An algorithm for suffix stripping
Readings in information retrieval
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Finding topic words for hierarchical summarization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A clustering algorithm for asymmetrically related data with applications to text mining
Proceedings of the tenth international conference on Information and knowledge management
Information Retrieval
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Ontologies for Knowledge Management: An Information Systems Perspective
Knowledge and Information Systems
Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites
Computational Linguistics
Ontology learning: state of the art and open issues
Information Technology and Management
A Hybrid Approach to Ontology Relationship Learning
NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
NAGA: Searching and Ranking Knowledge
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Application of an ontology-based model to a selected fraudulent disbursement economic crime
AICOL-I/IVR-XXIV'09 Proceedings of the 2009 international conference on AI approaches to the complexity of legal systems: complex systems, the semantic web, ontologies, argumentation, and dialogue
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Ontologies are tools for describing and structuring knowledge, with many applications in searching and analyzing complex knowledge bases. Since building them manually is a costly process, there are various approaches for bootstrapping ontologies automatically through the analysis of appropriate documents. Such an analysis needs to find the concepts and the relationships that should form the ontology. However, since relationship extraction methods are imprecise and cannot homogeneously cover all concepts, the initial set of relationships is usually inconsistent and rather imbalanced - a problem which, to the best of our knowledge, was mostly ignored so far. In this paper, we define the problem of extracting a consistent as well as properly structured ontology from a set of inconsistent and heterogeneous relationships. Moreover, we propose and compare three graph-based methods for solving the ontology extraction problem. We extract relationships from a large-scale data set of more than 325K documents and evaluate our methods against a gold standard ontology comprising more than 12K relationships. Our study shows that an algorithm based on a modified formulation of the dominating set problem outperforms greedy methods.