Semanlink - Survey / Review AND Structured knowledge

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Aidan Hogan - Arxiv Doc - Axel Polleres - Data integration - End-to-End Learning - Explainable AI - Good - Graphs+Machine Learning - Knowledge Graph Embeddings - Knowledge Graphs - Knowledge Graphs and NLP - NLP@MIT - Ontology-based Data Access - Peter Bloem - Pre-Trained Language Models - Slides - Structured knowledge - Survey / Review -

Survey / Review AND Structured knowledge

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16 Documents

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

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2024-04-01 A propos

[2308.00081] Towards Semantically Enriched Embeddings for Knowledge Graph Completion

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2023-08-02 A propos

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

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2023-06-18 A propos

[2302.05019] A Comprehensive Survey on Automatic Knowledge Graph Construction

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2023-02-15 A propos

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

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2023-01-03 A propos

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

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2022-03-25 A propos

[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 A propos

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

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2020-05-04 A propos

[2004.14843] Knowledge Graph Embeddings and Explainable AI

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2020-05-04 A propos

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

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2020-04-29 A propos

Knowledge Graphs @ ICLR 2020 - Michael Galkin - Medium

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2020-04-28 A propos

[2003.02320] Knowledge Graphs

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2020-03-07 A propos

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

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2019-08-22 A propos

Knowledge Graphs: Technical Review

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2019-07-15 A propos

Virtual Knowledge Graphs: An Overview of Systems and Use Cases | MIT Press Journals (2019)

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2019-05-28 A propos

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 A propos