arXiv AI
Jun 11
Towards a Bridge Layer Between Bibliographic and Formalized Mathematical Knowledge
arXiv:2606. 11430v1 Announce Type: cross Abstract: Mathematical knowledge is split between bibliographic databases (e.
Making Mathematical Knowledge Explainable, Accessible and Interoperable Through Large Language Model Integration
arXiv:2607. 24512v1 Announce Type: new Abstract: Mathematical models are central to formalizing research problems, yet their documentation often falls short of FAIR principles.
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arXiv AI
1d ago
Valhalla: A Layered Knowledge-State and Service-Governance Framework for Long-Term Scientific Knowledge Work
arXiv:2608. 15193v1 Announce Type: cross Abstract: As large language model (LLM) agents are increasingly adopted in scientific research, external knowledge bases, knowledge graphs, and long-term memory have improved information retrieval and task continuity.
arXiv AI
Jul 21
Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation
arXiv:2607. 18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata.
arXiv Machine Learning
2d ago
AutoSchema: Live Schema Grounding for Agentic Text-to-Sparql over Heterogeneous Knowledge Graphs
arXiv:2608. 14228v1 Announce Type: new Abstract: Life science knowledge graphs make large collections of structured data available through SPARQL, but each resource uses its own schema, identifiers, and links.
arXiv AI
Jun 30
Categorizing Mathematical Concepts with LLM Voting Ensembles in Mathswitch
arXiv:2606. 28815v1 Announce Type: cross Abstract: Mathswitch is an open-source project that imports mathematical concept records from sources such as Wikidata, Wikipedia, MathWorld, Encyclopedia of Mathematics, nLab, ProofWiki, and Agda-Unimath, and links records that refer to the same concept.
arXiv AI
Jul 29
Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs
arXiv:2607. 25959v1 Announce Type: cross Abstract: Wikipedia and Wikidata are widely used for information access, LLM pre-training, and retrieval-augmented generation.
arXiv AI
Jun 8
MetaConfigurator: AI-Assisted RDF Authoring from JSON Data
arXiv:2606. 07094v1 Announce Type: cross Abstract: Scientific workflows increasingly generate structured JSON data that is easy to exchange but difficult to interpret consistently across systems due to lacking semantic interoperability.
arXiv AI
Jul 15
FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis
arXiv:2607. 11464v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) addresses the limitations of Large Language Models (LLMs) when providing responses to domain-specific questions.
arXiv AI
5d ago
SchemaLink: An Intelligent Web Editor for LinkML Schema Curation
arXiv:2608. 12529v1 Announce Type: cross Abstract: Motivation: LinkML is a suitable language for the representation of the structural and content constraints of different kinds of biomedical data.
arXiv AI
Aug 11
H2: A Dual Hybrid Semantic Data Lake Architecture for Medical Data Harmonization with Human-In-the-Loop verified, LLM Driven Metadata Annotation System
arXiv:2608. 08056v1 Announce Type: new Abstract: Medical data, by its nature, exhibit a high degree of heterogeneity on multiple levels ranging from (a) different modalities like images, text and time series, (b) diverse tabular schemata introduced by institutions and (c) completely unstructured textual information data provided by healthcare professionals.
arXiv AI
Jul 17
SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation
arXiv:2607. 14494v1 Announce Type: new Abstract: Complex knowledge base question answering (KBQA) is commonly approached through either information retrieval over a question-specific subgraph or semantic parsing into an executable logical form.
arXiv AI
Jul 17
ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System
arXiv:2607. 14178v1 Announce Type: new Abstract: Recent advances in Large Language Models have fueled autonomous AI agents capable of tackling complex scientific tasks, yet existing automated research systems remain predominantly focused on empirically driven domains with quantitative benchmarks, leaving theory-driven discovery, particularly in mathematically grounded disciplines requiring rigorous proofs and synthesis of domain knowledge, largely underexplored.