Ontology Maintenance Through Semantic Text Mining: An Application for IT Governance Domain

Manual ontology population and enrichment is a complex task that require professional experience involving a lot of efforts. The authors’ paper deals with the challenges and possible solutions for semi-automatic ontology enrichment and population. ProMine has two main contributions; one is the semantic-based text mining approach for automatically identifying domain-specific knowledge elements; the other is the automatic categorization of these extracted knowledge elements by using Wiktionary. ProMine ontology enrichment solution was applied in IT audit domain of an e-learning system. After seven cycles of the application ProMine, the number of automatically identified new concepts are significantly increased and ProMine categorized new concepts with high precision and recall.

[1]  Qiang Wang,et al.  Ontology Learning Using Word Net Lexical Expansion and Text Mining , 2012 .

[2]  Marek Hatala,et al.  An Approach to Folksonomy-Based Ontology Maintenance for Learning Environments , 2011, IEEE Transactions on Learning Technologies.

[3]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[4]  Nasser Ghasem-Aghaee,et al.  Text feature selection using ant colony optimization , 2009, Expert Syst. Appl..

[5]  Paul Mueller,et al.  Comparing Ontology Development Tools Based on an Online Survey , 2010 .

[6]  Gerhard Weikum,et al.  YAGO: A Large Ontology from Wikipedia and WordNet , 2008, J. Web Semant..

[7]  András Gábor,et al.  Agile Knowledge-Based E-Government Supported By Sake System , 2011, J. Cases Inf. Technol..

[8]  Asunción Gómez-Pérez,et al.  Ontology's Crossed Life Cycles , 2000, EKAW.

[9]  Marek Hatala,et al.  Towards open ontology learning and filtering , 2011, Inf. Syst..

[10]  Yau-Hwang Kuo,et al.  Automated ontology construction for unstructured text documents , 2007, Data & Knowledge Engineering.

[11]  András Gábor,et al.  Corporate Knowledge Discovery and Organizational Learning , 2016 .

[12]  Yücel Saygin,et al.  Ontology Supported Policy Modeling in Opinion Mining Process , 2012, OTM Workshops.

[13]  Mehrnoush Shamsfard,et al.  The state of the art in ontology learning: a framework for comparison , 2003, The Knowledge Engineering Review.

[14]  Andrea Ko,et al.  Incremental Ontology Population and Enrichment through Semantic-based Text Mining: An Application for IT Audit Domain , 2015, Int. J. Semantic Web Inf. Syst..

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

[16]  Krys J. Kochut,et al.  Wikipedia in Action: Ontological Knowledge in Text Categorization , 2008, 2008 IEEE International Conference on Semantic Computing.

[17]  Min Song,et al.  KPSpotter: a flexible information gain-based keyphrase extraction system , 2003, WIDM '03.

[18]  Gary Geunbae Lee,et al.  Information gain and divergence-based feature selection for machine learning-based text categorization , 2006, Inf. Process. Manag..

[19]  Amrit Tiwana,et al.  Special Issue: Information Technology and Organizational Governance: The IT Governance Cube , 2013, J. Manag. Inf. Syst..

[20]  Andrea Kő,et al.  ProMine: A Text Mining Solution for Concept Extraction and Filtering , 2016 .

[21]  Alfonso Valencia,et al.  Automatic ontology construction from the literature. , 2002, Genome informatics. International Conference on Genome Informatics.

[22]  Simone Paolo Ponzetto,et al.  Deriving a Large-Scale Taxonomy from Wikipedia , 2007, AAAI.

[23]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[24]  Don-Lin Yang,et al.  A semi-automatic approach to construct Vietnamese ontology from online text , 2012 .

[25]  Mohammed Bennamoun,et al.  Ontology learning from text: A look back and into the future , 2012, CSUR.

[26]  Ahmed A. Rafea,et al.  TextOntoEx: Automatic ontology construction from natural English text , 2008, Expert Syst. Appl..