Discovering Topic Evolution and Semantic Relationships in Patents
Explore the evolution path and semantic relationships in patents through entity relationships. Addressing limitations in existing work, this research focuses on discovering semantic similarities among entities and relationships among topics. The methodology involves training models for entity annotation and relationship extraction, clustering topics, and defining topic evolution patterns. The main results show efficacy, mechanical, and function-area relationships from 2015-2016.
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Presentation Transcript
Topic Evolution Path and Semantic Relationship Discovery Based on Patent Entity Relationship Jinzhu Zhang Linqi Jiang Department of Information Management, School of Economics & Management Nanjing University of Science and Technology Nanjing China zhangjinzhu@njust.edu.cn Department of Information Management, School of Economics & Management Nanjing University of Science and Technology Nanjing China sufi_jiang@163.com
Limitations of Existing Work Current studies didn't consider all the words in the patent and the semantic relationship between them. the relationships among topics should be more concrete, we should not only find the evolution relationship, but also need to reveal the semantic relationships among topics Integration in Function-Area Topic1 Keyword1; Keyword2; Topic1 Entity1; Entity2; Topic2 Keyword1; Keyword2; Topic2 Entity1; Entity2; integration Current studies Our studies
Method Discovery of Topic Evolution Path Based on Semantic Similarity Among Entities Discovery of Semantic Relationship Among Topics predefine five types of semantic relationships among patent entities Manually annotate the entity of data set Train BiLSTM-CRF Manually annotate the with predefined relationships Extract all entities of patents Train OpenNRE Clustering topics and get entities of each topic predict all relationships among entities the semantic relationship between two topics is based on the semantic relationship among all pairs of entities Define five topic evolution patterns
Main Result The results are obtained from 2015-2016 as an example T0(t2) T1(t2) T2(t2) T3(t2) T4(t2) T5(t2) T6(t2) T7(t2) Efficacy relationship Efficacy relationship Efficacy relationship T0(t1) Mechanical relationship Efficacy relationship T1(t1) Function- Area relationship Efficacy relationship T2(t1) Mechanical relationship Mechanical relationship Mechanical relationship T3(t1) Efficacy relationship Efficacy relationship T4(t1) Function- Area relationship Function- Area relationship T5(t1)
Thanks Jinzhu Zhang: zhangjinzhu@njust.edu.cn Linqi Jiang: sufi_jiang@163.com
