SOTAVerified

Knowledge Graphs

A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.

Papers

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Showing 2851–2900 of 2974 papers

TitleStatusHype
URIR: Recommendation algorithm of user RNN encoder and item encoder based on knowledge graph—0
GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering—0
Deep Outdated Fact Detection in Knowledge Graphs—0
Deep Learning on Knowledge Graph for Recommender System: A Survey—0
SpaceE: Knowledge Graph Embedding by Relational Linear Transformation in the Entity Space—0
Graph Agent: Explicit Reasoning Agent for Graphs—0
Graph AI in Medicine—0
Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs—0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs—0
GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking—0
An Ad-hoc graph node vector embedding algorithm for general knowledge graphs using Kinetica-Graph—0
Deconstructing Depression Stigma: Integrating AI-driven Data Collection and Analysis with Causal Knowledge Graphs—0
Graph Edits for Counterfactual Explanations: A comparative study—0
Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains—0
Graph-Grounded LLMs: Leveraging Graphical Function Calling to Minimize LLM Hallucinations—0
Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations—0
Decision Knowledge Graphs: Construction of and Usage in Question Answering for Clinical Practice Guidelines—0
Graph Learning: A Survey—0
Distributed Representations of Entities in Open-World Knowledge Graphs—0
Graph Machine Learning in the Era of Large Language Models (LLMs)—0
Graph Masked Language Models—0
Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs—0
Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs—0
DBkWik++ -- Multi Source Matching of Knowledge Graphs—0
Graph Pattern Entity Ranking Model for Knowledge Graph Completion—0
A Case Study in Bootstrapping Ontology Graphs from Textbooks—0
Graph RAG for Legal Norms: A Hierarchical and Temporal Approach—0
GraphRAG under Fire—0
GraphRank Pro+: Advancing Talent Analytics Through Knowledge Graphs and Sentiment-Enhanced Skill Profiling—0
Graph Reasoning for Explainable Cold Start Recommendation—0
Graph Reasoning for Question Answering with Triplet Retrieval—0
DBee: A Database for Creating and Managing Knowledge Graphs and Embeddings—0
Data Science with Vadalog: Bridging Machine Learning and Reasoning—0
Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models—0
Graph Transformer—0
Data Exploration and Validation on dense knowledge graphs for biomedical research—0
Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective—0
Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue—0
A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning—0
GRAPHYP: A Scientific Knowledge Graph with Manifold Subnetworks of Communities. Detection of Scholarly Disputes in Adversarial Information Routes—0
GREASE: A Generative Model for Relevance Search over Knowledge Graphs—0
GreaseLM: Graph REASoning Enhanced Language Models—0
Dangling-Aware Entity Alignment with Mixed High-Order Proximities—0
Great~Truths~are ~Always ~Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models—0
Dangling-Aware Entity Alignment with Mixed High-Order Proximities—0
GREG: A Global Level Relation Extraction with Knowledge Graph Embedding—0
Grid-Based Projection of Spatial Data into Knowledge Graphs—0
Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers—0
DAGE: DAG Query Answering via Relational Combinator with Logical Constraints—0
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All datasetsMARS (Multimodal Analogical Reasoning dataSet)JerichoWorldWikiKG90M-LSCFB15k

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MarT_MKGformerMRR0.34—Unverified
2MKGformerMRR0.32—Unverified
3MarT_FLAVAMRR0.29—Unverified
4ViLBERTMRR0.29—Unverified
5IKRL (ANALOGY)MRR0.28—Unverified
6IKRLMRR0.27—Unverified
7ViLTMRR0.26—Unverified
8TransAEMRR0.22—Unverified
#ModelMetricClaimedVerifiedStatus
1WorldformerSet accuracy39.15—Unverified
2Q*BERTSet accuracy32.78—Unverified
3GATA-WSet accuracy24.06—Unverified
4WorldformerSet accuracy23.22—Unverified
5Seq2SeqSet accuracy14.29—Unverified
6RulesSet accuracy4.7—Unverified
#ModelMetricClaimedVerifiedStatus
1TransE-ConcatTest MRR85.48—Unverified
2ComplEx-ConcatTest MRR0.86—Unverified
3ComplEx-RoBERTaTest MRR0.72—Unverified
4TransE-RoBERTaTest MRR0.63—Unverified
#ModelMetricClaimedVerifiedStatus
1COMPLEXMRR0.59—Unverified