Concept Science

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.

Concept Parsing Algorithms (CPA)

This chapter describe Concept Parsing Algorithms (CPA), a novel methodology of using text analysis tools for discovery of ‘building blocks' of concepts, with semantic searches of the full text of potentially relevant documents in relevant knowledge domains, for lexical labels of concepts in controlled vocabularies. The meaning of lexical label of a super-ordinate concept C' in a sublanguage with controlled vocabulary is encoded in a set that contains three sets of building blocks: Ci (set of co-occurring sub-ordinate concepts); Rj (set of relations); and Lk (set of linguistic elements/descriptors).

Natural Language and Sub-Languages with Controlled Vocabularies

This chapter describe differences between natural languages and special-purpose languages, where certain words used to describe observed regularities and patterns, acquire over time specific meanings that differ from their ‘ordinary' meanings in the language. Folk taxonomies, encoded in languages of peoples who occupy narrow ecological niches, serve an existential need of encoding knowledge important for survival. While folk biology developed taxonomies based on the human sensory system, modern biology evolves by including observational data from molecular biology collected with modern bio-chemical tools – scientific ‘extensions' of the human sensory system. In contrast to general language, the controlled vocabulary in ‘specialist discourse', also referred to by linguists as ‘sublanguage' and ‘Language for Special Purposes' (LSP) allows specialists to communicate in precisely defined terms and to avoid ambiguity in discussing specific conceptual situations

Meaning Equivalence Reusable Learning Objects (MERLO)

In the chapter we discuss Meaning Equivalence Reusable Learning Objects (MERLO), a multi-dimensional database that allow sorting and mapping of important concepts in a given knowledge domain through multi-semiotic representations in multiple sign systems, including: exemplary target statements of particular conceptual situations, and relevant other statements. MERLO pedagogy guides sequential teaching/learning episodes in a course by focusing learners' attention on meaning. The format of MERLO assessment item allow the instructor to assess deep comprehension of conceptual content by eliciting responses that signal learners' ability to recognize, and to produce, multiple representations, in multiple sign-systems - namely, multi-semiotic - that share equivalence-of-meaning. Exposure of scholars and learners to multi-semiotic inductive questions enhance cognitive control of inter-hemispheric attentional processing and enhance higher-order thinking. It highlights the important role of representational competence in scholarship, teaching and learning.

Interactive Concept Discovery (INCOD)

This chapter describe systematic exploration of important concepts in digital libraries with Key Word in Context (KWIC) semantic search that allow learners to explore specific conceptual situations by searching lexical label of a concept. Comprehensive record of a learner's sequence of searches allows for a detailed reconstruction of the learning episodes generated by Interactive Concept Discovery (InCoD) over time. It reveals the learner's consistency of ‘drilling-down' for discovering deeper building blocks of the particular concept, and the temporal evolution of learning outcomes.

Meaning Equivalence (ME), Boundary of Meaning (BoM), and Granulary of Meaning (GoM)

This chapter describe Meaning Equivalence (ME), Boundary of Meaning (BoM), and Granularity of Meaning (GoM). Meaning Equivalence (ME) is a polymorphous - one-to-many - transformation of meaning that signifies the ability to transcode equivalence-of-meaning through multiple representations within and across sign systems, and multiple definitions of a concept in multiple sign systems. Boundary of Meaning (BoM) is the boundary between two mutually exclusive semantic spaces in the sublanguage: (i) semantic space that contains only representations that do share equivalence-of-meaning with the Target Statement (TS); and (ii) semantic space that contains only representations that do not share equivalence-of-meaning with the TS. Granularity of Meaning (GoM) is the deepest level in which lexical label of a co-occurring subordinate concept appears in the Target Statement. It is therefore a measure of the ‘depth of exploration' of building blocks of a super-ordinate concept in TS. Boundary of Meaning (BoM) and Granularity of Meaning (GoM) are concepts in Pedagogy for Conceptual Thinking, a novel teaching and learning methodology in the digital age (Etkind, Kenett & Shafrir, 2016). These constructs describe important aspects of learning outcomes.

Evolving Concepts

This chapter describe the evolution of concepts with Concept Parsing Algorithms (CPA) that captures both the conceptual content and the conceptual structure of a context within a domain of knowledge, and results in a comprehensive, schematic description of important concepts at the time of analysis. Online availability of digital books, journals, comprehensive datasets, etc., lead to the evolution of research methods that expand the potential of CPA for exploring co-occurrence of concepts beyond the literature of a particular area in a knowledge domain, that may also include ‘neighbouring' areas in the knowledge domain. It supports the evolution of the novel research methodology Literature-Based Discovery (LBD).

Representation of Meaning in Different Semiotic Systems

This chapter describe issues related to the ability to represent meaning in different semiotic systems that plays a major role in the development of infants and continues to influence humans throughout life. The semiosphere is the symbolic environment into which a child grows, that defines the types of representations encoded in the developing child's mind. It is dynamic and multifunctional, and includes a class of meaning-preserving transformations. These symbolic transformations generate multiple representations with equivalent meaning, and inevitably result in the over-determination of meaning within the semiosphere. Early meaning, derived from perceptual cues, evolve to mature meaning derived from combinations of perceptual cues and memories of their consequences. Adults generate intentional responses to meaning of combinations of perceptual and intellectual stimuli, and are aware of representation of meaning in different semiotic systems.