ABSTRACT
Ontologies have proven to be a powerful tool for many tasks such as natural language processing and information filtering and retrieval. However their development is an error prone and expensive task. One approach for this problem is to provide automatic or semi-automatic support for ontology construction. This work presents the Probabilistic Relational Hierarchy Extraction (PREHE) technique, an approach for extracting concept hierarchies from text that uses statistical relational learning and natural language processing for combining cues from many state-of-the-art techniques. A Markov Logic Network has been developed for this task and is described here. A preliminary evaluation of the proposed approach is also outlined.
- P. Buitelaar, P. Cimiano, and B. Magnini. Ontology learning from text: An overview. Frontiers in Artificial Intelligence and Applications Series, 123, 2005.Google Scholar
- C. Burges. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2):121--167, 1998. Google ScholarDigital Library
- S. Caraballo. Automatic construction of a hypernym-labeled noun hierarchy from text. Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, pages 120--126, 1999. Google ScholarDigital Library
- P. Cimiano, A. Hotho, and S. Staab. Comparing conceptual, divisive and agglomerative clustering for learning taxonomies from text. In Proceedings of the European Conference on Artificial Intelligence (ECAI), pages 435--443, 2004.Google Scholar
- P. Cimiano, A. Hotho, and S. Staab. Learning concept hierarchies from text corpora using formal concept analysis. Technical report, Institute AIFB, Universität Karlsruhe, 2004.Google Scholar
- P. Cimiano and S. Staab. Learning concept hierarchies from text with a guided hierarchical clustering algorithm. In ICML workshop on Learning and Extending Lexical Ontologies with Machine Learning Methods, 2005.Google Scholar
- H. Cunningham. GATE, a general architecture for text engineering. Computers and the Humanities, 36(2):223--254, 2002.Google ScholarCross Ref
- M. de Marneffe, B. MacCartney, and C. Manning. Generating typed dependency parses from phrase structure parses. In LREC 2006, 2006.Google Scholar
- K. Dellschaft and S. Staab. On how to perform a gold standard based evaluation of ontology learning. In Proceedings of ISWC-2006 International Semantic Web Conference, 2006. Google ScholarDigital Library
- L. Drumond and R. Girardi. A survey of ontology learning procedures. In F. L. G. de Freitas, H. Stuckenschmidt, H. S. Pinto, A. Malucelli, and Óscar Corcho, editors, WONTO, volume 427 of CEUR Workshop Proceedings. CEUR-WS.org, 2008.Google Scholar
- L. Drumond and R. Girardi. An experiment using markov logic networks to extract ontology concepts from text. In Proceedings of the II Workshop on Web and Text Intelligence (STIL-WTI 2009), São Carlos, Brazil, 2009.Google Scholar
- C. Fellbaum. Wordnet: An Electronic Lexical Database. MIT Press, 1998.Google ScholarCross Ref
- W. Gilks, S. Richardson, and D. Spiegelhalter. Markov chain Monte Carlo in practice. Chapman & Hall/CRC, 1996.Google Scholar
- R. Girardi and B. Ibrahim. Using english to retrieve software. Journal of Systems Software, Special Issue on Software Reusability, 30(3):249--270, 1995. Google ScholarDigital Library
- Z. Harris. Mathematical Structures of Language. Wiley, 1968.Google Scholar
- M. Hearst. Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th International Conference on Computational Linguistics, pages 539--545, 1992. Google ScholarDigital Library
- H. Kautz, B. Selman, and Y. Jiang. A general stochastic approach to solving problems with hard and soft constraints. The Satisfiability Problem: Theory and Applications, pages 573--586, 1997.Google ScholarCross Ref
- M. Kavalec and V. Svatek. A study on automated relation labelling in ontology learning. Ontology Learning from Text: Methods, Evaluation and Applications, pages 44--58, 2005.Google Scholar
- S. Kok and P. Domingos. Statistical predicate invention. In Proceedings of the 24th international conference on Machine learning, pages 433--440. ACM New York, NY, USA, 2007. Google ScholarDigital Library
- S. Kok, M. Richardson, P. Singla, H. Poon, D. Lowd, J. Wang, and P. Domingos. The alchemy system for statistical relational ai. Technical report, Department of Computer Science and Engineering, University of Washington, Seattle, WA, 2009.Google Scholar
- A. Maedche and S. Staab. Ontology learning, pages 173--190. Springer, 2004.Google Scholar
- S. Middleton, N. Shadbolt, and D. D. Roure. Ontological user profiling in recommender systems. ACM Transactions on Information Systems, 22:54--88, 2004. Google ScholarDigital Library
- J. Neville, M. Rattigan, and D. Jensen. Statistical relational learning: Four claims and a survey. In Workshop SRL, Int. Joint. Conf. on AI, 2003.Google Scholar
- T. Ohta, Y. Tateisi, and J. Kim. The GENIA corpus: An annotated research abstract corpus in molecular biology domain. In Proceedings of the second international conference on Human Language Technology Research, pages 82--86. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA, 2002. Google ScholarDigital Library
- H. Poon and P. Domingos. Sound and efficient inference with probabilistic and deterministic dependencies. In PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, volume 21, page 458. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 2006. Google ScholarDigital Library
- A. Popescul and L. Ungar. Statistical relational learning for link prediction. In IJCAI workshop on learning statistical models from relational data, 2003.Google ScholarDigital Library
- M. Richardson and P. Domingos. Markov logic networks. Machine Learning, 62(1):107--136, 2006. Google ScholarDigital Library
- G. Salton and C. Buckley. Term Weighting Approaches in Automatic Text Retrieval. 1987.Google Scholar
- N. Shadbolt, W. Hall, and T. Berners-Lee. The semantic web revisited. Intelligent Syst, 21(3):96--101, 2006. Google ScholarDigital Library
- F. Silva, R. Girardi, and L. Drumond. A knowledge-based retrieval model. In Proceedings of the 21st International Conference on Software Engineering & Knowledge Engineering (SEKE'2009), pages 558--563, Boston, USA, 2009. Knowledge Systems Institute Graduate School.Google Scholar
- R. Snow, D. Jurafsky, and A. Ng. Learning syntactic patterns for automatic hypernym discovery. Advances in Neural Information Processing Systems, 17:1297--1304, 2005.Google ScholarDigital Library
- F. Wu and D. S. Weld. Automatically refining the wikipedia infobox ontology. In WWW '08: Proceeding of the 17th international conference on World Wide Web, pages 635--644, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
Index Terms
- Extracting ontology concept hierarchies from text using Markov logic
Recommendations
Ontology augmentation: combining semantic web and text resources
K-CAP '11: Proceedings of the sixth international conference on Knowledge captureThis work investigates the process of selecting, extracting and reorganizing content from Semantic Web information sources, to produce an ontology meeting the specifications of a particular domain and/or task. The process is combined with traditional ...
Infrastructure for dynamic knowledge integration-Automated biomedical ontology extension using textual resources
We present a novel ontology integration technique that explicitly takes the dynamics and data-intensiveness of e-health and biomedicine application domains into account. Changing and growing knowledge, possibly contained in unstructured natural language ...
Formal semantics-preserving translation from fuzzy ER model to fuzzy OWL DL ontology
Ontology is an important part of the W3C standards for the Semantic Web, and how to quickly and cheaply construct Web ontologies has become a key technology to enable the Semantic Web. However, information imprecision and uncertainty exist in many real-...
Comments