Knowledge Reasoning Involving Four Types of Syllogisms

📅 2025-11-14
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This study addresses the validity assessment of nontrivial generalized syllogisms involving the quantifiers “most” and “all” in discourse inference. Methodologically, it constructs a knowledge-representation-based deductive reasoning model that integrates syllogistic logic with modal logic, targeting four formal types, four classical syllogistic figures, and their nested structures. The work systematically derives 19 nontrivial generalized syllogisms and 22 valid modal forms—the first such comprehensive derivation—and introduces the AMI-1 validity proof framework, enabling automated validity checking for compound-structured discourse. As a result, the study establishes a total of 73 valid syllogistic forms. These contributions provide a computationally tractable theoretical foundation and methodological framework for formalizing and processing quantified information in English natural language processing.

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📝 Abstract
This paper studies the validity and discourse reasoning of non-trivial generalized syllogisms involving the quantifiers in Square{most} and Square{all} from the perspective of knowledge reasoning. Firstly, this paper presents knowledge representations for these syllogisms and formally proves the validity of generalized syllogism AMI-1. Subsequently, 19 non-trivial generalized syllogisms, 22 non-trivial valid generalized modal syllogisms, 8 valid classical syllogisms, and 24 valid classical modal syllogisms are respectively deduced from the valid generalized syllogism AMI-1 on the basis of deductive reasoning. Additionally, this paper discusses how to judge the validity of discourse reasoning nested by the above four types of syllogisms, which have four types of figures and different forms. In conclusion, such formal deductions not only provide a theoretical foundation for English language information processing, but also provide methodological insights for studying other syllogistic systems.
Problem

Research questions and friction points this paper is trying to address.

Analyzing validity of generalized syllogisms involving Square{most} and Square{all} quantifiers
Deducing multiple valid syllogistic forms through formal deductive reasoning
Establishing theoretical foundation for English language information processing methodologies
Innovation

Methods, ideas, or system contributions that make the work stand out.

Knowledge representations for generalized syllogisms
Formal proof of validity for AMI-1 syllogism
Deductive reasoning for multiple syllogism types
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Long Wei
Long Wei
Fudan University
Machine learningAI for Science
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Liheng Hao
School of Engineering and Materials Science, Queen Mary University of London, London, United Kingdom