Semanlink - Knowledge Graph + Deep Learning AND Language Models + Knowledge

Specializing Static and Contextual Embeddings in the Medical Domain Using Knowledge Graphs: Let’s Keep It Simple - ACL Anthology (2022)

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2023-10-11 A propos

[2205.08184] SKILL: Structured Knowledge Infusion for Large Language Models

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2022-05-18 A propos

[2012.02558] Pre-trained language models as knowledge bases for Automotive Complaint Analysis

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2021-04-11 A propos

Language Models are Open Knowledge Graphs... but are hard to mine - Towards Data Science

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2021-01-17 A propos

Knowledge Graphs in NLP @ EMNLP 2020 | by Michael Galkin | Nov, 2020 | Medium

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2020-11-24 A propos

[2010.03496] Inductive Entity Representations from Text via Link Prediction

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BLP "BERT for Link Prediction". Central idea: **training an entity encoder with a
link prediction objective** (using the textual descriptions of entities when computing entity representations - hence not failing with entities unknown in training)

> a method for **learning representations
of entities**, that uses a **pre-trained Transformer** based
architecture as an entity encoder, and
**link prediction training on a knowledge graph
with textual entity descriptions**.

> using entity descriptions,
an entity encoder is trained for link prediction in
a knowledge graph. The encoder can then be used
without fine-tuning to obtain features for entity classification
and information retrieval

Cites [Xie et al](doc:2020/10/representation_learning_of_know) and [Kepler](doc:2020/11/1911_06136_kepler_a_unified_). They claim that their
objective targeted exclusively for link prediction (and not an objective that combines language modeling
and link prediction as Kepler)
performs better than Kepler's more complex one.

2020-11-03 A propos

[2010.11967] Language Models are Open Knowledge Graphs

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2020-10-26 A propos

[2004.07202] Entities as Experts: Sparse Memory Access with Entity Supervision

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2020-07-11 A propos

[2007.00849] Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge

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> a neural language model that includes **an explicit interface between symbolically interpretable factual information and subsymbolic neural knowledge.**... **The model can be updated without re-training by manipulating its symbolic representations**. In particular this model allows us to add new facts and overwrite existing ones.

> a **neural language model which learns to access information
in a symbolic knowledge graph.**

> This
model builds on the recently-proposed [Entities as
Experts](doc:2020/07/2004_07202_entities_as_expert) (EaE) language model (Févry et al., 2020),
which extends the same transformer (Vaswani
et al., 2017) architecture of BERT (Devlin et al., 2019) with an additional external memory for entities.
>
> After training EaE, the embedding associated
with an entity will (ideally) capture information
about the textual context in which that
entity appears, and by inference, the entity’s semantic
properties
>
> we include an additional
memory called a fact memory, which encodes
triples from a symbolic KB.
>
> This combination results in a
neural language model which learns to access information
in a the symbolic knowledge graph.

TODO:

- read again IBM's [Span Selection Pre-training for Question Answering](doc:2019/09/_1909_04120_span_selection_pre) ("an effort to avoid encoding general knowledge in the transformer network itself")
- compare with [[1907.05242] Large Memory Layers with Product Keys](doc:2019/07/_1907_05242_large_memory_layer)
- how does it relate with [[2002.08909] REALM: Retrieval-Augmented Language Model Pre-Training](doc:2020/12/2002_08909_realm_retrieval_a)?

2020-07-09 A propos

[1909.04164] Knowledge Enhanced Contextual Word Representations

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2020-05-13 A propos

[1906.07241] Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling

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2020-05-11 A propos

[1909.01066] Language Models as Knowledge Bases?

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2019-09-05 A propos

[1905.07129] ERNIE: Enhanced Language Representation with Informative Entities

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2019-08-05 A propos