Ontological based Relevance Abstraction Identification Technique and Evaluation

Background/Objectives: To develop ontology based relevance abstraction identification technique for efficient Abstract identification. Methods/Statistical Analysis: The abstract terms are extracted from software related documents such as software requirement specifications, compilation report, bug corpus report, library code documents, testing materials and so on. Abstract identification is the process of analysing and identifying the important key words that are present in the requirements document which is essential to understood requirements for better development process. Findings: The automated abstraction identification was proposed to extract abstract terms called relevance-based abstraction identification (RAI). RAI-0 and RAI-1 two versions of abstraction identification were proposed. In RAI-1 significance score of term is calculated by assigning variable weights for terms based on the likelihood values where as RAI-0 assign equal weight for all terms. The main issues in RAI is used the lexical similarity which has to improved by using work Ontological based relevance Abstraction Identification (O-RAI) with consideration of conceptual meaning words. This work aims to retrieve the abstract terms by finding the conceptual meaning of every terms present in the requirements document. The O-RAI is implemented by constructing the domain ontology in the automated manner by using the methodology called the episode based ontology construction mechanism.An episode is a partially ordered collection of actions taking place together which is represented as directed acyclic graphs. In episode based ontology construction mechanism, concept attributes and relation among attributes are extracted from episodes, the non-taxonomic relations among attribute also formed based on episodes. Improvements/Applications: The significant score of relevant terms from documents is calculated with considering the conceptual of terms which are occurred in the domain ontology. Thus semantic significant score is used to rank the relevance abstract terms.