Triple Patterns Extraction from Unstructured Sentence Using Domain Specific Ontology

Abstract

Triplet extraction algorithms can assist the web query service in order to translate the unstructured input query to ontology understanding query SPARQL. SPARQL query language has a graph based structure and can be built by using the triplets (i.e. subjects, predicates and object). However, end users enter the unstructured sentence (words, statements, etc.) as an input when they wanted to search the required information on the web. So, it is needed to extract the triplets (i.e. subjects, predicates and objects) from the input query to build the ontology browsing query SPARQL. Although there are many triplet extraction algorithms, either they can’t fully define all triple patterns from the incoming query or they are time consuming process. The proposed algorithm presented in this paper can handle this triplet’s incompleteness problem and the aim of this system is to extract the specific triplets from incoming query and to add the necessary information for supporting SPARQL query generating process in a time-saving manner. This algorithm mainly detects the noun form of words from the input query with the help of domain specific ontology instead of using parser and takes the worst case time complexity O(n2) to extract the triplets from the unstructured sentences.