Semanlink - AI + Knowledge Bases AND Graph Embeddings

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

Integrate LLM workflows with Knowledge Graph using Neo4j and APOC | by Tomaz Bratanic | Jun, 2023 | Towards Data Science

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2023-06-09 A propos

Daniel Daza sur Twitter : "BioBLP, a method for learning embeddings on multimodal knowledge graphs...."

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2023-06-07 A propos

[2110.08151] mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models

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[Ikuya Yamada sur Twitter : "Is entity representation effective to improve multilingual language models?..."](doc:2022/04/ikuya_yamada_sur_twitter_is_)

> Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual alignment information from Wikipedia entities. However, **existing methods only exploit entity information in pretraining and do not explicitly use entities in downstream tasks**. In this study, we explore the **effectiveness of leveraging entity representations for downstream cross-lingual tasks**.
>
> the key insight is that incorporating entity representations into the input allows us to extract more language-agnostic features.

[Github](https://github.com/studio-ousia/luke)

> Entity representations are known to enhance
language models in mono-lingual settings
(Zhang et al., 2019: [ERNIE](tag:ernie.html); Peters et al., 2019:  [[1909.04164] Knowledge Enhanced Contextual Word Representations](doc:2020/05/1909_04164_knowledge_enhanced); Wang et al.,
2021 [[1911.06136] KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](doc:2020/11/1911_06136_kepler_a_unified_); Xiong et al., 2020; Yamada et al., 2020: [[2010.01057] LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](doc:2020/11/2010_01057_luke_deep_context))
presumably by introducing real-world knowledge.
We show that using entity representations facilitates
cross-lingual transfer by providing languageindependent
features.
>
> Multilingual extension of LUKE. The model is trained with the multilingual
masked language modeling (MLM) task as well
as the masked entity prediction (MEP) task with
Wikipedia entity embeddings

> We investigate two ways of using the entity representations
in cross-lingual transfer tasks:
> 1. perform
entity linking for the input text, and append
the detected entity tokens to the input sequence.
The entity tokens are expected to provide language independent
features to the model
> 2. use the entity
[MASK] token from the MEP task as a languageindependent
feature extractor.

2022-04-17 A propos

Ikuya Yamada sur Twitter : "Is entity representation effective to improve multilingual language models?..."

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2022-04-13 A propos

Studio Ousia sur Twitter : "Now using LUKE is easier than ever!" / Twitter

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2022-03-15 A propos

[1911.03681] E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT

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> way of **injecting factual knowledge about entities into the pretrained BERT model**.

(Feeding entity vectors
into BERT as if they
were wordpiece vectors without additional encoder
pretraining)

>
> **We align [Wikipedia2Vec](tag:wikipedia2vec) entity vectors (Yamada et al., 2016) with BERT's native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors**. The resulting entity-enhanced version of BERT (called E-BERT) is similar in spirit to [ERNIE](tag:ernie) (Zhang et al., 2019) and [KnowBert](tag:knowbert) (Peters et al., 2019), but it **requires no expensive further pretraining of the BERT encoder**.
>
> Our vector space alignment strategy is inspired by
cross-lingual word vector alignment

Related work on Entity-enhanced BERT:

> ([ERNIE](doc:2019/08/_1905_07129_ernie_enhanced_la) and [Knowbert](doc:2020/05/1909_04164_knowledge_enhanced)) are based on the design principle
that BERT be adapted to entity vectors. They introduce
new encoder layers to feed pretrained entity
vectors into the Transformer, and they require additional
pretraining to integrate the new parameters.
In contrast, E-BERT’s design principle is that entity
vectors be adapted to BERT.
>
> Two other knowledge-enhanced MLMs are [KEPLER](doc:2020/11/1911_06136_kepler_a_unified_)
(Wang et al., 2019c) and K-Adapter (Wang
et al., 2020)... Their factual knowledge
does not stem from entity vectors – instead, they
are trained in a multi-task setting on relation classification
and knowledge base completion.

