Multiple order semantic relation extraction

• Published in: Neural computing & applications Vol. 31; no. 9; pp. 4563 - 4576
• Main Authors: Song, Shengli, Sun, Yulong, Di, Qiang
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2019; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Construction methods; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Information retrieval; Knowledge base; Logistics; Object recognition; Probability and Statistics in Computer Science; S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems; Semantic relations; Semantic web; Semantics; Structural hierarchy; Texts; Unstructured data; Semantic Web; Relation extraction; Hierarchical binary structure; Multiple order semantic parsing
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-018-3453-x

In order to get more comprehensive and accurate knowledge from the Semantic Web, it is essential to design an effective method to extract semantic data from the web and documents. However, as a crucial topic of semantic information extraction, relation extraction is a major challenge in knowledge base construction. Existing methods perform poorly on texts in the open domain due to their complex structure; this paper proposes a method named multiple order semantic relation extraction (MOSRE), which applies for multiple orders, a conceptual expression used in formal logistics, to build semantic patterns for extracting information from hybrid unstructured texts in the open domain with deep semantic analyses. Specifically, the proposed method automatically constructs a multiple order semantic tree from complex natural sentences and converts semantic information into a binary structure. Instead of constructing a large amount of pattern sets for comparing binary semantics between entities, MOSRE splits and reconstructs sentences into a strict hierarchical binary structure with combination rules in order to extract as much semantic information as possible. The extracted triples are then processed into several entity relations in the format 〈 subject, relational label, object 〉 after named entity recognition and refinement. MOSRE is validated by test results on two different datasets, achieving F 1 values of 83.8% on SENT500 and 35.5% on KBP.