ConNeKTion: A Tool for Handling Conceptual Graphs Automatically Extracted from Text

Studying, understanding and exploiting the content of a digital library, and extracting useful information thereof, require automatic techniques that can effectively support the users. To this aim, a relevant role can be played by concept taxonomies. Unfortunately, the availability of such a kind of resources is limited, and their manual building and maintenance are costly and error-prone. This work presents ConNeKTion, a tool for conceptual graph learning and exploitation. It allows to learn conceptual graphs from plain text and to enrich them by finding concept generalizations. The resulting graph can be used for several purposes: finding relationships between concepts (if any), filtering the concepts from a particular perspective, extracting keyword, retrieving information and identifying the author. ConNeKTion provides also a suitable control panel, to comfortably carry out these activities.

[1]  Carlo Meghini,et al.  Digital Libraries and Archives - 7th Italian Research Conference, IRCDL 2011, Pisa, Italy, January 20-21, 2011. Revised Papers , 2012, IRCDL.

[2]  George Karypis,et al.  Concept Indexing: A Fast Dimensionality Reduction Algorithm With Applications to Document Retrieval and Categorization , 2000 .

[3]  Stefano Ferilli,et al.  Improving Robustness and Flexibility of Concept Taxonomy Learning from Text , 2012, NFMCP.

[4]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[5]  Jörg Kindermann,et al.  Authorship Attribution with Support Vector Machines , 2003, Applied Intelligence.

[6]  Brian Davis,et al.  Knowledge Engineering and Knowledge Management , 2012, Lecture Notes in Computer Science.

[7]  Luc De Raedt,et al.  ProbLog: A Probabilistic Prolog and its Application in Link Discovery , 2007, IJCAI.

[8]  Gerard Salton,et al.  The SMART Retrieval System—Experiments in Automatic Document Processing , 1971 .

[9]  Stefano Ferilli,et al.  Plugging Taxonomic Similarity in First-Order Logic Horn Clauses Comparison , 2011, AI*IA.

[10]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[11]  Steffen Staab,et al.  Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis , 2005, J. Artif. Intell. Res..

[12]  Steffen Staab,et al.  The TEXT-TO-ONTO Ontology Learning Environment , 2000 .

[13]  Inderjit S. Dhillon,et al.  Concept Decompositions for Large Sparse Text Data Using Clustering , 2004, Machine Learning.

[14]  Richard W. Hamming,et al.  Error detecting and error correcting codes , 1950 .

[15]  Taisuke Sato,et al.  A Statistical Learning Method for Logic Programs with Distribution Semantics , 1995, ICLP.

[16]  Stefano Ferilli,et al.  A Domain Based Approach to Information Retrieval in Digital Libraries , 2012, IRCDL.

[17]  Dan Klein,et al.  Fast Exact Inference with a Factored Model for Natural Language Parsing , 2002, NIPS.

[18]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[19]  David I. Holmes,et al.  Neural network applications in stylometry: The Federalist Papers , 1996, Comput. Humanit..

[20]  Antony Jay,et al.  Effective Presentation: How to Create and Deliver a Winning Presentation , 1999 .

[21]  Martha Palmer,et al.  Verb Semantics and Lexical Selection , 1994, ACL.

[22]  Norihiro Ogata.  A Formal Ontology Discovery from Web Documents , 2001, Web Intelligence.

[23]  G. Furnas,et al.  Pictures of relevance: a geometric analysis of similarity measures , 1987 .

[24]  Shlomo Argamon,et al.  Style mining of electronic messages for multiple authorship discrimination: first results , 2003, KDD '03.

[25]  Steffen Staab,et al.  Mining Ontologies from Text , 2000, EKAW.

[26]  Rita Cucchiara,et al.  AI*IA 2009: Emergent Perspectives in Artificial Intelligence, XIth International Conference of the Italian Association for Artificial Intelligence, Reggio Emilia, Italy, December 9-12, 2009, Proceedings , 2009, AI*IA.

[27]  Christopher D. Manning,et al.  Generating Typed Dependency Parses from Phrase Structure Parses , 2006, LREC.

[28]  Amit Singhal,et al.  Pivoted document length normalization , 1996, SIGIR 1996.

[29]  Ning Zhong,et al.  Web Intelligence: Research and Development , 2001, Lecture Notes in Computer Science.

[30]  Mitsuru Ishizuka,et al.  Keyword extraction from a single document using word co-occurrence statistical information , 2004, Int. J. Artif. Intell. Tools.

[31]  Stefano Ferilli,et al.  Cooperating Techniques for Extracting Conceptual Taxonomies From Text , 2011 .

[32]  Weiru Liu,et al.  Agwan: A Generative Model for Labelled, Weighted Graphs , 2013, NFMCP.