mlsanityData to 7 Oct 2026

LLM-Assisted Modeling of Semantic Web-Enabled Multi-Agents Systems with AJAN

arXiv 2510.06911 ↗First posted 8 Oct 2025cs.AIcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

There are many established semantic Web standards for implementing multi-agent driven applications. The AJAN framework allows to engineer multi-agent systems based on these standards. In particular, agent knowledge is represented in RDF/RDFS and OWL, while agent behavior models are defined with Behavior Trees and SPARQL to access and manipulate this knowledge. However, the appropriate definition of RDF/RDFS and SPARQL-based agent behaviors still remains a major hurdle not only for agent modelers in practice. For example, dealing with URIs is very error-prone regarding typos and dealing with complex SPARQL queries in large-scale environments requires a high learning curve. In this paper, we present an integrated development environment to overcome such hurdles of modeling AJAN agents and at the same time to extend the user community for AJAN by the possibility to leverage Large Language Models for agent engineering.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 6 of its 15 references that are in the map. First posted on arXiv 8 Oct 2025.

Builds on

6 of 15 references.

References · 6

  1. 2019Bidirectional Attentive Memory Networks for Question Answering over Knowledge Basescited by 30
  2. 2023Robot Behavior-Tree-Based Task Generation with Large Language Modelscited by 22
  3. 2018EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphscited by 20
  4. 2021Bio-SODA: Enabling Natural Language Question Answering over Knowledge Graphs without Training Datacited by 6
  5. 2021Learning and Executing Re-usable Behaviour Trees from Natural Language Instructioncited by 5
  6. 2022Integrating AI Planning with Natural Language Processing: A Combination of Explicit and Tacit Knowledgecited by 3

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