Ontology Learning and Its Application in Software-Intensive Projects

Software artifacts, such as requirements, design, source code, documentation, and safety-related artifacts are typically expressed using domain-specific terminology. Automated tools which attempt to analyze software artifacts in order to perform tasks such as trace retrieval and maintenance, domain analysis, program comprehension, or to service natural language queries, need to understand the vocabulary and concepts of the domain in order to achieve acceptable levels of accuracy. Domain concepts can be captured and stored as an ontology. Unfortunately, constructing ontologies is extremely time-consuming and has proven hard to automate. This dissertation proposes a novel approach for semi-automated ontology building that leverages user-defined trace links to identify candidate domain facts. It uses a variety of web-mining, Natural Language Processing, and machine learning techniques to filter and rank the candidate facts, and to assist the user in building a domain-specific ontology. The benefits of the constructed ontology are described and evaluated within the context of automated trace link creation.

[1]  Chang Wang,et al.  Relation extraction and scoring in DeepQA , 2012, IBM J. Res. Dev..

[2]  William W. Cohen,et al.  Grounded Discovery of Coordinate Term Relationships between Software Entities , 2015, ArXiv.

[3]  Jane Cleland-Huang,et al.  Towards an intelligent domain-specific traceability solution , 2014, ASE.

[4]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[5]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[6]  Marti A. Hearst.  Automatic Acquisition of Hyponyms from Large Text Corpora , 1992, COLING.

[7]  Peter Sawyer,et al.  Ensemble Methods for Ontology Learning - An Empirical Experiment to Evaluate Combinations of Concept Acquisition Techniques , 2008, Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008).

[8]  Peter Sawyer,et al.  A Flexible Framework To Experiment With Ontology Learning Techniques , 2007, SGAI Conf..

[9]  Maria Ruiz-Casado,et al.  Automatising the learning of lexical patterns: An application to the enrichment of WordNet by extracting semantic relationships from Wikipedia , 2007, Data Knowl. Eng..

[10]  Haixun Wang,et al.  Probase: a probabilistic taxonomy for text understanding , 2012, SIGMOD Conference.

[11]  Estevam R. Hruschka,et al.  Toward an Architecture for Never-Ending Language Learning , 2010, AAAI.

[12]  John Mylopoulos,et al.  A core ontology for requirements , 2009, Appl. Ontology.

[13]  Stéphane Faulkner,et al.  Towards conceptual foundations for service-oriented requirements engineering: bridging requirements and services ontologies , 2012, IET Softw..

[14]  Juan Llorens Morillo,et al.  Towards an ontology-based retrieval of UML Class Diagrams , 2012, Inf. Softw. Technol..

[15]  Jane Cleland-Huang,et al.  Foundations for an expert system in domain-specific traceability , 2013, 2013 21st IEEE International Requirements Engineering Conference (RE).

[16]  Jane Huffman Hayes,et al.  Advancing candidate link generation for requirements tracing: the study of methods , 2006, IEEE Transactions on Software Engineering.

[17]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..