https://github.com/mohbattharani/kerl
공식 코드KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models
Papers with Code 아카이브
Knowledge Graphs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/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
https://github.com/mu-data-science/gaf
공식 코드A Scalable Tool For Analyzing Genomic Variants Of Humans Using Knowledge Graphs and Machine Learning
https://github.com/muguruzawang/kgsum
공식 코드Multi-Document Scientific Summarization from a Knowledge Graph-Centric View
https://github.com/mutiann/ccc
공식 코드On the Role of Conceptualization in Commonsense Knowledge Graph Construction
https://github.com/n28div/llm-disjointness
공식 코드Enriching Ontologies with Disjointness Axioms using Large Language Models
https://github.com/nasrinkalanat/skg-sym
공식 코드https://github.com/navidmdn/logic_based_qa
공식 코드https://github.com/nddsg/KGMiner
공식 코드Discriminative Predicate Path Mining for Fact Checking in Knowledge Graphs
https://github.com/nec-research/emu-kg
공식 코드Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction
https://github.com/nec-research/fact-linking
공식 코드https://github.com/nec-research/recurrency_baseline_tkg
공식 코드History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting
https://github.com/neph0s/lmke
공식 코드https://github.com/nets4geeks/dockercomposedataset
공식 코드https://github.com/ngl567/GANA-FewShotKGC
공식 코드https://github.com/ngl567/cake
공식 코드CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion
https://github.com/ngl567/dhns
공식 코드Diffusion-based Hierarchical Negative Sampling for Multimodal Knowledge Graph Completion
https://github.com/ngl567/lcge
공식 코드Logic and Commonsense-Guided Temporal Knowledge Graph Completion
https://github.com/nheist/Cat2Ax
공식 코드https://github.com/nheist/caligraph-for-semrec
공식 코드https://github.com/nicolas-hbt/benchmark-sematk
공식 코드Sem@$K$: Is my knowledge graph embedding model semantic-aware?
https://github.com/nicolas-hbt/pygraft
공식 코드PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
https://github.com/nicolas-hbt/semantic-lossfunc
공식 코드https://github.com/nicolas-hbt/versatile-embeddings
공식 코드Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
https://github.com/ninecl/dekg-ilp
공식 코드Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction
https://github.com/ninibymilk/pmf-mmea
공식 코드Progressively Modality Freezing for Multi-Modal Entity Alignment
https://github.com/niraj17singh/linklogic
공식 코드LinkLogic: A New Method and Benchmark for Explainable Knowledge Graph Predictions
https://github.com/niteshroyal/solvinghardanalogyquestions
공식 코드Solving Hard Analogy Questions with Relation Embedding Chains
https://github.com/niva-knowledge-graph/kgs_and_effect_prediction_2020
공식 코드Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings
https://github.com/nju-websoft/AliNet
공식 코드Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.