Controlled vocabulary of concepts

Prominent figures such as Gottfried Wilhelm Leibniz [Leib1999], Charles Sanders Peirce [Peir1898a], Rudolph Wittgenstein [LuWi1921], Rudolph Carnap [Carn1947], Rosch et al. [Rosc1975], [Rosc1978], [MeRo1981], and Willem Levelt [Leve1989] have made significant contributions to the understanding and processing of language. Leibniz laid the foundation for formal language processing and tried to create a lingua universalis with the aim of creating a world catalog of ideas. Peirce focused on semiotics and meaning making. Wittgenstein's ideas on language and meaning influenced contemporary debates. Carnap emphasized clarity and logic in language use. Rosch's research on categorization shaped linguistic studies, and Levelt contributed to language production models. Their work has enriched various disciplines, including computational linguistics, semiotics, psychology, and philosophy of language. In this paper, we take on the challenge of creating a Controlled Vocabulary of Concepts (CVC) that allows the composition of term meanings through arbitrary semantic relations to sub terms. So far, mainly relations such as hypernym, hyponym, synonym, antonym, and meronym have been available. To the best of our knowledge, there is currently no other general methodology for defining composite terms such as functional nouns, conceptual noun phrases, prepositional concepts, etc. We acknowledge this gap and aim to explore and present a novel approach to address this challenge, with a particular focus on demonstrating the benefits of such a methodology in improving semantic search (SS) capabilities by a method called Search by Meaning (SbM). By showcasing how the proposed method can enhance the accuracy and relevance of semantic search results, we aim to illustrate its practical value and potential impact on semantic search.

The central research question to be answered is: “How can entries of a Controlled Vocabulary of Concepts (CVC) be structured in such a way that they accurately represent one or more meanings by Word Sense Definitions (WSD)?” This leads to further questions: What is a minimal set of words to construct a Foundational Core Glossary (FCG) to support the automated translation between any pair of languages?  How can the definition of complex Semantic Compounds (SC) be constructed from Semantic Primes (SP)? How to extend existing methods for Semantic Search (SS) to a Deep Semantic Search (DSS) that provides more relevant results.

The rest of this paragraph is organized as follows. The Related Work discusses related work from the fields of linguistics, Semantography, and Princeton WordNet (PWN). In the Concept Binary Tree (CBT), we introduce Concept Binary Trees (CBTs) along with other basic definitions. They are the basis for defining, representing, and querying complex linguistic data structures such as Word Sense Definitions (WSD). The central Concept Composition describes which concepts are defined as Semantic Primes (SP) and how they are used in the composition of Semantic Compounds (SC). The new Search by Meaning (SbM) technology is described in Search by Meaning (SbM), and the Discussion explains the application of Search by Meaning (SbM) in several large-scale ontology applications and compares its efficiency in random examples with Google search and Princeton WordNet (PWN). The analysis in the Evaluation takes up and shows how to solve the deficits described in the Related Work, and the Results summarizes the results.