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.

In the modern information society, learning is no longer just about obtaining factual knowledge, but more about general skills on how and where to apply available knowledge and obtain new knowledge in order to solve new problems. Such skills include abilities to connect and organize ideas, fill gaps in knowledge structures, evaluate evidence, argue with new information, test and modify, predict, clarify, generate questions, learn new concepts, make unexpected connections, reflect, analyze, synthesize and loop back. This work presents the Immersion framework, a digital ecosystem for adaptive smart learning environments for interactive mixed reality driven methods to foster learners' self-regulating skill development.

The aim of this paper is to present authors' proposal regarding semantic interoperability for interconnected and semantically coordinated smart entities in a Web of Things. More specific, the paper presents a use case scenario and requirements related to the semantic registration, coordination and retrieval of smart entities. Motivated by these, the paper accentuates the need for, and emphasizes, a framework of Semantic Smart Gateways (SSGF) in the Semantic Web of Things (SWoT), proposing an ontology learning and an ontology alignment method respectively.

Ontology engineering has been fully or partially practiced by knowledge engineers or knowledge workers, towards delivering either fully fledged conceptualizations of domains or providing lightweight ontology versions for less demanding but more frequent knowledge tasks. Domain-specific information can be shaped into ontologies either manually or (semi-)automatically using ontology learning techniques. The aim of the paper is to present a novice ontology learning approach that automatically constructs kick-off and useful ontologies from query logs. We place tasks related to the proposed learning approach in all phases of an ontology engineering life-cycle. By providing knowledge workers a useful kick-off ontology that is automatically built from ¿their needs (i. e. users' search interests)¿ in order to address ¿their needs (i. e. use of the kick-off ontology to query data precisely)¿, an approach that contributes as an incentive in the semantic content creation bottleneck is introduced.

Ontology matching is still one of the hottest topics of Semantic Web research. The aim of this position statement is three-fold. Firstly, to briefly update the research community about the ldquowhere are we nowrdquo in ontology matching. Secondly, to trigger discussion on dilemmas and critical questions as these were recently identified in the latest ontology-matching-related research events. Thirdly, to comment on visible challenges that may influence the future of this hot topic and consequently the Semantic Web research in general, pointing on the ldquowhere shall we gordquo in the near future.

One of the most challenging problems in grid environments concerns the matchmaking between resource requests and offers. As it happens in the physical economy, grid economy must be supported by services that locate resources based not only on their characteristics, but also on market-related properties, offerspsila and requestspsila properties and constraints, as well as on declarative specifications of peerspsila (providers and consumers) features. Resource retrieval in the context of a grid economy extends the notion of resource matchmaking to the process of discovering those markets that trade resources through market orders. This paper describes an ontology that represents resource orders (offers and requests) in a market-oriented resource retrieval process, showing preliminary results of its utilization for the retrieval of traded resources.