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Three Types of Negation of Triple and its Elements and an Extension of Triple

arXiv 2609.08271 ↗First posted 8 Sep 2026cs.AIcited by 0

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Abstract (arXiv)

In various data models, the classical triple is a typical semantic data model. However, due to the design of the triple as a simple structure for representing positive assertions, it cannot sufficiently express different forms of negation present in the triple and its elements. This paper conceptually proposes that there are three distinct forms of negation within triples and their elements: contradictory negation, opposite negation and intermediary negation. Based on the the set SCOI and the logic LCOI+PLCOI with three kinds of negation, we propose an extension of triple that can distinguish and express these three different negations in the triple and its elements, called the TCOI triple with contradictory negation, opposite negation and intermediary negation. The TCOI triple is a semantic and structural extension of the classical triple. While retaining the ability to express positive assertions, it systematically introduces the three semantic dimensions of three negations, allowing these negations to independently act on the elements of the triple and on the whole triple. This significantly enhances the triple model capability to represent and reasoning about complex negative information. This paper also explores the expressive power and reasoning of the TCOI triple, as well as the application of TCOI triple implication reasoning in counterfactuals and counterfactual reasoning. We propose a truth-value (continuous value) algorithm for TCOI triple implication reasoning and perform its calculation through an example of the counterfactuals and counterfactual reasoning.

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References · 13

  1. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  2. 2016Complex Embeddings for Simple Link Predictioncited by 897
  3. 2020Knowledge Graphscited by 619
  4. 2015A Review of Relational Machine Learning for Knowledge Graphscited by 264
  5. 2019DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphscited by 173
  6. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  7. 2020Negative Statements Considered Usefulcited by 9
  8. 2023Completeness, Recall, and Negation in Open-World Knowledge Bases: A Surveycited by 7
  9. 2016A Survey of RDF Data Management Systemscited by 3
  10. 2023Can large language models generate salient negative statements?cited by 3
  11. 2025Three Kinds of Negation in Knowledge and Their Mathematical Foundationscited by 3
  12. 2016Negation in SPARQLcited by 1
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  1. 2022A Minimal Deductive System for RDFS with Negative Statementscited by 1

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