Novel da Costian-Tarskian Ontology Heterogeneity Approach
📄#ontologies#consequence-systems#heterogeneityRecentcollected in 5h

Novel da Costian-Tarskian Ontology Heterogeneity Approach

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💡New logic framework unifies ontologies via consequence systems—vital for scalable AI knowledge graphs.

⚡ 30-Second TL;DR

What changed

Introduces da Costian-Tarskianism inspired by Carnap, Goguen, da Costa, and Tarski.

Why it matters

Advances modular ontology engineering, potentially improving heterogeneous knowledge integration in AI systems like semantic webs and multi-ontology reasoning.

What to do next

Download arXiv:2602.15158v1 to implement extended consequence systems in your ontology toolkit.

Who should care:Researchers & Academics

🧠 Deep Insight

Web-grounded analysis with 3 cited sources.

🔑 Key Takeaways

  • The paper introduces **da Costian-Tarskianism** as a novel method for managing ontological heterogeneity, drawing from Carnapian-Goguenism while using consequence systems instead of institutions[1][3].
  • Named after Newton da Costa’s Principle of Tolerance (renamed Principle of Non-Triviality) and Alfred Tarski’s consequence operators, it serves as a dual to the Carnapian-Goguenist approach[1].
  • Builds on consequence systems developed by Carnielli et al. and Citkin & Muravitsky, extending them with ontological axioms[1][3].

🛠️ Technical Deep Dive

  • Uses **extended consequence systems** augmented with ontological axioms, analogous to institutions but focused on theorem conservation in refinements[1].
  • Refinements represented diagrammatically similar to institutions, but links denote theoremhood preservation in da Costian-Tarskian approach versus model conservation in Carnapian-Goguenism[1].
  • Formalizes da Costa’s Principle using Tarski-style consequence operators \( \mathrel{\hbox{\set@color\raisebox{3.44444pt}{$\rule[-6.45831pt]{0.47787pt}}}} [1].
  • Leverages machinery from [3] (Carnielli et al.) and (Citkin & Muravitsky) for representing classes of logics[1][3].

🔮 Future ImplicationsAI analysis grounded in cited sources

This theoretical framework could enhance tools for applied ontology in AI, improving interoperability across heterogeneous knowledge representations in multi-ontology systems.

⏳ Timeline

2010-01
Kutz, Mossakowski, Lücke publish foundational Carnapian-Goguenism paper, inspiring the dual da Costian-Tarskian approach[1].

📎 Sources (3)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

  1. arxiv.org
  2. papers.cool
  3. arxiv.org

This arXiv paper proposes da Costian-Tarskianism, blending Carnapian-Goguenism with da Costa's tolerance principle and Tarski's consequence operators for ontological heterogeneity. It introduces extended consequence systems augmented with ontological axioms and extended development graphs for relating ontologies via morphisms, fibring, and splitting. The work discusses implications for applied ontology.

Key Points

  • 1.Introduces da Costian-Tarskianism inspired by Carnap, Goguen, da Costa, and Tarski.
  • 2.Defines extended consequence systems with ontological axioms.
  • 3.Proposes extended development graphs supporting morphisms, fibring, and splitting.
  • 4.Builds on consequence systems by Carnielli et al. and Citkin & Muravitsky.

Impact Analysis

Advances modular ontology engineering, potentially improving heterogeneous knowledge integration in AI systems like semantic webs and multi-ontology reasoning.

Technical Details

Leverages consequence system machinery for formal ontology relations. Extended graphs enable structured operations between ontologies, extending prior development graph concepts.

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Original source: ArXiv AI