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Meaning Computation AI: Time, Space, and Knowledge Graphs
  • Leonid Nisenboym
Leonid Nisenboym

Corresponding Author:[email protected]

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Abstract

This paper aims to broaden the horizons of symbolic artificial intelligence. The key to doing so is to establish a scientific basis for knowledge foundations, specifically for the concept of meanings and meaning computation. This led to the development of meaning computation artificial intelligence (MCAI) as a research and development framework providing a semantic modeling environment to deliver meaning computation intelligence solutions. MCAI is based on research in the areas of knowledge mathematics, semantic information structures, computational ontologies, and models of acquiring and applying knowledge. The paper discovered that relations appear to be fundamentally positioned at the core of knowledge. They are implicit relations (which reflect meaning) and semantic relations (which describe how implicit relations act between entities). As a result, knowledge mathematics was introduced based on algebras of implicit and semantic relations. On the basis of semantic information structures in the form of semantic triple chains and implicit relations algebra, the paper proposed a semantic knowledge model defined by an axiomatic computational model of relational domain ontology and an associated algebraic semantic reasoning method. The semantic knowledge model was successfully tested in the domains of time (including non-metric and metric interval relations) and space (spatial distance relations). Further, semantic relations algebra was applied to solving problems of knowledge graph intelligence. To accomplish this, a relational ontology-based model of knowledge graph was introduced. This led to the development of semantic reasoning models for resolving knowledge graph intelligence problems of completion and querying. Additionally, a novel intelligence task was introduced: knowledge graph compression, along with a semantic reasoning model for its resolution.
17 Jan 2024Submitted to TechRxiv
26 Jan 2024Published in TechRxiv