The paper introduces AVA, a framework that tests whether general NLP embeddings can differentiate logic-sensitive relational semantics in ontologies and knowledge graphs. AVA uses 171,007 contrastive triplets from 163 ontologies, each containing an ontology statement, a paraphrase, and a hard negative with contradictory meaning. Evaluation of over 25 embedding models shows significant limitations, with the best model achieving only 0.739 triplet accuracy and 0.135 for hard negatives; fine‑tuning helps but does not transfer well to downstream Semantic Web tasks.
General-purpose NLP embedding models perform well on linguistic tasks, but their ability to capture symbolic ontological structure remains unclear. We introduce AVA, a systematic framework for evaluat...
arXiv:2609.26610v1 Announce Type: new Abstract: Despite their outstanding performance on many NLP tasks, LLMs face serious challenges related to semantic abstraction. In this study, we are interested...
arXiv:2510. 09711v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond traditional embedding-based approaches.
The paper presents an automated framework for building ontology networks by discovering typed semantic links between concepts across domains. It uses domain‑adapted DistilBERT embeddings, clustering to prune candidates, and GPT‑4o for generating interpretable relationships, reducing 800k raw pairs to 95k high‑quality candidates. Human validation on 429 links shows an 80.19% precision and 0.890 F1, outperforming five similarity‑based baselines and demonstrating the need for LLM reasoning in ontology link construction.
arXiv:2606. 29180v1 Announce Type: new Abstract: A Knowledge Graph (KG) represents facts as structured triples and is widely used to organize relational knowledge across diverse domains.
The paper introduces a neuro‑symbolic framework for constructing knowledge graphs (KGs) that are grounded in an ontology. It combines open‑domain extraction, embedding‑based canonicalization of types and predicates, and a post‑extraction LLM‑based correction step to fix ontology violations, thereby reducing token usage and improving KG consistency. The resulting KGs support symbolic querying, as evidenced by the prevalence of SPARQL graph patterns in the extracted data.
arXiv:2502. 11491v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in natural language processing.
arXiv:2607. 15776v1 Announce Type: new Abstract: OWL ontologies provide a formal knowledge representation framework that enables semantic reasoning, and have been widely adopted across domains such as healthcare and bioinformatics.
arXiv:2610.33267v1 Announce Type: new Abstract: LLMs often answer semantically equivalent queries inconsistently, even when the underlying facts are known. We formalize consistency as semantic invari...
The paper demonstrates that Matryoshka Representation Learning (MRL) embeddings contain a natural coarse‑to‑fine hierarchy, with prefix length aligning to gold taxonomy granularity. By training a simple order‑embedding probe called PEEL on these frozen embeddings, the authors can recover high‑fidelity taxonomic DAGs from a concept vocabulary alone, achieving strong results on two OLLM benchmarks and transferring to new domains without target‑edge supervision. The study shows that pre‑trained retrieval embeddings encode a decodable granularity gradient, enabling a minimal probe to extract explicit parent‑child (is‑a) structure.
arXiv:2606. 15656v1 Announce Type: new Abstract: Modern artificial intelligence remains fundamentally divided between the continuous, probabilistic spaces of Foundation Models and the discrete, deterministic structures of Knowledge Graphs.
arXiv:2608. 07838v1 Announce Type: new Abstract: Large language models (LLMs) have increasingly supported response generation grounded in user-provided knowledge spanning heterogeneous structures.