Not to be cofounded with [[2009.02835] E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce](doc:2020/12/2009_02835_e_bert_a_phrase_a)

2021-01-12 A propos

[2010.01057] LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

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2020-11-26 A propos

raphaelsty/ckb: Contextual knowledge bases

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2020-11-09 A propos

Text-Enhanced Representation Learning for Knowledge Graph (IJCAI 2016)

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2020-11-03 A propos

[1911.06136] KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation

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2020-11-03 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

[1802.05930] Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing

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2020-10-02 A propos

SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions (AAAI 2017)

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2020-10-02 A propos

Representation learning of knowledge graphs with entity descriptions (AAAI 2016)

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"Description-Embodied Knowledge Representation Learning" (DKRL)

> In most knowledge graphs there are usually concise descriptions for entities, which cannot be well utilized by existing methods... Experimental results on real-world datasets show that, our method outperforms other baselines on the knowledge graph completion and entity classification tasks, especially under the zero-shot setting, which indicates that **our method is capable of building representations for novel entities according to their descriptions**. [Source code on github](https://github.com/xrb92/DKRL).

For fact triples: TransE. Meanwhile, given an entity we will also learn to maximize
the likelihood of predicting its description (using either CBOW or CNN encoder) (head + relation = tail, also in "text space")

Two types of representations for entities: structure-based
representations and description-based representations. They are learned simultaneously into the same
vector space but not forced to be unified **so that novel
entities with only descriptions can be represented**.

2020-10-02 A propos

Representing Text for Joint Embedding of Text and Knowledge Bases (EMNLP 2015)

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2020-09-14 A propos

[1806.06478] Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment

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2020-09-06 A propos

[1909.01259] Neural Attentive Bag-of-Entities Model for Text Classification

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A model that performs **text classification using entities in a knowledge base**.

> Entities provide unambiguous and relevant semantic signals that are beneficial for capturing semantics in texts. We combine **simple high-recall entity detection based on a dictionary** (word->list of entities), to detect entities in a document, with a novel neural **attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities**.

2 steps:

1. Entity detection
2. Classification using the detected entities (+text) as inputs

Regarding entity linking, a local model which uses cosine
similarity between the embedding of the target
entity and the word-based representation of
the document to capture the relevance of an entity
given a document.

Embeddings from the KB: computed using [#Wikipedia2Vec](tag:wikipedia2vec) (similar words and entities
close to one another in a unified vector space)

Model using attention, with 2 features :

- cosine similarity between the
embedding of the entity and the word based
representation of the document
- the probability that the entity
name refers to the entity in KB.

Somewhat [related](doc:2020/01/investigating_entity_knowledge_)

### Conclusion:

>a neural
network model that performs text classification using
entities in Wikipedia. We combined simple
dictionary-based entity detection with a neural attention
mechanism to enable the model to focus
on a small number of unambiguous and relevant
entities in a document.

2020-09-02 A propos

[1812.06280] Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia

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2020-09-02 A propos

[1807.08447] LinkNBed: Multi-Graph Representation Learning with Entity Linkage

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2020-05-11 A propos

Bootstrapping Entity Alignment with Knowledge Graph Embedding | IJCAI

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2020-05-11 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

Deepak Nathani | Pay Attention, Relations are Important

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2020-04-30 A propos

[1906.01195] Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

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2020-04-30 A propos

Iterative Entity Alignment with Improved Neural Attribute Embedding

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2020-04-29 A propos

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

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2020-04-29 A propos

[2001.09522] TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

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how to add a set of new concepts to an existing taxonomy.

[Tweet](https://twitter.com/mickeyjs6/status/1253772146142216194?s=20) [GitHub](https://github.com/mickeystroller/TaxoExpan)

> we study the taxonomy expansion task: given an
existing taxonomy and a set of new emerging concepts, we aim
to automatically expand the taxonomy to incorporate these new
concepts (without changing the existing relations in the given taxonomy).

> To the best of our knowledge, this is the first study on **how to
expand an existing directed acyclic graph (as we model a taxonomy
as a DAG) using self-supervised learning**.

