Semantic Retrieval Based on User Intention Recognition in Engineering Domain

  • Ling GeEmail author
  • Boshen Ding
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


In the big data era, the rapid explosion and diversification of knowledge have brought various difficulties to effective knowledge retrieval. The keyword matching-based retrieval can only carry out character matching mechanically, which ignores the semantic content contained in the keyword itself; ontology-based semantic retrieval does not recognize and analyze the structural relationship among keywords user inputs, so it is unable to mine the implicit user intentions; the effectiveness of the intention recognition method based on user’s behaviors is not easy to guarantee because behaviors are usually uncontrollable and variable. In view of the above deficiencies, this paper proposes a semantic retrieval method based on user intention recognition. Firstly, the paper introduces a glossary model to indicate different facets of relations of same type from different dimensions, and mine the inner association among keywords deeply. Secondly, by analyzing the structural relationship of multiple keywords, it acquires the user’s intention which is represented by four parameters using retrieval intention representation method, and gives a specific word expansion strategy directed towards different intentions. Finally, the effectiveness of the method is verified by an example which proves that the method can obtain more knowledge matching with the retrieval intent.


Semantic retrieval User intention recognition Glossary model Retrieval intention representation Word expansion 



This work was funded by National Key R&D Program of China, R&D and Application of 3D Printing Cloud Service Platform for Innovation and Entrepreneurship (No. 2017YFB1104200).


  1. 1.
    Voorhees, E.: Query expansion using lexical-semantic relations. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 61–69, Dublin (1994)Google Scholar
  2. 2.
    Maki, W., Mckinley, L., Thompson, A.: Semantic distance norms computed from an electronic dictionary (wordnet). Behav. Res. Methods Instrum. Comput. 36(3), 421–431 (2004)CrossRefGoogle Scholar
  3. 3.
    Gomez-Perez, A.: Evaluation of taxonomic knowledge in ontologies and knowledge bases. In: Proceedings of KAW (1999)Google Scholar
  4. 4.
    Song, J., Zhang, W., Xiao, W.: Ontology-based information retrieval model. J. Nanjing Univ. 189–197 (2005)Google Scholar
  5. 5.
    Liu, Y., Miao, J., Zhang, M.: How do users describe their information need: query recommendation based on snippet click model. Expert Syst. Appl. 38(11), 13847–13856 (2011)Google Scholar
  6. 6.
    Sadikov, E., Madhavam, J., Wand, L.: Clustering query refinements by user intent. In: Proceedings of the 19th International Conference on World Wide Web, pp. 841–850. ACM (2010)Google Scholar
  7. 7.
    Ran, J., Cha, L.: Ontology-based semantic retrieval design and implementation. Electron. Des. Eng. (5), 12–14 (2015)Google Scholar
  8. 8.
    Chauhan, R., Goudar, R., Sharma, R.: Domain ontology based semantic search for efficient information retrieval through automatic query expansion. In: International Conference on Intelligent Systems and Signal Processing (2013)Google Scholar
  9. 9.
    Mustamiin, M., Budi, I., Santoso, H.B.: Multi-documents summarization based on clustering of learning object using hierarchical clustering. J. Phys: Conf. Ser. 978(1), 012053 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Beijing Shenzhou Aerospace Software Technology Co., Ltd.BeijingChina
  2. 2.Beijing Institute of Aerospace Test TechnologyBeijingChina

Personalised recommendations