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Semantic Entity Recognition and Relation Construction Method for Assembly Process Document

Abstract

Assembly process documents record the designers’ intention or knowledge. However, common knowledge extraction methods are not well suitable for assembly process documents, because of its tabular form and unstructured natural language texts. In this paper, an assembly semantic entity recognition and relation construction method oriented to assembly process documents is proposed. First, the assembly process sentences are extracted from the table through concerned region recognition and cell division, and they will be stored as a key-value object file. Then, the semantic entities in the sentence are identified through the sequence tagging model based on the specific attention mechanism for assembly operation type. The syntactic rules are designed for realizing automatic construction of relation between entities. Finally, by using the self-constructed corpus, it is proved that the sequence tagging model in the proposed method performs better than the mainstream named entity recognition model when handling assembly process design language. The effectiveness of the proposed method is also analyzed through the simulation experiment in the small-scale real scene, compared with manual method. The results show that the proposed method can help designers accumulate knowledge automatically and efficiently.

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Correspondence to Jinsong Bao.

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Gu, X., Hua, B., Liu, Y. et al. Semantic Entity Recognition and Relation Construction Method for Assembly Process Document. J. Shanghai Jiaotong Univ. (Sci.) (2022). https://doi.org/10.1007/s12204-022-2474-x

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  • DOI: https://doi.org/10.1007/s12204-022-2474-x

Key words

  • assembly process design
  • knowledge extraction
  • named entity recognition
  • text extraction in table
  • dependency syntactic parsing
  • attention mechanism

CLC number

  • TP 391

Document code

  • A