https://github.com/mberr/ea-sota-comparison
공식 코드A Critical Assessment of State-of-the-Art in Entity Alignment
Papers with Code 아카이브
Knowledge Graphs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 4
- 보존된 평가 기록
- 19
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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mberr/rank-based-evaluation
공식 코드On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
https://github.com/mbrockmeier/KnowledgeGraphOnDemand
공식 코드On-Demand and Lightweight Knowledge Graph Generation -- a Demonstration with DBpedia
https://github.com/md-k-sarker/KG-Cmpl-dataset
공식 코드https://github.com/meaningful96/satkgc
공식 코드https://github.com/medinaandresel/eomqa
공식 코드Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs
https://github.com/meettyj/gnp
공식 코드https://github.com/megagonlabs/cypherbench
공식 코드CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era
https://github.com/memexproject/memex-kg
공식 코드Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding
https://github.com/menggehe/DRAW
공식 코드https://github.com/merialdo/research.lpca
공식 코드Knowledge Graph Embedding for Link Prediction: A Comparative Analysis
https://github.com/mianzg/kgbiasdetec
공식 코드https://github.com/michiyasunaga/dragon
공식 코드https://github.com/michiyasunaga/qagnn
공식 코드QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
https://github.com/microsoft/DEKCOR-CommonsenseQA
공식 코드Fusing Context Into Knowledge Graph for Commonsense Question Answering
https://github.com/mie-lab/ckg-traffic-forecasting
공식 코드Context-aware knowledge graph framework for traffic speed forecasting using graph neural network
https://github.com/migalkin/NodePiece
공식 코드NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
https://github.com/migalkin/StarE
공식 코드https://github.com/migalkin/rwl
공식 코드https://github.com/mingchaoqi/skill_transfer
공식 코드https://github.com/minhvuong2000/llmreasoncert
공식 코드Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs
https://github.com/minqi/wordcraft
공식 코드WordCraft: An Environment for Benchmarking Commonsense Agents
https://github.com/miralab-ustc/qe-cone
공식 코드ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs
https://github.com/miulab/kg-survey
공식 코드https://github.com/ml4ai/scenario-context
공식 코드When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context
https://github.com/mlii0117/dcl
공식 코드Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation
https://github.com/mmorris44/gnn-sound-rule-extraction
공식 코드Relational Graph Convolutional Networks Do Not Learn Sound Rules
https://github.com/modultechnology/affective-models-corpora
공식 코드Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications
https://github.com/mohbattharani/kerl
공식 코드KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models
https://github.com/moonwalker1997/vsannkgalignment
공식 코드Aligning Knowledge Graphs Provided by Humans and Generated from Neural Networks in Specific Tasks
https://github.com/moritzblum/literalevaluation
공식 코드Numerical Literals in Link Prediction: A Critical Examination of Models and Datasets
https://github.com/mpietrasik/hb
공식 코드https://github.com/mpietrasik/smich
공식 코드https://github.com/msorkhpar/wiki-entity-summarization
공식 코드https://github.com/mstubbemann/Orometric-Methods-in-Bounded-Metric-Data
공식 코드https://github.com/mstubbemann/lg4av
공식 코드LG4AV: Combining Language Models and Graph Neural Networks for Author Verification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.