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 2551–2600 of 2974 papers

TitleStatusHype
Few-Shot Knowledge Graph CompletionCode0
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link PredictionCode1
Joint Embedding Learning of Educational Knowledge Graphs—0
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood AggregationCode0
Rule-Guided Compositional Representation Learning on Knowledge GraphsCode0
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons LearnedCode0
Using Mapping Languages for Building Legal Knowledge Graphs from XML Files—0
Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs (Technical Report)—0
Pattern-based design applied to cultural heritage knowledge graphs—0
Inductive Relation Prediction by Subgraph ReasoningCode1
FAQ-based Question Answering via Knowledge Anchors—0
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language RepresentationCode0
Tensor Decomposition with Relational Constraints for Predicting Multiple Types of MicroRNA-disease AssociationsCode0
Decompressing Knowledge Graph Representations for Link PredictionCode0
Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering—0
A Re-evaluation of Knowledge Graph Completion MethodsCode0
Neural Graph Embedding Methods for Natural Language ProcessingCode0
Relation Adversarial Network for Low Resource Knowledge Graph Completion—0
Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge GraphsCode0
Unsupervised Domain Adaptation of Contextual Embeddings for Low-Resource Duplicate Question Detection—0
Path-Based Contextualization of Knowledge Graphs for Textual Entailment—0
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks—0
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning—0
CaRe: Open Knowledge Graph Embeddings—0
Reasoning Over Paths via Knowledge Base Completion—0
Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph ModelCode0
Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment—0
InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature InteractionsCode0
Learning to Update Knowledge Graphs by Reading News—0
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network—0
Improving Distantly-Supervised Relation Extraction with Joint Label Embedding—0
Relation Prediction for Unseen-Entities Using Entity-Word Graphs—0
DBee: A Database for Creating and Managing Knowledge Graphs and Embeddings—0
Node Embeddings for Graph Merging: Case of Knowledge Graph Construction—0
DRUM: End-To-End Differentiable Rule Mining On Knowledge GraphsCode0
A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?—0
Is it a Fruit, an Apple or a Granny Smith? Predicting the Basic Level in a Concept Hierarchy—0
Question Answering over Knowledge Graphs via Structural Query Patterns—0
Towards Combinational Relation Linking over Knowledge Graphs—0
Building Dynamic Knowledge Graphs from Text-based GamesCode0
Towards Learning Cross-Modal Perception-Trace Models—0
Hierarchical Attentive Knowledge Graph Embedding for Personalized Recommendation—0
Using a KG-Copy Network for Non-Goal Oriented DialoguesCode0
Efficiently Embedding Dynamic Knowledge Graphs—0
Aligning Cross-Lingual Entities with Multi-Aspect InformationCode0
GREASE: A Generative Model for Relevance Search over Knowledge Graphs—0
Knowledge-based Biomedical Data Science 2019—0
Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context ExpansionCode0
Commonsense Knowledge Base Completion with Structural and Semantic ContextCode0
Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations—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