Knowledge graphs (KGs) have emerged as foundational infrastructures in modern natural language processing (NLP) and recommender systems. However, long-tailed relational distributions and structural sparsity pose significant challenges to few-shot knowledge graph completion (FKGC). Under extremely low-resource scenarios, conventional neural architectures are prone to overfitting, leading to suboptimal generalization capabilities. To address these issues, we propose RANS-KAN-DRIA, a dynamic meta-learning framework incorporating three core components: (1) a Relation-Aware Neighbor Selector (RANS), which adaptively integrates relation-relevant neighborhood context with support instances to strengthen relational reasoning; (2) a lightweight KAN-based Relational Meta-Learner, which operates on low-dimensional entity-pair representations from the few-shot support set and constructs nonlinear and topology-aware relational features through Kolmogorov–Arnold decomposition; and (3) a Diffusion-Regularized Inner-loop Adapter (DRIA), which enables rapid parameter adaptation via gradient updates and stabilizes model performance using diffusion-generated hard negatives. Experimental results on two benchmark datasets demonstrate that RANS-KAN-DRIA achieves strong performance in few-shot scenarios. The proposed framework contributes a task-specific integration of relation-aware neighborhood selection, lightweight KAN-based relational meta-learning, and diffusion-regularized inner-loop adaptation for robust FKGC under extreme sparsity.
While existing knowledge bases maintain factual world knowledge, we view the social network of Twitter (now, X) as a complementary source of social world knowledge. Treating popular Twitter accounts as entities of general interest, our aim is to elicit useful semantic information about these entities that may be used by downstream applications. We first address the task of entity typing, such as determining whether an account belongs to a politician or a musical artist. To obtain labeled data, we align a subset of social entities with DBpedia and Wikidata, yielding an aligned dataset of over 20K Twitter entities annotated with 136 fine-grained semantic types. In learning models of type prediction, we process network and content-based evidence as entity embeddings, where we finetune the textual encoder on the semantic typing task. Our best classifier yields weighted F1 performance of 0.68 on set-aside labeled examples; manual inspection indicates that many apparent mismatches correspond to semantically plausible alternative types. Applying classification at large-scale, we obtain semantic embeddings and types for as many as 200K social entities, many of which are not covered by existing factual knowledge bases. Our study offers insights into the distribution of entity types across the ‘Twitter sphere’. Additionally, we show that content-based embeddings encode fine-grained entity semantics, as demonstrated on the key task of entity similarity assessment. We believe that the inferred semantic entity types and embeddings can serve various applications, such as social conversation and recommendation tasks using LLM agents, which involve social entities. We make all artifacts of this work available to the research community.
The Internet of Things enables continuous acquisition of physiological data through wearable and environmental sensors, creating new opportunities for early risk detection and timely clinical intervention. However, most IoT-based healthcare monitoring systems lack real-time semantic interpretation, limiting the transformation of raw sensor measurements into clinically meaningful and actionable information.This paper proposes an ontology-driven real-time architecture for semantic interpretation of IoT sensor data in healthcare monitoring scenarios. The system integrates streaming data ingestion, in-stream semantic classification, composite health condition inference, and scalable persistence within a unified pipeline. Physiological parameters such as heart rate, blood oxygen saturation, respiratory rate, heart rate variability, blood pressure, sleep duration, and physical activity are classified using guideline-based thresholds, while higher-level composite conditions are inferred from co-occurring semantic statuses.The approach is experimentally validated using a hybrid dataset composed of real-world wearable data and simulated high-throughput sensor streams. Results show that the complete pipeline processes 1000 observations with an average end-to-end latency of approximately 195 ms, enabling near real-time and deterministic semantic interpretation and visualization. A semantic correctness analysis based on predefined clinical scenarios confirms consistent and reproducible mapping between physiological measurements and ontology-defined health statuses.These findings demonstrate that ontology-based semantic interpretation can be operationalized within real-time IoT healthcare pipelines, significantly improving interpretability, responsiveness, and clinical relevance. The proposed architecture provides a practical and scalable foundation for semantically enriched patient health monitoring and supports future integration with standardized clinical data ecosystems.
