#knowledge-graphs

16 curated events

papersTODAY 04:00 UTC

Self-Demonstrations Improve LLM Schema-Ontology Mapping, arXiv Paper Finds

A new arXiv preprint examines how self-demonstrations, where a model generates its own worked examples before answering, affect the task of mapping relational database schemas onto a shared ontology. The authors frame the problem around semantic heterogeneity, obscure table and column names, missing metadata, and the abstraction gap that complicates enterprise knowledge integration. Results reported in the abstract suggest the self-demonstration approach is unexpectedly effective for this mapping task.

llm/ontology/knowledge-graphs/prompting

papersTODAY 04:00 UTC

arXiv paper proposes federated approach to knowledge graph question answering

A new arXiv preprint examines knowledge graph question answering in settings where no single organization holds the entire graph. The authors describe a scenario in which parties share entity identifiers but own disjoint sets of relations, so federated methods are needed to reason across them. The paper presents design lessons from a working system and an interactive prototype.

knowledge-graphs/federated-learning/question-answering/research

papersTODAY 04:00 UTC

Biquaternionic Space with Complex-Valued Attention for Temporal Knowledge Graph Completion

A new arXiv preprint proposes embedding temporal knowledge graphs in biquaternionic space, arguing that relying on a single geometric space limits how well models capture varied relational patterns. The method pairs that representation with complex-valued attention to score facts whose validity changes over time. The work targets the link-prediction task of inferring missing facts in evolving knowledge graphs.

knowledge-graphs/embeddings/attention/research

papersTODAY 04:00 UTC

DARE: Dialectical Agentic Reasoning for Structured Knowledge Fact Checking

A new arXiv paper introduces DARE, a method for verifying natural language claims by reasoning over structured evidence such as knowledge graphs. It builds on program-generation approaches in which large language models produce executable graph reasoning routines, adding a dialectical agentic layer to improve verification. The work targets fact checking tasks where evidence is organized in structured form rather than plain text.

fact-checking/agents/knowledge-graphs/reasoning

papersTODAY 04:00 UTC

arXiv paper proposes traceable multi-hop navigation for knowledge graph question answering

A new arXiv preprint introduces an approach for multi-hop knowledge graph question answering that emphasizes how a model travels through a graph rather than only the answer it produces. The authors argue that prior systems typically optimize for final-answer accuracy, leaving the relational evidence path unexplained. The work, titled "Theseus in the Graph," aims to make those navigation steps traceable.

knowledge-graphs/question-answering/interpretability/research

papersTODAY 04:00 UTC

Paper Argues Controlled Semantics Improve LLM Knowledge Graph Query Execution

A new arXiv paper examines how large language models receive data model concepts when translating natural language into database queries. The authors argue that conveying these concepts through controlled semantics rather than informal prompt prose, schema dumps, and examples leads to better query execution. The work focuses on natural language knowledge graph querying within the LLM context window.

knowledge-graphs/llm/query-execution/research

papersTODAY 04:00 UTC

arXiv paper argues Semantic Web's machine-reasoning goals went unmet

A new arXiv paper examines the history of the Semantic Web, noting that while its standards became part of scientific knowledge infrastructure, the machine understanding and reasoning capabilities it promised largely failed to materialize. The author argues that the field lost sight of certain goals and never possessed others, and proposes semantic knowledge technologies as a reframing of the original ambitions.

semantic-web/knowledge-graphs/research/arxiv

papersTODAY 04:00 UTC

Pipeline builds citation-grounded causal graphs from humanitarian reports

Researchers describe a processing pipeline that turns large volumes of heterogeneous humanitarian reporting into structured disaster storylines and causal knowledge graphs, with each extracted claim tied back to its source citation. The work targets the first hours of a crisis, when the amount of incoming information typically outstrips what human analysts can review. The approach is presented as a way to make automated synthesis of relief-related reporting more traceable and verifiable.

