A relationship extraction task requires the detection and classification of semantic relationship mentions within a set of artifacts, typically from text or XML documents. The task is very similar to that of information extraction (IE), but IE additionally requires the removal of repeated relations (disambiguation) and generally refers to the extraction of many different relationships.
Concept and applications
The concept of relationship extraction was first introduced during the 7th Message Understanding Conference in 1998. Relationship extraction involves the identification of relations between entities and it usually focuses on the extraction of binary relations. Application domains where relationship extraction is useful include gene-disease relationships, protein-protein interaction etc.
Current relationship extraction studies use machine learning technologies, which approach relationship extraction as a classification problem. Never-Ending Language Learning is a semantic machine learning system developed by a research team at Carnegie Mellon University that extracts relationships from the open web.
There are several methods used to extract relationships and these include text-based relationship extraction. These methods rely on the use of pretrained relationship structure information or it could entail the learning of the structure in order to reveal relationships. Another approach to this problem involves the use of domain ontologies. There is also the approach that involves visual detection of meaningful relationships in parametric values of objects listed on a data table that shift positions as the table is permuted automatically as controlled by the software user. The poor coverage, rarity and development cost related to structured resources such as semantic lexicons (e.g. WordNet, UMLS) and domain ontologies (e.g. the Gene Ontology) has given rise to new approaches based on broad, dynamic background knowledge on the Web. For instance, the ARCHILES technique uses only Wikipedia and search engine page count for acquiring coarse-grained relations to construct lightweight ontologies.
The relationships can be represented using a variety of formalisms/languages. One such representation language for data on the Web is RDF.
More recently, end-to-end systems which jointly learn to extract entity mentions and their semantic relations have been proposed with strong potential to obtain high performance.
Researchers have constructed multiple datasets for benchmarking relationship extraction methods. One such dataset was the document-level relationship extraction dataset called DocRED released in 2019. It uses relations from Wikidata and text from the English Wikipedia. The dataset has been used by other researchers and a prediction competition has been setup at CodaLab.
- Text analytics
- Semantic analytics
- Semantic role labeling
- Information extraction
- Business Intelligence
- Ning, Huansheng (2019). Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health: International 2019 Cyberspace Congress, CyberDI and CyberLife, Beijing, China, December 16–18, 2019, Proceedings, Part II. Singapore: Springer Nature. p. 260. ISBN 978-981-15-1924-6.
- Denecke, Kerstin (2012). Event-Driven Surveillance: Possibilities and Challenges. Heidelberg: Springer Science & Business Media. p. 36. ISBN 978-3-642-28134-1.
- Hong-Woo Chun; Yoshimasa Tsuruoka; Jin-Dong Kim; Rie Shiba; Naoki Nagata; Teruyoshi Hishiki; Jun-ichi Tsujii (2006). "Extraction of Gene-Disease Relations from Medline Using Domain Dictionaries and Machine Learning". Pacific Symposium on Biocomputing. CiteSeerX 10.1.1.105.9656.
- Minlie Huang and Xiaoyan Zhu and Yu Hao and Donald G. Payan and Kunbin Qu and Ming Li (2004). "Discovering patterns to extract protein-protein interactions from full texts". Bioinformatics. 20 (18): 3604–3612. doi:10.1093/bioinformatics/bth451. PMID 15284092.
- Tickoo, Omesh; Iyer, Ravi (2016). Making Sense of Sensors: End-to-End Algorithms and Infrastructure Design from Wearable-Devices to Data Centers. Portland: Apress. p. 68. ISBN 978-1-4302-6592-4.
- T.C.Rindflesch and L.Tanabe and J.N.Weinstein and L.Hunter (2000). "EDGAR: Extraction of drugs, genes, and relations from the biomedical literature". Proc. Pacific Symposium on Biocomputing. pp. 514–525. PMC 2709525.
- C. Ramakrishnan and K. J. Kochut and A. P. Sheth (2006). "A Framework for Schema-Driven Relationship Discovery from Unstructured Text". Proc. International Semantic Web Conference. pp. 583–596.
- W. Wong and W. Liu and M. Bennamoun (2009). "Acquiring Semantic Relations using the Web for Constructing Lightweight Ontologies". Proc. 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). doi:10.1007/978-3-642-01307-2_26.
- Dat Quoc Nguyen and Karin Verspoor (2019). "End-to-end neural relation extraction using deep biaffine attention". Proceedings of the 41st European Conference on Information Retrieval (ECIR). arXiv:1812.11275. doi:10.1007/978-3-030-15712-8_47.
- Elena Bruches; Alexey Pauls; Tatiana Batura; Vladimir Isachenko (14 December 2020), Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian (PDF), arXiv:2011.09817, Wikidata Q104419957
- Pham Quang Nhat Minh (18 December 2020). "An Empirical Study of Using Pre-trained BERT Models for Vietnamese Relation Extraction Task at VLSP 2020" (PDF). arXiv. arXiv:2012.10275. ISSN 2331-8422. Wikidata Q104418048.
- Yuan Yao; Deming Ye; Peng Li; et al. (2019). "DocRED: A Large-Scale Document-Level Relation Extraction Dataset" (PDF). Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: 764–777. arXiv:1906.06127. doi:10.18653/V1/P19-1074. Wikidata Q104419388.
- Wang Xu; Kehai Chen; Tiejun Zhao (21 December 2020). "Document-Level Relation Extraction with Reconstruction" (PDF). arXiv. arXiv:2012.11384. ISSN 2331-8422. Wikidata Q104417795.
- "DocRED. Competition. CodaLab".