The rapid growth of open and structured RDF data on the Web has promoted the development of dataset search as an important research topic. The core function of existing systems is ad hoc dataset retrieval (AHDR) based on the metadata of datasets, which contains limited information and often suffers from quality issues. To overcome the limitations, in this article, we systematically investigate content-based AHDR to exploit the actual RDF data in datasets. We address three main tasks of content-based AHDR with novel methods for handling the large size and complex structure of RDF data to facilitate dataset retrieval, deduplication, and snippet extraction. These methods are integrated into an online and open-source prototype called Caddie. The effectiveness and practicability of its components are evaluated on a public test collection and by a user study.
The growing demand for lithium-ion batteries (LIBs) in industries such as electric vehicles (EVs) and renewable energy storage underscores the need for tools that ensure transparent, sustainable, and compliant production processes. This paper presents the Battery Production Ontology (BPO), a comprehensive framework designed to standardize the representation and traceability of LIB production lifecycles. By modeling key aspects such as material flows, energy consumption, carbon emissions, and production activities, the BPO supports environmental impact assessments, supply chain transparency, and process optimization.The BPO is aligned with existing standards, including the EU’s digital battery passport requirements, ensuring interoperability across diverse systems. Developed using a structured methodology, the ontology underwent rigorous validation. Real-world case studies demonstrated its capacity to model emissions, trace materials, and represent production sequences, while quantitative assessments confirmed its scalability, reasoning efficiency, and accuracy for industrial applications. Additionally, the ontology integrates seamlessly with external standards like BONSAI, FOAF, Schema, and OWL Time, fostering semantic reuse and interoperability.By addressing the critical need for transparency and sustainability in LIB production, the BPO provides stakeholders with a robust tool to drive the green energy transition and achieve global sustainability goals.
The Belief–Desire–Intention (BDI) model is a cornerstone for representing rational agency in artificial intelligence and cognitive sciences. Yet, its integration into structured, semantically interoperable knowledge representations remains limited. This paper presents a formal BDI Ontology, conceived as a modular Ontology Design Pattern (ODP) that captures the cognitive architecture of agents through beliefs, desires, intentions, and their dynamic interrelations. The ontology ensures semantic precision and reusability by aligning with foundational ontologies and best practices in modular design. Two complementary lines of experimentation demonstrate its applicability: (i) coupling the ontology with Large Language Models (LLMs) via Logic Augmented Generation (LAG) to assess the contribution of ontological grounding to inferential coherence and consistency; and (ii) integrating the ontology within the Semas reasoning platform, which implements the Triples-to-Beliefs-to-Triples (T2B2T) paradigm, enabling a bidirectional flow between RDF triples and agent mental states. Together, these experiments illustrate how the BDI Ontology acts as both a conceptual and operational bridge between declarative and procedural intelligence, paving the way for cognitively grounded, explainable, and semantically interoperable multi-agent and neuro-symbolic systems operating within the Web of Data.
Despite many advances in knowledge engineering (KE), challenges remain in areas such as engineering knowledge graphs (KGs) at scale, automating tasks, and keeping pace with evolving domain knowledge. KE has used NLP demonstrating notable advantages in knowledge-intensive tasks, but the most effective use of generative AI to support knowledge engineers across the KE activities is still in its infancy. To explore how generative AI may enhance KE and change existing KE practices, we conducted a multi-method study during a KE hackathon. We investigated participants’ views on the use of generative AI, the challenges they face, the skills they may need to integrate generative AI into their practices, and how they use generative AI responsibly. We found participants felt LLMs could indeed contribute to improving efficiency when engineering KGs, but presented increased challenges around the already complex issues of evaluating KE task success. We discovered prompting to be a useful but undervalued skill for knowledge engineers working with LLMs, and note that NLP skills may become more relevant across more roles in KE workflows. Integrating generative AI into KE tasks needs to be done with awareness of potential risks and harms. Given the limited ethical training most knowledge engineers receive, solutions such as our proposed ‘KG Cards’ based on Data Cards could be a useful guide for KG construction. Our findings can support designers of KE AI copilots, KE researchers, and practitioners using advanced AI to develop trustworthy applications, propose new methodologies for KE and operate new technologies responsibly.
