This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessArticle
DIKWP Semantic Judicial Reasoning: A Framework for Semantic Justice in AI and Law
by
Yingtian Mei
Yingtian Mei 1,†
and
Yucong Duan
Yucong Duan 2,*,†
1
School of Cyberspace Security, Hainan University, Haikou 570228, China
2
School of Computer Science and Technology, Hainan University, Haikou 570228, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Information 2025, 16(8), 640; https://doi.org/10.3390/info16080640 (registering DOI)
Submission received: 14 June 2025
/
Revised: 24 July 2025
/
Accepted: 25 July 2025
/
Published: 27 July 2025
Abstract
Semantic modeling of legal reasoning is an important research direction in the field of artificial intelligence and law (AI and law), aiming to enhance judicial transparency, fairness, and the consistency of legal applications through structured semantic representations. This paper proposes a semantic judicial reasoning framework based on the “Data–Information–Knowledge–Wisdom–Purpose” (DIKWP) model, which transforms the conceptual expressions of traditional legal judgment into DIKWP graphs enriched with semantics. The framework integrates the objective content of legal norms with stakeholders’ subjective cognition through a DIKWP×DIKWP bidirectional mapping mechanism, achieving “semantic justice”. Specifically, we define a DIKWP-based legal knowledge representation method and design a mapping algorithm from traditional legal concepts to the DIKWP semantic structure. To validate the effectiveness of the framework, we use a real administrative law case as an example and construct DIKWP (normative content) and DIKWP (subjective cognition) graphs to model legal rules, evidence, and various perspectives. The results indicate that the intention-driven semantic transformation mechanism can harmonize legal reasoning with stakeholders’ cognitive backgrounds, thereby enhancing the interpretability and fairness of judicial interpretation. Case analysis further demonstrates that reasoning within the DIKWP semantic space can reveal underlying assumptions, bridge cognitive gaps, and promote judicial fairness by aligning legal intentions. This study provides new theoretical and methodological support for the explainable reasoning of intelligent judicial systems.
Share and Cite
MDPI and ACS Style
Mei, Y.; Duan, Y.
DIKWP Semantic Judicial Reasoning: A Framework for Semantic Justice in AI and Law. Information 2025, 16, 640.
https://doi.org/10.3390/info16080640
AMA Style
Mei Y, Duan Y.
DIKWP Semantic Judicial Reasoning: A Framework for Semantic Justice in AI and Law. Information. 2025; 16(8):640.
https://doi.org/10.3390/info16080640
Chicago/Turabian Style
Mei, Yingtian, and Yucong Duan.
2025. "DIKWP Semantic Judicial Reasoning: A Framework for Semantic Justice in AI and Law" Information 16, no. 8: 640.
https://doi.org/10.3390/info16080640
APA Style
Mei, Y., & Duan, Y.
(2025). DIKWP Semantic Judicial Reasoning: A Framework for Semantic Justice in AI and Law. Information, 16(8), 640.
https://doi.org/10.3390/info16080640
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article metric data becomes available approximately 24 hours after publication online.