Papers Knowledge Graphs
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The Answer Path and the Grounding Instruction in LLM Question Answering over Knowledge Graphs
A graph retrieval-augmented generation pipeline chooses which triples to put in the prompt, a syntax to write them in, an order to write them in, and a sentence telling the model what to do with them. We vary all four ov…
Graph Question AnsweringKnowledge GraphsFrom State Synchronization to Cognitive Self-Evolution: An Operational Architecture for Cognitive Digital Twins
As Digital Twin (DT) systems evolve beyond state synchronization toward task-oriented and knowledge-driven operation, Cognitive Digital Twins (CDTs) have emerged as an extension that incorporates cognitive capabilities i…
Semantic CommunicationKnowledge GraphsCommonsense Reasoning in Computer Vision: Foundations, Recent Advancements, and Future Directions
Commonsense reasoning in computer vision encompasses integrating visual data and contextual knowledge, crucial for enhancing AI's understanding of everyday scenarios. This understanding not only improves machine learning…
Object RecognitionKnowledge GraphsContinual Graph Memory for Adaptive Recommendation under Intent Drift
This paper studies adaptive recommendation under intent drift, where feedback from each recommendation outcome can reveal whether the relational evidence used for ranking is useful, missing, or misleading. While Knowledg…
Knowledge GraphsPyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN
Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embedding models are trained relying on both positive and negative samples o…
Knowledge Graph EmbeddingTriple ClassificationKnowledge GraphsLink PredictionAdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA
Path-finding over knowledge graphs has become an effective way to ground LLM reasoning on multi-hop questions. However, biomedical QA introduces two distinct challenges that general-domain methods are not designed for: (…
Knowledge GraphsBEACON: Behavior-Anchored Cross-Source Knowledge Graph Construction for Cyber Threat Intelligence
Cyber threat intelligence (CTI) is foundational to modern cyber defense, yet much of it resides in unstructured reports whose volume and heterogeneity far exceed manual analysis, motivating research on automatically cons…
Semantic SimilarityKnowledge GraphsC-Unseen: Weak Signal Detection in Dynamic Temporal Knowledge Graphs via LLM Reasoning
Weak signals are early, low-visibility indicators that precede significant changes before those changes become established. Existing detection methods, based on keyword frequency, topic modeling, or untyped graph topolog…
Knowledge GraphsNeural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs
In recent years, transductive knowledge graph embedding models have been applied to tasks such as link prediction and query answering. Although knowledge graphs often contain rich numerical attributes, most embedding mod…
Knowledge Graph EmbeddingKnowledge GraphsLink PredictionSurgical Alignment in Knowledge Graph Training for Clinical Diagnosis with Large Language Models
Biomedical knowledge graphs (KGs) offer structured medical knowledge that can ground large language model (LLM) reasoning in clinical diagnosis application, yet how KG signal should be integrated into LLMs remains an ope…
Knowledge GraphsMulti-Granularity Context-Enhanced RAG over Multimodal Knowledge Graphs
Retrieval-augmented generation (RAG) is widely used to mitigate hallucination issues in large language models (LLMs) and multimodal large language models (MLLMs). In particular, knowledge graph (KG)-based RAG leverages s…
Information ExtractionKnowledge GraphsCross-Domain, Multi-Task Data-to-Text Generation without In-Domain Training Data
Structured data exists in many forms (tables, knowledge graphs, charts, and time series), and converting it into text may involve different generation tasks. However, most prior work on data-to-text (D2T) generation has …
Data-to-Text GenerationKnowledge DistillationKnowledge GraphsAutomated Construction of FAIR Digital Object Knowledge Graphs from Flat Cultural Heritage Records
The FAIR Digital Object (FDO) framework mandates that metadata attribute values be expressed as persistent identifiers (PIDs) wherever possible, to produce a fully machine-actionable graph in which every reference is res…
Knowledge GraphsAligning Biomedical Texts and Knowledge Graphs: A Systematic Comparison of Lightweight Alignment Strategies
Biomedical knowledge exists in two complementary but distinct forms: unstructured scientific literature and structured knowledge graphs (KGs). Aligning them is essential for knowledge grounding, evidence retrieval, and K…
Knowledge GraphsHierarchy-Aware Semantic Losses for Knowledge Graph Link Prediction
Knowledge graphs are often accompanied by ontological class hierarchies that encode valuable semantic information, yet many link prediction methods either ignore such hierarchies or incorporate them indirectly through ad…
Representation LearningGraph Neural NetworkKnowledge GraphsLink PredictionClinical Graph-JEPA: Predictive Patient-State Knowledge Graphs for Cognitive Decision Support
Clinical records contain rich evidence about patient state, but converting that evidence into reliable, structured knowledge graphs remains difficult because extraction errors, ontology mismatch, missing relations, and t…
Clinical KnowledgeKnowledge GraphsAdapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation
Sequential recommendation predicts the next item from a user's interaction history, but not every interaction is equally informative. Real logs combine persistent preferences with temporary needs, exploration, and incide…
Sequential RecommendationKnowledge GraphsSABET-QA: Temporal Knowledge Graph Question Answering
Question Answering over Temporal Knowledge Graphs (TKGQA) requires reasoning over time-sensitive facts, yet existing embedding-based methods struggle with multi-step queries due to single-pass reasoning pipelines. We pro…
Graph Question AnsweringKnowledge GraphsG-MARK: Grounded Multi-Agent Reasoning for Cooperative Driving via Knowledge Graphs
Autonomous driving systems must operate under partial observability, where safety-critical objects may be occluded or visible only to neighboring connected vehicles. Vehicle-to-vehicle cooperation can reduce this uncerta…
Trajectory ForecastingAutonomous DrivingKnowledge GraphsMissDiag: Diagnostic Evaluation of Incomplete-Knowledge Robustness in KGQA and KG-RAG
Knowledge graph question answering (KGQA) and knowledge-graph-based retrieval-augmented generation (KG-RAG) aim to ground answers in explicit graph evidence, but real-world knowledge graphs are often sparse, outdated, an…
Graph Question AnsweringKnowledge Graphs