Abstract
Users need better ways to explore large complex linked data resources. Using SPARQL requires not only mastering its syntax and semantics but also understanding the RDF data model, the ontology and URIs for entities of interest. Natural language question answering systems solve the problem, but these are still subjects of research. The Schema agnostic SPARQL queries task defined in SAQ-2015 challenge consists of schema-agnostic queries following the syntax of the SPARQL standard, where the syntax and semantics of operators are maintained, while users are free to choose words, phrases and entity names irrespective of the underlying schema or ontology. This combination of query skeleton with keywords helps to remove some of the ambiguity. We describe our framework for handling schema agnostic or schema free queries and discuss enhancements to handle the SAQ-2015 challenge queries. The key contributions are the robust methods that combine statistical association and semantic similarity to map user terms to the most appropriate classes and properties used in the underlying ontology and type inference for user input concepts based on concept linking.
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Cimiano, P., Haase, P., Heizmann, J.: Porting natural language interfaces between domains: an experimental user study with the ORAKEL system. In: Proceedings of 12th International Conference on Intelligent User Interfaces, pp. 180–189. ACM (2007)
Dredze, M., McNamee, P., Rao, D., Gerber, A., Finin, T.: Entity disambiguation for knowledge base population. In: Proceedings of the 23rd International Conference on Computational Linguistics, August 2010
Han, L., Finin, T., Joshi, A.: Schema-free structured querying of DBpedia data. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 2090–2093. ACM (2012)
Han, L., Finin, T., McNamee, P., Joshi, A., Yesha, Y.: Improving word similarity by augmenting pmi with estimates of word polysemy. IEEE Trans. Knowl. Data Eng. IEEE Comput. Soc. 25(6), 1307–1322 (2013)
Harris, Z.S.: Mathematical Structures of Language. Wiley, New York (1968)
Kashyap, A., Han, L., Yus, R., Sleeman, J., Satyapanich, T., Gandhi, S., Finin, T.: Meerkat mafia: multilingual and cross-level semantic textual similarity systems In: Proceedings of the 8th International Workshop on Semantic Evaluation, August 2014
Lin, D., Pantel, P.: Discovery of inference rules for question answering. Natural Lang. Eng. 7(4), 343–360 (2001)
Lin, D.: Automatic retrieval and clustering of similar words. In: Proceedings of the 17th International Conference on Computational Linguistics, pp. 768–774, Montreal (1998)
Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and knowledge-based measures of text semantic similarity. In: Proceedings of the 21st National Conference on Artificial Intelligence, pp. 775–780 (2006)
Milne, D., Witten, I.H.: Learning to link with wikipedia. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 509–518. ACM (2008)
Rapp, R.: Word sense discovery based on sense descriptor dissimilarity. In: Proceedings of the 9th Machine Translation Summit, pp. 315–322 (2003)
Syed, Z., Finin, T.: Creating and exploiting a hybrid knowledge base for linked data. In: Filipe, J., Fred, A., Sharp, B. (eds.) ICAART 2010. CCIS, vol. 129, pp. 3–21. Springer, Heidelberg (2011)
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Syed, Z., Han, L., Rahman, M., Finin, T., Kukla, J., Yun, J. (2015). UMBC_Ebiquity-SFQ: Schema Free Querying System. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds) Semantic Web Evaluation Challenges. SemWebEval 2015. Communications in Computer and Information Science, vol 548. Springer, Cham. https://doi.org/10.1007/978-3-319-25518-7_17
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DOI: https://doi.org/10.1007/978-3-319-25518-7_17
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