Semantic web technologies are sometimes perceived as difficult to adopt and use, and when developing data-driven applications a significant portion of the overall effort involves the writing of data access code. We propose, therefore, a technique to automatically generate data access layer code from data in semantic formats, abstracting away from developers data querying concerns. We also explore its use inside the EHRI project in order to support cultural heritage institutions in publishing their data in a systematic and sustainable way and we compare it against other libraries covering a similar purpose by means of a subjective analysis and a quantitative performance evaluation. Our results show how their design choices affect the overall usability and ultimately the final performance when integrated in larger applications. As a result, we deliver some recommendations of use for each of the tools depending on the developers’ needs. Thus, we see this work as a first step in exploring the benefits that these libraries can provide in abstracting semantic technologies and, thereby, promoting their adoption for data-driven applications.
Constructing comprehensive knowledge graphs requires the use of multiple ontologies in order to fully contextualize data into a domain. Ontology matching finds relationships between concepts interconnecting ontologies and creating a cohesive semantic layer. While the simple equivalence pairwise state of the art is well established, these simple mappings cannot provide full semantic integration of related but disjoint ontologies. Complex multi-ontology matching (CMOM) aligns three or more ontologies using composite logical expressions or transformations, establishing more nuanced connections and provenance along the ontological hierarchy.We present CMOMgen, the first end-to-end CMOM strategy that generates complete and semantically sound 1:n multi-ontology complex equivalence mappings, without establishing any restrictions on the number of target ontologies or entities. Retrieval-Augmented Generation selects relevant classes to compose the mapping and filters reference mappings to serve as examples to guide In-Context Learning in generating complex OWL expressions. The strategy was evaluated in three biomedical tasks with partial reference alignments, demonstrating consistent performance across settings, with a minimum F1-score of 63%. In addition, a manual evaluation of non-reference mappings showed that 46% of the mappings achieve the maximum score, further substantiating its ability to construct accurate, high-fidelity mappings.
Judicial efficiency is critical to social stability. However, in many countries worldwide, grassroots courts face substantial case backlogs, and judicial decisions remain heavily dependent on judges' cognitive efforts, with insufficient intelligent tools to enhance efficiency. To address this issue, we propose a highly efficient law article recommendation approach combining a Knowledge Graph (KG) and a Large Language Model (LLM). First, we construct a Case-Enhanced Law Article Knowledge Graph (CLAKG) to store current law articles, historical case information, and their interconnections, alongside an LLM-based automated construction method. Building on this, we propose a closed-loop law article recommendation framework integrating graph embedding-based retrieval and KG-grounded LLM reasoning. Experiments on judgment documents from China Judgments Online demonstrate that our method boosts law article recommendation accuracy from 0.549 to 0.694, outperforming strong baselines significantly. To support reproducibility and future research, all source code and processed datasets are publicly available on GitHub (see Data Availability Statement).
Solving the sparql query containment problem is of fundamental importance for the verification and optimization of sparql queries. With the increasing popularity of the Semantic Web and its applications, sparql query containment solvers face significant challenges: covering a wide range of language constructs, achieving high efficiency, and guaranteeing correctness. While language coverage and efficiency can be reliably evaluated by testing with relevant benchmarks, we need formal proof of correctness to ensure the trustworthiness of a tool.In this paper, we prove the correctness of SpeCS a highly efficient state-of-the-art query containment solver that supports reasoning about queries containing all commonly used sparql language constructs. We outline set semantics that cover the most common subset of the sparql language and give precise definitions of all fundamental sparql concepts. We briefly discuss the procedure used by SpeCS for reducing the query containment problem into a formal logical framework. We prove that this procedure is both sound and complete for conjunctive queries as well as for some important classes of non-conjunctive queries (queries containing the union operator, the optional operator, and subqueries). We consider soundness and completeness in both containment and subsumption forms. We also discuss the advantages of solver development driven by correctness proofs.
