Computer Science ›› 2019, Vol. 46 ›› Issue (8): 272-276.doi: 10.11896/j.issn.1002-137X.2019.08.045

• Artificial Intelligence • Previous Articles     Next Articles

Natural Language Querying with External Semantic Enrichment

FENG Xue   

  1. (Computer School,Beijing Information Science and Technology University,Beijing 100192,China)
  • Received:2018-09-26 Online:2019-08-15 Published:2019-08-15

Abstract: Semantic Web is one kind of extremely important resources based on Internet technique.Querying on a semantic Web only supports formal languages,which need manipulator to strictly observe certain syntax constraints,and thus only experts that are familiar with semantic Web system and formal language are capable of querying.To overcome this problem,this paper presented an unsupervised natural language querying system,which can convert natural languages into formal languages automatically,thus making common users query on a semantic web using natural languages conveniently.The system first extracts all entities and attributes in a sentence based on a specific semantic Web,then connects them to form a semantic relationship graph,and finally exploits a heuristic strategy to search for an optimum path which is used to produce the output SPARQL expression.The key of the system is the coverage of the entities and attributes from the semantic Web,which directly decides the quality of the inter-mediate semantic relationship graph,and influences the final performance of system.In order to achieve a practical system,this paper enriched a human-annotated semantic Web for a specific domain through using external semantic knowledge,so that the natural language formed languages can contain more information.By this method,better semantic relationship graphs can be obtained and more accurate SPARQL expressions for sentences are achieved.Finally,this paper used the dataset based on American geography for experimental evaluation to verify this system.The dataset is widely acceptable for related research work of natural language querying,which includes manually-annotated SPARQL expressions with 880 questions.The experimental results show that this system can correctly answer 77.6% of the natural queries,outperforming the best unsupervised system in the literature significantly.After knowledge enriching by the external semantic Web,the system reaches 78.5% in term of the correctly-answering accuracy

Key words: Natural language querying, Semantic Web, Unsupervised learning, Formal language, SPARQL

CLC Number: 

  • TP391
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