A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia
This work addresses the gap between natural language queries and structured knowledge bases for users needing accurate answers from DBpedia, representing an incremental improvement in query understanding and ranking.
The paper tackles the problem of answering non-aggregation natural language questions over DBpedia by developing a graph traversal method that jointly addresses semantic mapping and disambiguation, achieving best performance compared to state-of-the-art systems on evaluated datasets.
We present a question answering system over DBpedia, filling the gap between user information needs expressed in natural language and a structured query interface expressed in SPARQL over the underlying knowledge base (KB). Given the KB, our goal is to comprehend a natural language query and provide corresponding accurate answers. Focusing on solving the non-aggregation questions, in this paper, we construct a subgraph of the knowledge base from the detected entities and propose a graph traversal method to solve both the semantic item mapping problem and the disambiguation problem in a joint way. Compared with existing work, we simplify the process of query intention understanding and pay more attention to the answer path ranking. We evaluate our method on a non-aggregation question dataset and further on a complete dataset. Experimental results show that our method achieves best performance compared with several state-of-the-art systems.