Claims, Arguments & Evidence Framework

• The CAE Framework is a semantic model that structures scientific argumentation with explicit claims, supporting evidence, and traceable attributions.
• It employs formal ontologies, networked reasoning, and dynamic modeling to automate verification and enhance transparency in complex domains.
• Its applications span biomedicine, legal causality, cybersecurity, and AI fairness, enabling robust argument mapping and continuous assurance.

The Claims Arguments Evidence (CAE) Framework is a structured, semantic paradigm for representing and analyzing scientific and technical argumentation. It formalizes the relationships among claims, arguments, and evidence, enabling explicit attribution, traceability, and computability across domains such as biomedicine, legal causality, cyber-attribution, safety assurance, and AI fairness. The framework is instantiated in models such as Micropublications (Clark et al., 2013), extended in agent-based, probabilistic, and structured argumentation frameworks (Shakarian et al., 2014, Assaad et al., 2023, Bloomfield et al., 16 May 2024), and operationalized in dynamic assurance cases for advanced AI systems (Sabuncuoglu et al., 12 May 2025, Goemans et al., 12 Nov 2024, Schnelle et al., 11 Jun 2025). CAE's central contributions include its ontological clarity, support for automation, explicit provenance, and a foundation for both human-in-the-loop and machine-driven verification and governance.

1. Formal Foundations and Semantic Structures

At its core, the CAE framework provides a semantic model for formalizing the structure of arguments as networks of claims, their supporting or challenging evidence, and explicit argumentation relationships.

• In the Micropublications model (Clark et al., 2013), every argument is encapsulated as a micropublication, formally represented as

$\mathrm{MP}_a = (A_{mp(a)}, c, A_c, D, R)$

where $c$ is the primary claim, $A_{mp(a)}$ and $A_c$ are respective attributions, $D$ is a non-empty set of supporting representations (statements, data, methods), and $R$ encodes directed support and challenge relations as a strict partial order over the argument graph.

• The model distinguishes between different classes of representations, including claims (truth-bearing statements, natural language or formal), empirical evidence (tables, figures, datasets), methods, and objections.
• Provenance and attribution are first-class citizens: each element (claim, evidence, annotation) is explicitly attributed to its source, supporting both human and computational verification.
• The CAE framework often employs formal knowledge representation standards, notably OWL 2 ontologies, RDF serialization for interoperability, and SWRL rules to support reasoning over support/challenge relationships.

This fine-grained, semantically annotated structure enables precise tracing of claim lineages, fine-grained citation (claims citing other explicit statements), and computable challenge/support graphs.

2. Key Features: Granularity, Computability, and Networked Reasoning

The CAE approach distinguishes itself through several core features:

• Semantic Clarity and Granularity: Each claim is asserted alongside its direct supporting evidence or method, making the semantics, attribution, and evidential basis of each assertion explicit. Models support layering from the minimal (single claim plus attribution) to maximal (deep evidentiary networks).
• Computability and Interoperability: Formalizations in OWL/RDF support machine-queryable, reasoner-friendly representations. Support and challenge relations embed directed acyclic argumentation graphs that can be mined, traversed, and analyzed algorithmically.
• Claim and Citation Network Construction: Explicit modeling of inter-claim support and challenge—along with transitive closure across documents—enables systematic tracing of argument provenance and identification of phenomena such as citation distortion (where claims propagate absent evidence).
• Explicit Modeling of Objections and Disagreement: Bipolar/multipolar networks encode not only supporting but also challenging and objection relations, allowing models to represent nuanced scientific disagreement and refine argument closure procedures.

These features enable downstream applications, such as automated argument mapping, semantic search, algorithmic review support, and robust audit trails in digital knowledge ecosystems.

3. Exemplary Use Cases and Domain Applications

The CAE framework’s broad applicability is demonstrated in a spectrum of domains.

Domain	Application	CAE Feature Used
Biomedical Publishing	Micropublications for claim annotation	Support graph; RDF
Cyber-warfare Attribution	InCA framework for dialectical reasoning	Probabilistic + PreDeLP
Legal Causality	Probability of causation bounds	Counterfactual modeling
ML Safety Assurance	Safety case argumentation	Dynamic CAE structure
AI Fairness Assurance	Argument-based continuous monitoring	Dynamic evidence linkage

• Micropublications enable citation-level annotation, digital abstract generation, evidence-linked formal summaries, and stand-off annotation in full-text articles (Clark et al., 2013).
• In cyber-warfare, InCA (Shakarian et al., 2014) leverages a probabilistic environmental model and a structured, defeasible argumentation logic (PreDeLP), linking analytical claims to uncertain evidence via annotated support graphs.
• Causality assessments (effects of causes vs. causes of effects) rely on counterfactual reasoning, where the probability of causation is bounded using statistical and epidemiological evidence (e.g., $pc_A \geq \max\left\{0, 1 - 1/RR_A\right\}$) (Dawid et al., 2013).
• Safety assurance for learning-enabled systems employs dynamic, evidence-linked CAE cases, incorporating ongoing evidence ingestion and risk register management (Dong et al., 2021, Sabuncuoglu et al., 12 May 2025, Goemans et al., 12 Nov 2024, Schnelle et al., 11 Jun 2025).

