Total views : 204

Ontological based Relevance Abstraction Identification Technique and Evaluation


  • Department of Computer Science, Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore – 641020, Tamil Nadu, India


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.


Abstract Identification, Likelihood Values, Ontological based Relevant Abstraction Identification, Relevance-based Abstraction Identification.

Full Text:

 |  (PDF views: 186)


  • Zimmermann T, Premraj R, Zeller A. Predicting defects for eclipse. In PROMISE’07: ICSE International Workshop on Predictor Models in Software Engineering. 2007; 9–9.
  • Louvan S. Extracting the Main Content from Web Documents. 2009; 303:217–36.
  • Kim S, Whitehead Jr EJ, Zhang Y. Classifying software changes: Clean or buggy? IEEE Transactions on Software Engineering. 2008; 34(2):181–96.
  • Hersh WR, Cohen AM, Roberts PM, Rekapalli HK. TREC 2006 genomics track overview. In TREC. 2006.
  • Kim S, Whitehead Jr EJ, Zhang Y. Classifying software changes: Clean or buggy? IEEE Transactions on Software Engineering. 2008; 34(2):181–96.
  • Kim S, Zimmermann T, Whitehead Jr EJ, Zeller A. Predicting faults from cached history. In Proceedings of the 29th International Conference on Software Engineering. 2007. p. 489–98.
  • De Lucia A, Di Penta M, Oliveto R. Improving source code lexicon via traceability and information retrieval. IEEE Transactions on Software Engineering. 2011; 37(2):205–27.
  • Marcus A, Maletic JI. Recovering documentation-to-source-code traceability links using latent semantic indexing. In Proceedings 25th International Conference on Software Engineering. 2003. p. 125–35.
  • Basili VR, Briand LC, Melo WL. A validation of object-oriented design metrics as quality indicators. IEEE Transactions on Software Engineering. 1996; 22(10):751–61.
  • Gyimothy T, Ferenc R, Siket I. Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Transactions on Software Engineering. 2005; 31(10):897–910.
  • Takang AA, Grubb PA, Macredie RD. The effects of comments and identifier names on program comprehensibility: an experimental investigation. J Prog Lang. 1996; 4(3):143–67.
  • Lee CS, Kao YF, Kuo YH, Wang MH. Automated ontology construction for unstructured text documents. Data and Knowledge Engineering. 2007; 60(3):547–66.
  • Gacitua R, Sawyer P, Gervasi V. Relevance-based abstraction identification: technique and evaluation. Requirements Engineering. 2011; 16(3):251–65.
  • Yesudoss J, Ramani AV. A survey on abstraction identification techniques in requirement engineering. Karpagam Journal of Computer Science. 2014; 8(3):141–50.
  • Abdul Razak SH, Darleena Eri Z, Abdullah R, Azmi Murad MA. Ontological Model of Virtual Community of Practice (VCoP) Participation: a Case of Research Group Community in Higher Learning Institution. Indian Journal of Science and Technology. 2013; 6(10). Doi:10.17485/ijst/2013/v6i10/38781.
  • Mohankumar P, Vaideeswaran J. Assessment on precision-imprecision essentials in Semantic query processing. Indian Journal of Science and Technology. 2015; 8(13). Doi: 10.17485/ijst/2015/v8i13/55330.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.