Abstract
This chapter describe the evolution of Concept Science that gave rise to Concept Parsing Algorithms (CPA). Concept Science developed ways to clarify conceptual content encoded in unstructured text that communicate context-specific knowledge in a sublanguage within a discipline. It was developed and tested since the early 1990s at the University of Toronto and Ryerson University in Toronto (Shafrir and Etkind, 2010). Concept Science lead to Pedagogy for Conceptual Thinking with Meaning Equivalence Reusable Learning Objects (MERLO) that offer a powerful tool for engaging and motivating students, and enhancing learning outcomes. This chapter describe some of Concept Science-based tools that provide new ways to discover, encode, and manage knowledge in large digital libraries of unstructured text in educational, governmental, NGO, and business organizations.
TopIntroduction
Concept Science is an emergent discipline. Its content is the conceptual structure of content of different disciplines, and the different ways in which labeled patterns in human experience are encoded and communicated in different domains of knowledge. The challenge of Concept Science is not the construction of generic semantic tools for discovering literal meanings encoded in natural language. Rather, Concept Science strives to find ways to clarify conceptual content encoded in unstructured text that communicate context-specific knowledge in a sublanguage within a discipline: ‘Taking conceptual analyses seriously entails demonstrating in any research project as strong a concern for epistemological and ontological issues as for issues of study design, sample characteristics, measurement issues, data collection, and statistical analyses. Deep conceptual issues need to be understood as integral to, and constitutive of, the entire research process, not as peripheral addenda or convenient heuristics’ (Overton, 2015).