4. Technical Implementations and Operationalization

A key trait of CAE frameworks is instantiability in formal and computational environments.

• Ontology and Rule Representation: Micropublications and their descendants define explicit OWL 2 vocabularies (e.g., mp:Micropublication, mp:Claim, mp:Attribution, mp:Representation, mp:supports, mp:challenges).
• Knowledge Graph Integration: Advanced models (e.g., ClaimVer (Dammu et al., 12 Mar 2024)) retrieve and align claims with multi-hop KG evidence, quantifying evidential attribution through scores such as the KG Attribution Score (KAS):

$$\mathrm{KAS} = \sigma_{mod} \left( \sum_{i=1}^n TMS_i \times cs(y_i) \right)$$

where TMS combines semantic similarity and entity overlap, and $cs(y_i)$ is a claim-type-specific score.

• Dialectical and Probabilistic Reasoning: The InCA model (Shakarian et al., 2014) connects a probabilistic evidence environment with an analytical model (defeasible logic), using an annotation function to validate the warranting status of claims across probabilistic worlds.
• Dynamic Continuous Monitoring: AI fairness assurance frameworks (Sabuncuoglu et al., 12 May 2025) continually update the evidence base—drawing from data/model cards, bias metrics, and experiment logs—mapping this dynamic evidence into structured arguments.
• Evaluation and Scoring: Safety case assessments employ dual-scoring tables for procedural and implementation support, directly linking documentation adequacy and real-world process application to the credibility of claims and evidence (Schnelle et al., 11 Jun 2025).

5. Challenges, Limitations, and Ongoing Developments

Despite CAE’s formal rigor and extensibility, several limitations persist:

• Scalability and Annotation Cost: Comprehensive instantiation of support/challenge graphs, with granular attribution, places annotation demands on authors and curators. Success depends on seamless integration into routine workflows or automated extraction toolchains.
• Ambiguity and Subjectivity: Determining claim equivalence (e.g., similarity groups) and assigning evidential weight or challenge status is often subjective, necessitating clear standards and ontological alignment.
• Integration with Legacy Data: Retrofitting CAE structures into existing, unstructured literature requires substantial automated support or manual curation.
• Tooling and Community Standards: Adopting community-accepted ontologies and reasoning engines (e.g., SWRL, RDF, s(CASP), Clarissa/ASCE toolchains) is crucial for interoperability and automation.
• Ongoing Assurance and Trust: Dynamic systems (frontier AI, adaptive safety cases) require CAE frameworks to support continuous governance, risk register synchronization, and real-time linking of new evidence to existing argument structures.

Addressing these limitations involves refining formal ontologies, advancing automated annotation tools, and incorporating CAE into collaborative platforms and regulatory frameworks.

6. Comparative Perspectives and Integration with Related Paradigms

The CAE framework is distinguished in contrast to other scientific and technical argumentation models:

• Nanopublications represent minimal assertion-provenance-evidence triples but lack deep structures for comprehensive evidence or methods; CAE approaches explicitly integrate these components and support challenge relationships.
• SWAN provides an ontology for hypotheses and claims with literature-centric evidence, whereas CAE extends to direct empirical data, methods, and multipolar challenges, supporting layered abstraction and stronger networked analysis.
• Dynamic Assurance and Eliminative Argumentation: Recent developments incorporate defeaters (explicitly represented doubts or negations) and eliminative (negative) proof strategies, capturing dialectical processes and documenting the full argument lifecycle, including residual risk and known challenges (Bloomfield et al., 16 May 2024).
• Agent-Based and Probabilistic Models: Multi-agent communication frameworks (e.g., NormAN (Assaad et al., 2023)) introduce Bayesian and argument-exchange models that diverge from the deterministic logic of traditional CAE, providing insights into opinion dynamics and debate structure.

This comparative richness underlines the adaptability and continuous evolution of the CAE paradigm across scientific, engineering, legal, and socio-technical domains.

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In summary, the Claims Arguments Evidence (CAE) Framework is a rigorously formalized, semantically expressive, and computationally grounded approach for representing, analyzing, and automating scientific and technical argumentation. Its explicit attribution, layered abstraction, and computability provide a foundation for advanced applications in digital publishing, cyber-physical assurance, fairness auditing, and trustable AI, while ongoing research addresses its scalability, subjectivity, and integration challenges.