13 articles tagged with #semantic-web. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce ContextEA, an advanced foundation model for entity alignment across knowledge graphs that significantly improves upon existing approaches by better leveraging structural context. The model demonstrates superior transfer capabilities to unseen knowledge graph pairs, outperforming finetuned baselines without requiring task-specific adaptation.
Researchers have developed a framework for evaluating fuzzy quantification queries over OWL ontologies and knowledge graphs, enabling retrieval of individuals matching Type I or Type II fuzzy quantified expressions. The system is agnostic to quantifier types and data sources, with Q2S2 released as an open implementation for future research.
This paper addresses ABox abduction in description logic EL_bot by investigating hypotheses that satisfy multiple desired properties simultaneously under repair semantics. The research demonstrates that combining signature restrictions with optimality criteria often does not increase computational complexity, advancing the theoretical foundations of knowledge base repair.
Researchers propose a formal temporal modeling framework using the LRMoo ontology to represent how legal norms evolve over time, enabling precise point-in-time reconstruction of legal texts. The approach treats legal amendments as event-centric chains of versioned works, addressing a critical gap in automated legal processing that could improve AI reliability in legal applications.
MetaConfigurator introduces an AI-assisted RDF Authoring View that enables researchers to convert structured JSON, YAML, and CSV data into semantic RDF format through an integrated web interface. The tool bridges conventional data management with Semantic Web technologies, demonstrated using laboratory synthesis experiment data, and includes features like ontology-aware IRI auto-completion and AI-generated SPARQL queries.
SchemaForge, a new AI framework, improves text-to-SPARQL query generation over heterogeneous knowledge graphs by using schema-grounded validation. The system achieves 11.5 percentage points higher accuracy than existing baselines across four benchmarks, demonstrating practical advances in natural language to database query translation.
Researchers introduce OLG++, an enhanced framework for representing regulatory and legal rules using semantic graph structures. The model extends the original Obligation Logic Graph with spatial, temporal, and defeasibility constructs, demonstrating improved expressiveness for municipal regulations through food-business compliance examples.
BoxLitE introduces a new knowledge base embedding model for DL-Lite ontologies that leverages convex optimization to represent hierarchical conceptual knowledge. The research demonstrates that faithful embeddings can be mathematically formulated as convex optimization problems, combining classical knowledge graph embeddings with ontology-based reasoning.
Researchers propose an ontology-driven framework called CCAI (Contextual Collaboration AI Ontology) to document and trace human-AI interactions, converting ephemeral prompt-response exchanges into structured, queryable collaboration records. The framework addresses transparency and accountability gaps in AI-assisted workflows by explicitly modeling tasks, agent roles, resources, and constraints within a machine-interpretable vocabulary.
Researchers propose a formal framework for describing knowledge graph affordances to agents, extending decades-old semantic web service standards to address modern KG discovery and composition challenges. The framework introduces the Agentic Affordance Profile (AAP), a metadata layer that enables principled selection and failure diagnosis by specifying what agents can prove from a knowledge graph and under what epistemic conditions.
This academic paper addresses inconsistency handling in prioritized knowledge bases by analyzing the computational complexity of query entailment and repair enumeration under three optimal repair notions (global, Pareto, completion). The work establishes formal connections between optimal repairs and argumentation theory extensions, offering theoretical foundations for knowledge base consistency management.
Researchers introduce Autonomous FAIR Digital Objects (aFDOs), a framework that transforms static scientific data into self-governing entities capable of validating evidence, resolving contradictions, and updating confidence independently. The system combines semantic web standards with Byzantine-fault-tolerant consensus mechanisms to enable scientific knowledge to persist and evolve beyond institutional stewardship.
Researchers propose new methods for real-time reasoning over streaming data using Description Logic, addressing challenges of high-velocity data processing and inconsistency handling. The work introduces incremental algorithms for maintaining data materialization over sliding windows, with applications in OWL2 RL reasoning systems.
