A multi-phase correlation search framework for mining non-taxonomic relations from unstructured text

• Published in: Knowledge and information systems Vol. 38; no. 3; pp. 641 - 667
• Main Authors: Wong, Mei Kuan, Abidi, Syed Sibte Raza, Jonsen, Ian D.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2014; Springer; Springer Nature B.V
• Subjects: Analysis; Applied sciences; Computer Science; Computer science; control theory; systems; Correlation; Correlation analysis; Data mining; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Database Management; Electronic documents; Exact sciences and technology; Extraction; Information retrieval; Information Storage and Retrieval; Information systems; Information Systems and Communication Service; Information Systems Applications (incl.Internet); Information systems. Data bases; IT in Business; Knowledge management; Labeling; Labelling; Machine learning; Marine biology; Memory organisation. Data processing; Ontology; Regular Paper; Searches; Searching; Semantic web; Semantics; Sentences; Software; Studies; Subject specialists; Taxonomy; Texts; Correlation search; Relation labelling; Association rule mining; Non-taxonomic relation; Ontology learning; Lift interestingness measure; Ontology; Non structured information; Electronic document; Labelling; Statistical association; Biology; Text; Data mining; Association scheme; Interest; Optical correlation; Optical filter; Verb; Spatial filters; Sentence; Expertise

Over the last decade, ontology engineering has been pursued by “learning” the ontology from domain-specific electronic documents. Most of the research works are focused on extraction of concepts and taxonomic relations. The extraction of non-taxonomic relations is often neglected and not well resear...