humanitarian/knowledge-graphs/causal-reasoning/nlp

papersTODAY 04:00 UTC

arXiv Paper Analyzes Query Costs in Vertically Partitioned Federated Knowledge Graphs

A new arXiv preprint examines the cost of answering multi-hop queries over knowledge graphs split across organizations that share an entity space but hold disjoint sets of relations, a setup the authors call vertical partitioning. Because such queries often need facts from multiple silos, the paper characterizes the resulting communication and computation costs. It is a research contribution rather than a product or model release.

knowledge-graphs/federated-learning/query-optimization/research

papersSEP 10 04:00 UTC

Answer paths and grounding instructions studied in LLM question answering over knowledge graphs

A graph retrieval-augmented generation pipeline must decide which knowledge graph triples to place in the prompt, what syntax to serialize them in, in what order, and with what instruction telling the model how to use them. This arXiv paper varies all four design choices across six large language models to assess how each factor affects question answering performance.

rag/knowledge-graphs/qa/llms

papersSEP 10 04:00 UTC

Paper Proposes Auto-Generating Evolution Trees of AI Methods from Citation Graphs

A new arXiv paper presents an approach that automatically builds evolution trees tracing how methods in a research subfield descend from earlier work, using citation graph data. The authors argue that manually written surveys can no longer keep pace with publication volume, while existing taxonomy-induction techniques capture only leaf categories and lack temporal awareness. The work aims to enable scalable, time-aware mapping of research lineages across AI fields.

citation-graphs/nlp/knowledge-graphs/research-tools

papersSEP 10 04:00 UTC

ViSR-KGC: Vision-Language Model Approach to Multimodal Knowledge Graph Completion

Researchers have introduced ViSR-KGC, a method that uses vision-language models to reason over visual subgraphs when filling in missing entities or relations in knowledge graphs. The approach extends knowledge graph completion to multimodal settings by combining textual graph structure with information from entity-associated images. The work is detailed in a revised preprint posted on arXiv.

knowledge-graphs/multimodal/vlm/research

papersSEP 11 04:00 UTC

AI Economist Agent Framework Combines RAG and Knowledge Graphs

A new arXiv paper proposes an agentic system that helps analysts work through economic and financial scenarios where historical data is thin. The framework pairs retrieval-augmented generation with knowledge graphs and large language models to pull together evidence from many sources and support risk assessment. It targets situations where emerging risks have little precedent, requiring analysts to synthesize disparate information.

agents/rag/knowledge-graphs/llm

papersSEP 12 04:00 UTC

EXYGEN framework enables conversational access to large knowledge graphs

Researchers present EXYGEN, an engine designed to let users query knowledge graphs through natural conversation rather than manual query languages. The work examines how well large language models translate ordinary text into SPARQL, the standard query language for graph databases, and then looks at applying those queries across large-scale graphs. The paper is a preprint posted to arXiv and has not yet been peer reviewed.

knowledge-graphs/text-to-sparql/llm/research

papersSEP 12 04:00 UTC

arXiv Paper Turns Knowledge Graph Queries Into Cultural Heritage Narratives

A new arXiv preprint describes a method for converting queries over cultural heritage knowledge graphs, such as the NFDI4Culture-KG, into narrative data stories that are easier for non-expert users to follow. The authors also use these generated narratives as a way to assess the quality of the underlying graph data. The work targets the gap between large triple stores covering art, music, inscriptions and historical events and the difficulty many users face in exploring them.

knowledge-graphs/cultural-heritage/research/data-quality

papersSEP 12 04:00 UTC

Reification Approach Enables Zero-Shot Link Prediction With Standard GNNs

A new arXiv paper proposes moving the transfer mechanism used by knowledge graph foundation models out of specialized architectures and into the data representation itself. The authors treat reification as a transferable vocabulary, allowing plain graph neural networks to perform zero-shot link prediction on previously unseen knowledge graphs. This approach aims to match dedicated models such as ULTRA without requiring architecture-level hard-coding of transfer behavior.

knowledge-graphs/zero-shot/gnn/research