Large Language Models (LLMs) are increasingly being integrated into knowledge-based applications, leveraging their language understanding capabilities to construct and interact with Knowledge Graphs (KGs) in meaningful ways. However, effective utilization of LLMs in knowledge-based applications requires more than just language processing; it requires an approach that enables structured/linked data handling, semantic querying, and interaction. This paper presents a novel approach that utilizes LLMs with Retrieval-Augmented Generation (RAG) and AI agents to support the development of KG-based semantic applications. Specifically, the approach is implemented with a custom framework, namely LLM4KGen, and the use of LangChain and LangGraph frameworks, implemented specifically using two versions of Gemini, 1.5 and 2.0. The first prototype implementation of the LLM4KGen framework has been evaluated within the context of a digital culture semantic application. The presented approach involves deploying specialized AI agents capable of performing targeted tasks like KG generation, semantic query handling, and framework-specific systems engineering (semi-automated code generation), enabling dynamic interactions between LLMs, developers, and knowledge engineers, within structured data environments. The proposed approach integrates LLM-generated KGs in graph databases (Neo4J) enabling semantic querying and data retrieval through AI agents. The evaluation of the presented LLM4KGen framework focuses on assessing the effectiveness of agent-based LLM interactions for KG generation and retrieval, measuring the query accuracy and efficiency of the approach.

Abstract Ontologies constitute the semantic model of Knowledge Graphs (KGs). This structural association indicates the potential existence of methodological analogies in the development of ontologies and KGs. The deployment of fully and well-defined methodologies for KG development based on existing ontology engineering methodologies (OEMs) has been suggested and efficiently applied. However, most of the modern/recent OEMs may not include tasks that (i) empower knowledge workers and domain experts to closely collaborate with ontology engineers and KG specialists for the development and maintenance of KGs, (ii) satisfy special requirements of KG development, such as (a) ensuring modularity and agility of KGs, (b) assessing and mitigating bias at schema and data levels. Toward this aim, the paper presents a methodology for the Collaborative and Hybrid Engineering of Knowledge Graphs (CHEKG), which constitutes a hybrid (schema-centric/top-down and data-driven/bottom-up), collaborative, agile, and iterative approach for developing modular and fair domain-specific KGs. CHEKG contributes to all phases of the KG engineering lifecycle: from the specification of a KG to its exploitation, evaluation, and refinement. The CHEKG methodology is based on the main phases of the extended Human-Centered Collaborative Ontology Engineering Methodology (ext-HCOME), while it adjusts and expands the individual processes and tasks of each phase according to the specialized requirements of KG development. Apart from the presentation of the methodology per se, the paper presents recent work regarding the deployment and evaluation of the CHEKG methodology for the engineering of semantic trajectories as KGs generated from unmanned aerial vehicles (UAVs) data during real cultural heritage documentation scenarios.

Bias identification and mitigation in the Twitter ecosystem has been lately researched towards achieving a more efficient utilization of the application by different stakeholders and for a wide area of purposes. Among these stakeholders, intelligence services worldwide, collectively called the Intelligence Community (IC), tend to use Twitter, supplementarily to their pre-existent disciplines, for monitoring areas of interest and identifying emerging social, political and security trends/threats. Over time, the IC has identified bias as the major obstacle in information analysis, thus it has developed scientific and empirical methods for bias mitigation, in parallel to those developed by the information and communication technology (ICT) and artificial intelligence (AI) community. As it becomes apparent, it is to both communities’ interest to accurately trace bias and ideally eradicate or moderate its effects. In this paper we draw systemic parallels between Intelligence Analysis (IA) and Twitter Analytics (TA), comparatively examine existing bias mitigating methodologies to pinpoint similarities/dissimilarities, and utterly investigate the feasibility of adapting and adjusting methodologies from the first field to the latter. Furthermore, we propose a novel framework for AI-augmented bias mitigation in the IC. Finally, we propose methods and tools, already adapted by the ICT community, for efficiently supporting bias mitigation methodologies adapted by the IC.