Structured query construction via knowledge graph embedding
Wang, R., Wang, M., Liu, J., Cochez, M., & Decker, S. (2020). Structured query construction via knowledge graph embedding. Knowledge and Information Systems, 62(5), 1819-1846. https://doi.org/10.1007/s10115-019-01401-x
Published inKnowledge and Information Systems
© Springer-Verlag London Ltd., part of Springer Nature 2019
In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency. ...
Publication in research information system
MetadataShow full item record
Additional information about fundingThis work is supported by National Key Research and Development Program of China (No. 2018YFB1004500), National Natural Science Foundation of China (61532015, 61532004, 61672419, and 61672418), Innovative Research Group of the National Natural Science Foundation of China (61721002), Innovation Research Team of Ministry of Education (IRT_17R86), Project of China Knowledge Centre for Engineering Science and Technology, Science and Technology Planning Project of Guangdong Province (No. 2017A010101029), Teaching Reform Project of XJTU (No. 17ZX044), and China Scholarship Council (No. 201806280450). ...
Showing items with similar title or keywords.
Drawing conclusions about what co-participants know : Knowledge-probing question-answer sequences in new employee orientation lectures Mikkola, Piia; Lehtinen, Esa (Sage Publications Ltd., 2019)This study aims to uncover the processes of interaction through which knowledge acquisition in new employee orientation is monitored and controlled. Using video-recordings of orientation lectures as data, the study focuses ...
Ambaye, Michael (2020)The aim of this thesis is to provide viable methods that can be used to improve the return position (RP) of a relevant document when a natural language query (NLQ) is applied by a user. For the purpose of demonstration, ...
Aalto, Eija; Tarnanen, Mirja; Heikkinen, Hannu L.T. (Pergamon Press, 2019)In this paper we report a qualitative case study of a teaching intervention in which a pre-service subject teacher pair planned and conducted a course integrating Finnish language and ethics in a multilingual setting. ...
Nurhas, Irawan; Pirkkalainen, Henri; Geisler, Stefan; Pawlowski, Jan (SCITEPRESS - Science and Technology Publications, 2021)This study aims to determine the competing concerns of people interested in startup development and entrepreneurship by using topic modeling and sentiment analysis on a social question-and-answer (SQA) website. Understanding ...
The integration of content and language in students’ task answer production in the bilingual classroom Jakonen, Teppo (Routledge, 2019)The notion of content and language integration has recently become a key topic of inquiry in research on content and language integrated learning and other kinds of bilingual educational programmes. Understanding what ...