Towards a New Grammar of Reasoning for Artificial Legal Intelligence and the Mecelle as Its Semantic Protocol
Ali Goksu, F. Gozde Kardes, Mustafa Yaylali
Why It Matters
What makes this one worth your time
This research is relevant for AI engineers and researchers interested in developing systems that better understand and interact with legal contexts, moving beyond traditional data-driven approaches.
Mecellem offers a new framework for dynamic legal reasoning in the age of AI.
Summary
The paper proposes the Mecellem semantic protocol as a framework to address the epistemic and methodological challenges in legal practice, emphasizing the need for context-dependent legal reasoning rather than mere data retrieval.
Key contributions
- Introduction of the Mecellem semantic protocol for legal reasoning.
- Framework for integrating neurosymbolic systems and knowledge graphs within legal contexts.
- Reconceptualization of law as an ontodynamic architecture.
Notable insights
- The paper highlights the inadequacy of purely quantitative methods in legal reasoning, advocating for a more nuanced ontological approach.
- It suggests that legal knowledge should be viewed as an evolving construct rather than a static set of rules.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2608.04011v1 Announce Type: cross Abstract: This article examines the enduring epistemic and methodological crisis of traditional legal practice in light of the opportunities and constraints introduced by artificial intelligence. It proposes an ontologically grounded framework termed the Mecellem semantic protocol as a response to this crisis. The analysis focuses on the structural tension within law between maintaining normative coherence and adapting to evolving social and institutional conditions, and shows why approaches based solely on codification, positivist systematization, or quantitative methods such as jurimetrics are insufficient. The article argues that legal reasoning cannot be reduced to data retrieval or statistical pattern recognition. Instead, it is grounded in the premise that meaning is context-dependent and must be dynamically reconstructed through ontologically defined entity categories and differentiated layers of knowledge. Within this perspective, Mecellem reconceptualizes law not as a fixed system of rules, but as an ontodynamic architecture structured along the interconnected axes of ontology, epistemology, and methodology. The transition from jurimetrics to a semantic protocol is presented not merely as a technical shift, but as a transformation in the foundations of legal knowledge. The article further argues that neurosymbolic systems, knowledge graphs, and agentic artificial intelligence can effectively address persistent legal challenges only when embedded within such an ontodynamic framework. By understanding law as a domain of ongoing formation rather than a completed rational totality, Mecellem advances a context-sensitive, auditable, and coherent model for legal meaning production at both human and machine levels, offering a comprehensive framework for rethinking legal reasoning in the age of artificial intelligence.