Human-inspired Identification of High-level Concepts using OWA and Linguistic Quantifiers


  • Marek Z. Reformat thinkS2: thinking Software and Systems laboratory Electrical and Computer Engineering University of Alberta, Canada
  • Ronald R. Yager 1. Machine Intelligence Institute Iona Collage, New Rochelle, NY, USA 2. Visiting Distinguished Scientist King Saud University, Riyadh, Saudi Arabia
  • Zhan Li thinkS2: thinking Software and Systems laboratory Electrical and Computer Engineering University of Alberta, Canada
  • Naif Alajlan Advanced Lab for Intelligent Systems Research College of Computer and Information Sciences King Saud University, Riyadh, Saudi Arabia


concept identification, text documents, ontology, hierarchy of concepts, ordered weighted averaging operator, importance of concepts


Intelligent agent based system can be used to identify high-level concepts matching sets of keywords provided by users. A new human-inspired approach to concept identification in documents is introduced here. The proposed method takes keywords and builds concept structures based on them. These concept structures are represented as hierarchies of concepts (HofC). The ontology is used to enrich HofCs with terms and other concepts (sub-concepts) based on concept definitions, as well as with related concepts. Additionally, the approach uses levels of importance of terms defining the concepts. The levels of importance of terms are continuously updated based on a flow of documents using an Adaptive Assignment of Term Importance (AATI) schema. The levels of activation of concepts identified in a document that match these in the HofC are estimated using ordered weighted averaging (OWA) operators with linguistic quantifiers. A simple case study presented in the paper is designed to illustrate the approach.


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