An Entity-Based Main Path Analysis Method to Trace Knowledge Evolution at Micro-Level

DOI

10.2991/978-94-6463-498-3_10How to use a DOI?

Keywords

Main Path Analysis; Patent mining; Entity Extraction; Hard Disk Heads

Abstract

Main Path Analysis (MAP) method is a significant method for knowledge flow extraction from citation networks. Traditional MPA methods treat documents as network vertices, while neglecting the more granular information within the document, this neglect limits an in-depth understanding of knowledge development. To remedy the weakness, this study leverages deep learning algorithm on MPA method to facilitate an entity-based pathfinding method, thus to improve the interpretability of MPA method. This study introduces a four-step process to implement the proposed method: (1) Data preprocessing to structure the citation network for analysis. (2) Knowledge entity extraction using BERT-BiLSTM-CRF for identifying significant entities. (3) Main path search at the document level with a cluster-based approach for path identification. (4) Entity relationship identification across documents using a BERT-based model with a three-level masking strategy. This study aims to transform literature-based citation networks into detailed entity-based networks, enabling finer-grained knowledge flow extraction. Finally, to demonstrate the advantages of the new method, extensive experiments are conducted on a patent dataset pertaining to thin film head in computer hardware. Experimental results show that our method is capable of discovering more fine-grained knowledge flows from important sub-fields, and improving the interpretability of candidate paths as well.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Chi Yu
AU  - Weijiao Shang
AU  - Xiaozhao Xing
AU  - Haiyun Xu
AU  - Liang Chen
PY  - 2024
DA  - 2024/08/22
TI  - An Entity-Based Main Path Analysis Method to Trace Knowledge Evolution at Micro-Level
BT  - Proceedings of 2023 China Science and Technology Information Resource Management and Service Annual Conference (COINFO 2023)
PB  - Atlantis Press
SP  - 105
EP  - 122
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-498-3_10
DO  - 10.2991/978-94-6463-498-3_10
ID  - Yu2024
ER  -