The paradigm of context-aware computing allows storing situational and environmental information in such a way that its interpretation can be done easily and more meaningfully. In turn, this understanding is used to anticipate users’ needs, and proactively provide them with situation-aware content and experiences. Whereas context-awareness has been investigated extensively in the computer science and IoT disciplines, it has been largely overlooked by the research community dealing with musical interfaces design. Existing musical instruments are not equipped with the ability to understand the context around them, namely who is the musician playing them, what musical activity is being conducted, as well as where and when. Enhancing musical instruments with context-awareness has the concrete potential to enable novel kinds of interactions between musicians and musical content in a large variety of situations, from playing alone to playing in a group, from music learning to music composition. To accomplish such a vision of intelligence embedded in musical instruments it is necessary to model the context around their users. In this paper, we present an ontology devised to represent the knowledge related to musicians and musical activities, the “Musician’s Context Ontology” (MUSICO) to facilitate the development of context-aware musical applications. There was no previous comprehensive data model for the domain of musicians’ context, nevertheless, the new ontology relates to several existing ontologies, including the Internet of Musical Things Ontology to represent Internet of Musical Things ecosystems and the Music Ontology that deals with the description of the music value-chain from production to consumption. This paper documents the design of the ontology and its evaluation with respect to specific requirements gathered from an extensive literature review and interviews with musicians. The utility of the ontology is demonstrated by a smartphone application that enables to search for musicians based on both textual and content-based musical queries. MUSICO can be accessed at: https://w3id.org/musico#.
Artificial Intelligence (AI), as a research field, is experiencing an intensive, dynamic period with major new trends and technologies emerging at an unprecedented pace. Generative AI systems, neuro-symbolic AI, agentic AI, AI regulations are just a few of the ground-breaking, ongoing changes in the field. Against this backdrop, it is natural for each community to embark in a “soul-searching” and strategic positioning activity: What is our role in AI? What are major current and long-term developments?In this introduction to the special issue “Opportunities for Knowledge Graphs in the AI Landscape - An Application-Centric Perspective”, we report on such a soul-searching activity for the knowledge graph community: authors of 17 papers reported on how they used KGs in the context of AI applications/tasks and reflected on the challenges encountered. Collectively, as will be evident from this short introductory piece, the papers reflect a vibrant and varied space of how KGs are used as a key component in the DNA of modern AI systems. They also identify several cross-cutting challenges, that lend themselves for an ambitious research agenda on KGs.
A question-answering (QA) system is essential for helping users retrieve relevant and accurate answers based on their queries. The precision of SPARQL query syntax generation is directly linked to the accuracy of the answers provided. Recently, many studies on knowledge graph-based natural language question-answering (KGQA) systems have leveraged the Neural Machine Translation (NMT) framework to translate input questions into SPARQL query syntax, a process known as Text-to-SPARQL. In NMT, cross-attention-based Transformers, ConvS2S, and BiLSTM models are commonly used for training. However, comparing the translation performance of these models is challenging due to their significant architectural differences. To address this issue, this paper integrates various encoder and cross-attention methods with a fixed LSTM decoder to form hybrid models, which are then trained and evaluated on QA systems. Beyond the hybrid models discussed, this study introduces an improved ConvS2S architecture featuring a Multi-Head Convolutional (MHC) encoder, designated as QAWizer_MHC. The MHC encoder incorporates the Transformer’s multi-head attention mechanism to compute dependencies within the input sequence. Additionally, the enhanced ConvS2S model captures local hidden features across different receptive fields within the input sequence. Experimental results demonstrate that QAWizer_MHC outperforms other models, achieving BLEU-1 scores of 76.52% and 83.37% on the QALD-9 and LC-QuAD-1.0 datasets, respectively. Furthermore, in end-to-end system evaluations on the same datasets, the model attained Macro F1 scores of 52% and 66%, respectively, surpassing other KGQA systems. The experimental findings indicate that even with limited computational resources and general embeddings, a well-designed encoder–decoder architecture that integrates cross-attention can achieve performance comparable to large pre-trained models.
