This work targets a KG for Sophocles’ Antigone that supports two coupled uses: structured retrieval, through integrity and competency questions expressed in SPARQL over dramatic structure and interpretive annotations; and interactive exploration, through a lightweight read client that navigates lines across languages, shows scene context, and reports corpus statistics.
A production extraction layer that converts a live document stream into a validated knowledge graph aligned to a formal ontology, and improved search recall from roughly 70 to 95 percent with no false merges, and corrected seven classes of silent quality defect.
Vaibhav Dangaich, Kevin Lewis, Kundeshwar Pundalik· 0 citations
Constructing formal ontologies from domain documents requires simultaneously enforcing corpus grounding, vocabulary consistency, axiom-level expressivity, and end-to-end provenance, a combination no existing automatic system delivers. We present a seven-stage graph-grounded pipeline that converts domain documents into a complete, auditable Web Ontology Language (OWL) Terminological Box (TBox) without any unconstrained generation step. Documents are first encoded as Unified Discourse-Hypergraphs (UDH) capturing entity participation and discourse dependencies; subsequent stages transform this graph evidence into a class hierarchy, typed object and datatype properties, and restriction axioms, with Large Language Model (LLM) usage restricted to narrow, graph-grounded mediation tasks. A paired Assertional Box (ABox) population procedure grounds named individuals in the induced TBox, enabling SPARQL-based functional evaluation. Every emitted term carries a full decision chain from raw source passages through each pipeline stage, making the TBox directly auditable and suitable for targeted human refinement. Evaluated on the life insurance domain using two established benchmarks and a new 100-contract corpus spanning ten product types, our pipeline achieves strong results across all evaluation dimensions, outperforming direct and multi-agent LLM baselines on competency-question (CQ) coverage (0.85 vs. 0.63 and 0.62 on one term-life contract; 0.77 vs. 0.40 and 0.44 on another contract), while also attaining high keyphrase coverage comparable to a manually-constructed reference ontology and strong performance on structured gap-and-overlap reasoning, all without any manual TBox engineering. Ontology growth analysis provides evidence consistent with vocabulary saturation at scale, demonstrating that the pipeline produces stable, reusable domain representations from large document corpora.
Maruf Ahmed Mridul, A. Talukder, O. Seneviratne· 0 citations
Translating a natural-language question into a SPARQL query that can be executed against a large knowledge graph requires resolving lexical ambiguity, grounding surface terms in the target ontology, and producing graph patterns that are both syntactically valid and semantically faithful. We present an agentic text-to-SPARQL system that goes one step beyond static tool-using agents: a researcher agent that, after each round of inference on a validation set, proposes and tests changes to its own prompts, rules, and tool-orchestration code. We instantiate the loop on DBpedia, evolve nine successive versions of the agent driven by a low-cost reasoning model, and deploy the best-performing configuration with two stronger backbone models. The study yields three observations: (i) self-improvement converges quickly and then achieves 0.22 overall accuracy on the 2025 DBpedia validation set; (ii) the bottleneck is consistently in basic-graph-pattern predicate selection, not in SPARQL syntax or modifiers; and (iii) several benchmark items appear to penalise correct queries due to property ambiguity in DBpedia, suggesting that future Text-to-SPARQL benchmarks should be scored using a combination of machine translation and information retrieval metrics.
. Large Language Models (LLMs) are increasingly used to maintain persistent, domain-specific knowledge bases—a paradigm in which assertions must remain accurate as the field evolves. Existing staleness detection treats each assertion independently, missing a structural reality: when a foundational concept becomes outdated, every dependent concept inherits some degree of that staleness through ontology relationships. We present WikiMonitor-Onto, a lightweight propagation layer built on WikiMonitor that models staleness as a signal flowing through a domain ontology graph. We extract a concept graph of 642 nodes and 487 edges from 61 AI lecture documents, define three typed propagation relations ( is-a , depends-on , related-to ), and propagate staleness via weighted Breadth-First Search (BFS) with exponential hop decay. On a human-annotated gold standard of 62 concepts (25 indirect-stale, 37 fresh), the independent baseline detects zero indirect-stale concepts by construction, while WikiMonitor-Onto achieves precision 0.824 and recall 0.560 at the optimally tuned configuration ( λ = 0.30). Grid search reveals that is-a and depends-on carry equal optimal propagation weight (both 0.90). A sensitivity analysis confirms that propagation is robust to the choice of seed-value distribution, with F1 varying by only 0.10 (0.571–0.667) across four tested distributions. Propagation saturates at hop depth 1 under conservative thresholds, suggesting that one-hop propagation suffices for high-precision deployment.
Bailing Zhang· Journal of Advances in Artif...· 0 citations
A novel, interpretable metric for intrinsic KG quality assessment that measures how closely an automatically extracted graph approximates an"ideal"graph capturing the key noun phrases, predicate relations, and basic linguistic phenomena such as negation expressed in the source text is proposed.
Nipun Misra, Vikranth Udandarao, Aanchal Gupta et al.· 0 citations
A two-stage LLM-assisted workflow for French maintenance regulations is presented: ontology engineering from a SEMLEG-based core ontology, followed by construction of an ontology-grounded French legal knowledge graph.
Génesis Montenegro, M. Billami, Catherine Faron et al.· 0 citations