A Multi-strategy Approach for Lexicalizing Linked Open Data

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• 作者：Rivindu Perera (14)
  Parma Nand (14)
  
  14. School of Computer and Mathematical Sciences ; Auckland University of Technology ; Auckland ; 1010 ; New Zealand
  
• 关键词：Lexicalization ; Linked Data ; DBpedia ; Natural Language Generation
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9042
• 期：1
• 页码：348-363
• 全文大小：629 KB
• 参考文献：1. Mendes, P.N., Jakob, M., Bizer, C.: DBpedia for NLP: A Multilingual Cross-domain Knowledge Base. In: International Conference on Language Resources and Evaluation, Istanbul, LREC (2012)
  2. Kipper, K., Dang, H.T., Palmer, M.: Class-Based Construction of a Verb Lexicon. In: Seventeenth National Conference on Artificial Intelligence, pp. 691鈥?96. AAAI Press, Austin (2000)
  3. Miller, G.A. (1995) WordNet: A Lexical Database for English. Communications of the ACM 38: pp. 39-41 CrossRef
  4. Perera, R., Nand, P.: Real text-cs - corpus based domain independent content selection model. In: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 599鈥?06 (November 2014)
  5. Perera, R., Nand, P.: The role of linked data in content selection. In: The 13th Pacific Rim International Conference on Artificial Intelligence, pp. 573鈥?86 (December 2014)
  6. Manning, C., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP Natural Language Processing Toolkit. In: The 52nd Annual Meeting of the Association for Computational Linguistics, ACL (2014)
  7. Brin, S.: Extracting Patterns and Relations from the World Wide Web. In: International Workshop on the World Wide Web and Databases, pp. 172鈥?83. Springer-Verlag, Valencia (1998)
  8. Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. In: Empirical Methods in Natural Language Processing, EMNLP 2002, vol. 10, pp. 71鈥?8. Association for Computational Linguistics, Morristown (2002)
  9. Kambhatla, N.: Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations. In: ACL 2004 on Interactive Poster and Demonstration Sessions. ACL, Morristown (2004)
  10. Etzioni, O., Banko, M., Soderland, S., Weld, D.S. (2008) Open information extraction from the web. Communications of the ACM 51: pp. 68 CrossRef
  11. Mausam, S.M., Bart, R., Soderland, S., Etzioni, O.: Open language learning for information extraction. In: Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 523鈥?34. ACL, Jeju Island (2012)
  12. Duma, D., Klein, E.: Generating Natural Language from Linked Data: Unsupervised template extraction. In: 10th International Conference on Computational Semantics, IWCS 2013. ACL, Potsdam (2013)
  13. Walter, S., Unger, C., Cimiano, P.: A Corpus-Based Approach for the Induction of Ontology Lexica. In: 18th International Conference on Applications of Natural Language to Information Systems, pp. 102鈥?13. Springer-Verlag, Salford (2013)
  14. Gerber, D., Ngomo, A.C.N.: Bootstrapping the Linked Data Web. In: The 10th International Semantic Web Conference. Springer-Verlag, Bonn (2011)
  15. Ell, B., Harth, A.: A language-independent method for the extraction of RDF verbalization templates. In: 8th International Natural Language Generation Conference. ACL, Philadelphia (2014)
  
• 作者单位：Computational Linguistics and Intelligent Text Processing
• 丛书名：978-3-319-18116-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

This paper aims at exploiting Linked Data for generating natural text, often referred to as lexicalization. We propose a framework that can generate patterns which can be used to lexicalize Linked Data triples. Linked Data is structured knowledge organized in the form of triples consisting of a subject, a predicate and an object. We use DBpedia as the Linked Data source which is not only free but is currently the fastest growing data source organized as Linked Data. The proposed framework utilizes the Open Information Extraction (OpenIE) to extract relations from natural text and these relations are then aligned with triples to identify lexicalization patterns. We also exploit lexical semantic resources which encode knowledge on lexical, semantic and syntactic information about entities. Our framework uses VerbNet and WordNet as semantic resources. The extracted patterns are ranked and categorized based on the DBpedia ontology class hierarchy. The pattern collection is then sorted based on the score assigned and stored in an index embedded database for use in the framework as well as for future lexical resource. The framework was evaluated for syntactic accuracy and validity by measuring the Mean Reciprocal Rank (MRR) of the first correct pattern. The results indicated that framework can achieve 70.36% accuracy and a MRR value of 0.72 for five DBpedia ontology classes generating 101 accurate lexicalization patterns.