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Abstract ▼ AI-Helper

One of the critical problems in natural language interfaces (NLI) to structured data is to handle the large variability in the way that a relation can be expressed. Existing approaches to solve this problem rely on manual process to adapt NLI to a new domain. The manual approaches can be efficiently...

One of the critical problems in natural language interfaces (NLI) to structured data is to handle the large variability in the way that a relation can be expressed. Existing approaches to solve this problem rely on manual process to adapt NLI to a new domain. The manual approaches can be efficiently applied to small structured data. However, nowadays, large volumes of graph-structured data such as RDF and OWL have been produced from a variety of areas on the web. It is not easy to adapt NLI to such large graph structured data. As a solution of the variability problem in NLI, this paper proposes an automatic method to identify relations in natural language queries for NLI to graph structured data. A relation between two entities is uniquely defined as an edge linking the entities on graph-structured data. The proposed method automatically discovers natural language expressions for given relations by querying entity pairs linked with the relations from a corpus. A relation corresponding to an edge is modeled as a collection of frequently appeared expressions. In the experiment, automatically collected expressions are applied to a NLI and the NLI showed comparable results with manually approaches.

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