But RDF, being simpler, is used as a basis for the bazillion sets of data being added to the web from countries and special interest groups all over the world.