Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
Pythia: compositional meaning construction for ontology-based question answering on the semantic web
NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
Template-based question answering over RDF data
Proceedings of the 21st international conference on World Wide Web
Named entity recognition and disambiguation using linked data and graph-based centrality scoring
SWIM '12 Proceedings of the 4th International Workshop on Semantic Web Information Management
Natural language questions for the web of data
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
PATTY: a taxonomy of relational patterns with semantic types
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Question answering is the task of answering questions in natural language. Linked Data project and Semantic Web community made it possible for us to query structured knowledge bases like DBpedia and YAGO. Only expert users, however, with the knowledge of RDF and ontology definitions can build correct SPARQL queries for querying knowledge bases formally. In this paper, we present a method for mapping natural language questions to ontology-based structured queries to retrieve direct answers from open knowledge bases (linked data). Our tool is based on translating natural language questions into RDF triple patterns using the dependency tree of the question text. In addition, our method uses relational patterns extracted from the Web. We tested our tool using questions from QALD-2, Question Answering over Linked Data challenge track and found promising preliminary results.