Due to its extensive applicability in various downstream domains, few-shot named entity recognition (NER) has attracted increasing attention, particularly in areas where acquiring sufficient labeled data poses a significant challenge. Recent studies have highlighted the potential of knowledge graphs (KGs) in enhancing natural language processing (NLP) tasks. However, a comprehensive understanding of whether and how KGs can effectively improve the NER performance under low-resource conditions remains elusive. In this paper, for the first time, we quantitatively investigate the effects of different kinds of extra KG features for few-shot NER. We enable our analysis by aggregating extra KG features into an NER framework. Through extensive experiments, we find that incorporating class features yields the best performance. To fully explore the potential of class features from KGs, we propose a novel network architecture, named KGen, to jointly leverage KG-based knowledge from both the input sentence side and the label semantic side for few-shot NER.The efficacy of our proposed method is validated through extensive experiments on five challenging datasets.
In this paper, we present MobiRDF, a novel cloud-based approach designed for the efficient and scalable management of RDF data, enabling real-time sharing and editing. MobiRDF offers two main services: (i) Partial Replication of RDF Graphs: This service facilitates the selective replication of RDF graphs on mobile devices, addressing their inherent resource limitations. Our partial graph selector allows using only the useful data requested by the user from the RDF graph instead of storing the entire RDF graph, which enables efficient data storage and retrieval. (ii) Collaboration Protocol: This protocol provides synchronization mechanisms for collaborative work in a fully decentralized manner. It uses commutativity-based consistency model to maintain the consistency of the shared RDF graph, ensuring seamless collaboration among users. The heavier computational tasks, such as dynamic group management, synchronization merging, and reasoning processes, are managed in the Cloud, optimizing the performance of resource-constrained mobile devices. The key novelty of MobiRDF is its ability to ensure both syntactic and semantic consistency of shared RDF data, through reasoning processes using the Closed-World Assumption (CWA) for inferring new triples. Experimental evaluations show that MobiRDF is efficient in terms of network bandwidth and energy consumption, validating its effectiveness in real-world scenarios.
Collections are objects used to arrange, into a single unit, multiple data items that form a natural group. Different types of collections exist, due to different constraints based on whether or not they impose an order on their elements and whether or not they allow repetition of elements. Any of them are easily found in several domains of our everyday life. For instance, a deck of cards, the prime divisors of a number or the teams that compete in a championship can be seen as a collection. Thus, an effective modeling of collections is a recurring issue in information management.In the ontology design field, recurring modeling problems can be addressed by the use of Ontology Design Patterns (ODPs). In the case of collections, ODPs have been proposed for representing sequences, lists, sets and bags. However, none of these patterns are completely adequate for representing collections of ordered elements without repetition. In this paper we present an ODP for representing that notion, which we have named Permutation. Moreover, another ODP named ListOfPermutations is also introduced, which allows to represent how the order of a Permutation varies along time. Because not all constraints required by these ODPs can be represented in OWL 2, SHACL shapes have been used in their definitions.
Generative AI provides an innovative and exciting way to manage knowledge and data at any scale; for small projects, at the enterprise level, and even at a world wide web scale. It is tempting to think that Generative AI has made other knowledge-based technologies obsolete; that anything we wanted to do with knowledge-based systems, knowledge graphs or even expert systems can instead be done with Generative AI. Our position is counter to that conclusion.Our practical experience on implementing enterprise question answering systems using Generative AI has shown that Knowledge Graphs support this infrastructure in multiple ways: they provide a formal framework to evaluate the validity of a query generated by an LLM, serve as a foundation for explaining results, and offer access to governed and trusted data. In this position paper, we share our experience, present industry needs, and outline the opportunities for future research contributions.
Exploratory search on Knowledge Graphs (KGs) arises when a user needs to understand and extract insights from an unfamiliar KG. In these exploratory sessions, the users issue a series of queries to identify relevant portions of the KG that can answer their questions, with each query answer informing the formulation of the next query. Despite the widespread adoption of KGs, the needs of current KG exploration use cases are not well understood. This work presents the “Exploratory Search Workflows” (ESW) collection focusing on real-world exploration sessions of an open-domain KG, Wikidata, conducted by 57 M.Sc. Computer Engineering students in two advanced Graph Database course editions. This resource includes 234 real exploratory workflows, each containing an average of 45 SPARQL queries and reference workflows that serve as gold-standard solutions to the proposed tasks. The ESW collection is also available as an RDF graph and accessible via a public SPARQL endpoint. It allows for analysis of real user sessions, understanding query evolution and complexity, and serves as the first query benchmark for KG management systems for exploratory search.
