A knowledge‐based approach to Information Extraction for semantic interoperability in the archaeology domain. (21st March 2015)

• Record Type: Journal Article
• Authors: Vlachidis, Andreas; Tudhope, Douglas
• Is Part Of: Journal of the Association for Information Science and Technology. Volume 67:Number 5(2016:May)
• Page Start: 1138
• Page End: 1152
• Publication Date: 2015-03-21
• Subjects: natural language processing -- text mining -- semantic analysis; Information science -- Periodicals; Information technology -- Periodicals; 020.5
• DOI: 10.1002/asi.23485
• Languages: English
• ISSNs: 2330-1635

Abstract

Abstract : The article presents a method for automatic semantic indexing of archaeological grey‐literature reports using empirical (rule‐based) Information Extraction techniques in combination with domain‐specific knowledge organization systems. The semantic annotation system (OPTIMA) performs the tasks of Named Entity Recognition, Relation Extraction, Negation Detection, and Word‐Sense Disambiguation using hand‐crafted rules and terminological resources for associating contextual abstractions with classes of the standard ontology CIDOC Conceptual Reference Model (CRM) for cultural heritage and its archaeological extension, CRM‐EH. Relation Extraction (RE) performance benefits from a syntactic‐based definition of RE patterns derived from domain oriented corpus analysis. The evaluation also shows clear benefit in the use of assistive natural language processing (NLP) modules relating to Word‐Sense Disambiguation, Negation Detection, and Noun Phrase Validation, together with controlled thesaurus expansion. The semantic indexing results demonstrate the capacity of rule‐based Information Extraction techniques to deliver interoperable semantic abstractions (semantic annotations) with respect to the CIDOC CRM and archaeological thesauri. Major contributions include recognition of relevant entities using shallow parsing NLP techniques driven by a complimentary use of ontological and terminological domain resources and empirical derivation of context‐driven RE rules for the recognition of semantic relationships from phrases of unstructured text.