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Arxiv Doc - Explainable AI - Good - Graphs+Machine Learning - Knowledge Graph Embeddings - Knowledge Graphs and NLP - NLP@MIT - Pre-Trained Language Models - Slides - Structured knowledge - Survey / Review -

Knowledge Representation ; Graphs+Machine Learning AND Survey / Review

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10 Documents

CIKM 2023 Tutorial--Reasoning beyond Triples: Recent Advances in Knowledge Graph Embeddings

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2024-04-01 About

[2308.00081] Towards Semantically Enriched Embeddings for Knowledge Graph Completion

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2023-08-02 About

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

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2023-06-18 About

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

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2023-01-03 About

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

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2022-03-25 About

[2006.13365] Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework

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2020-06-26 About

[1910.12507] A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?

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2020-05-04 About

[2004.14843] Knowledge Graph Embeddings and Explainable AI

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2020-05-04 About

A Comprehensive Survey of Knowledge Graph Embeddings with Literals: Techniques and Applications

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2020-04-29 About

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

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- "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