TY - GEN
T1 - Towards Incomplete SPARQL Query in RDF Question Answering - A Semantic Completion Approach
AU - Pang, Jinhui
AU - Jiao, Jie
AU - Ji, Guangxi
AU - Wu, Yunjie
AU - Zhang, Ding
AU - Wang, Shujun
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/4/20
Y1 - 2020/4/20
N2 - RDF question/answering(Q/A) system allows users to ask questions in natural language on a knowledge base represented by RDF and retrieve answers. A common problem in RDF Q/A is that existing works tend to translate a natural language question into an incomplete SPARQL query, which means that SPARQL queries may not fully understand user's ideas. For example, some triple patterns may be missing in the question translation stage. In this poster, we first present a siamese adaptation of the Long Short-Term Memory(LSTM) network to detect whether the SPARQL query generated by the RDF Q/A system is complete. Then, for incomplete queries, we propose a Markov-based method to supplement SPARQL queries. Finally, we compare our approach with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method improves the precision significantly.
AB - RDF question/answering(Q/A) system allows users to ask questions in natural language on a knowledge base represented by RDF and retrieve answers. A common problem in RDF Q/A is that existing works tend to translate a natural language question into an incomplete SPARQL query, which means that SPARQL queries may not fully understand user's ideas. For example, some triple patterns may be missing in the question translation stage. In this poster, we first present a siamese adaptation of the Long Short-Term Memory(LSTM) network to detect whether the SPARQL query generated by the RDF Q/A system is complete. Then, for incomplete queries, we propose a Markov-based method to supplement SPARQL queries. Finally, we compare our approach with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method improves the precision significantly.
KW - LSTM
KW - Question Answering
KW - RDF
KW - Siamese Neural Networks
UR - http://www.scopus.com/inward/record.url?scp=85091705181&partnerID=8YFLogxK
U2 - 10.1145/3366424.3382695
DO - 10.1145/3366424.3382695
M3 - Conference contribution
AN - SCOPUS:85091705181
T3 - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
SP - 57
EP - 58
BT - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
PB - Association for Computing Machinery
T2 - 29th International World Wide Web Conference, WWW 2020
Y2 - 20 April 2020 through 24 April 2020
ER -