Semanlink - Neural networks ; Graph AND Survey / Review

Printer friendly

Search Tag:

Search Doc:

Preferences...

Tag Cloud

AI: books & journals - Arxiv Doc - DeepMind - End-to-End Learning - Good - Graph neural networks - Graphs+Machine Learning - Knowledge Graphs - Neural-Symbolic Computing - NLP@MIT - Peter Bloem - Pre-Trained Language Models - Recommender Systems - Sample code - Structured knowledge - Survey / Review - Tutorial -

Neural networks ; Graph AND Survey / Review

Common descendants

12 Documents

[2306.08302] Unifying Large Language Models and Knowledge Graphs: A Roadmap

Tags:

2023-06-18 About

[2301.08210] Everything is Connected: Graph Neural Networks

Tags:

2023-01-21 About

Graph ML in 2023: The State of Affairs | by Michael Galkin | Towards Data Science

Tags:

2023-01-03 About

[2101.12294] Combining pre-trained language models and structured knowledge

Tags:

2022-03-25 About

[2011.02260] Graph Neural Networks in Recommender Systems: A Survey

Tags:

2020-11-11 About

[2010.05234] A Practical Guide to Graph Neural Networks

Tags:

2020-10-15 About

Graph Representation Learning Book - Will Hamilton

Tags:

2020-08-22 About

[2003.00330] Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

Tags:

2020-03-15 About

The knowledge graph as the default data model for learning on heterogeneous knowledge (2017)

Tags:

2019-08-22 About

[1901.00596] A Comprehensive Survey on Graph Neural Networks

Tags:

2019-07-15 About

Knowledge Graph Embedding: A Survey of Approaches and Applications - IEEE (2017)

Tags:

- "facts only" based embeddings
    - Translational Distance Models, distance-based scoring functions
       - TransE and co: entities and relations as points in vector space
           - TransH: relation-specific hyperplanes.
       - Gaussian Embeddings: entities and relations as random variables
    - Semantic matching models, similarity-based scoring functions
        - RESCAL (entities as a vector, relation as a matrix) and familiy
            - DistMult (relation as diagonal matrix)
            - Holographic Embeddings (HolE)
            - etc.
        - Matching with Neural Networks
            - SME, etc
- incorporating other info: entity types, relation paths,
textual descriptions, as well as logical rules
    - entity types
        - semantically smooth embedding
(SSE):  entities of the same type to stay close
to each other in the embedding space
        - type-embodied knowledge representation
learning (TKRL), which can handle hierarchical
entity categories and multiple category labels
- Applications in downstream tasks

2019-05-27 About

[1807.07984] Attention Models in Graphs: A Survey

Tags:

2018-11-14 About