Self-supervised framework, the existing taxonomy being used as training data: it learns a model to predict whether a query concept is the direct hyponym of an anchor concept.

> 2 techniques:
>
> 1. a **position-enhanced graph neural network that encodes the local structure of an anchor concept** in the existing taxonomy,
> 2. a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data.

Regarding 1: uses [GNN](/tag/graph_neural_networks.html) to model the "ego network" of concepts (potential “siblings”
and “grand parents” of the query concept).

> Regular
GNNs fail to distinguish nodes with different relative positions to
the query (i.e., some nodes are grand parents of the query while
the others are siblings of the query). To address this limitation, we
present a simple but effective enhancement to inject such position
information into GNNs using position embedding. We show that
such embedding can be easily integrated with existing GNN architectures
(e.g., [GCN](/tag/graph_convolutional_networks) and GAT) and significantly boosts the
prediction performance

Regarding point 2: uses InfoNCE loss, cf. [Contrastive Predictive Coding](/doc/?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1807.03748)

> Instead of predicting
whether each individual ⟨query concept, anchor concept⟩ pair
is positive or not, we first group all pairs sharing the same query
concept into a single training instance and learn a model to select
the positive pair among other negative ones from the group.

(Hum, ça me rappelle quelque chose)

> assume each concept (in existing taxonomy + set of new concepts) has an initial embedding
vector learned from some text associated with this concept.

To keep things tractable, only attempts to find a single parent node of each new concept.

2020-04-25 A propos

[1911.02168] CoKE: Contextualized Knowledge Graph Embedding

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2020-03-22 A propos

[1902.10197] RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

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2020-03-03 A propos

[1905.07129] ERNIE: Enhanced Language Representation with Informative Entities

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2019-08-05 A propos

Reasoning With Neural Tensor Networks for Knowledge Base Completion (2013)

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**Predicting the likely truth of additional facts based on existing facts in the knowledge base.**

> we introduce an expressive neural
tensor network suitable for reasoning over relationships between two entities.

Most similar work: [Bordes et al.](http://127.0.0.1:8080/semanlink/doc/2019/08/learning_structured_embeddings_) (2011)

Contributions:

1. new neural tensor
network (**NTN**) suitable for reasoning over relationships between two entities. Generalizes several previous neural network models and provides a more
powerful way to model relational information than a standard neural network layer.
2. a new way to represent entities in knowledge bases, as the
average of  their constituting word vectorss, allowing the sharing of statistical strength between the words describing
each entity (e.g., Bank of China and China).
3. incorporation of word vectors which are trained on large unlabeled text

> We **learn to modify word representations
via grounding in world knowledge**. This essentially allows us to analyze word embeddings and
query them for specific relations. Furthermore, the resulting vectors could be used in other tasks
such as named entity recognition or relation classification in natural language

**Makes use of entity name**: NTN
first learns word vectors from an auxiliary news corpus, and
then initializes the representation of each entity by averaging
the vectors of words contained in its name. For example,
**the embedding of AlfredHitchcock is initialized by the
average word vectors of “alfred” and “hitchcock”**... This kind of methods model textual information
separately from KG facts, and hence fail to leverage
interactions between them.[src](doc:2019/05/knowledge_graph_embedding_a_su)

2019-08-03 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

[1601.01343] Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

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2019-01-27 A propos

Wikipedia2Vec

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2018-12-23 A propos

[1809.00782] Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

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2018-09-06 A propos

A Tri-Partite Neural Document Language Model for Semantic Information Retrieval (2018 - ESWC conference)

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2018-06-08 A propos

[1804.01486] Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data

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2018-04-14 A propos

Modèle Neuronal de Recherche d'Information Augmenté par une Ressource Sémantique (2017)

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2018-02-12 A propos

Knowledge Graph and Text Jointly Embedding (2014)

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2018-01-05 A propos

Learning Concept-Driven Document Embeddings for Medical Information Search (2017)

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2018-01-03 A propos

Combining word and entity embeddings for entity linking (ESWC 2017)

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2017-12-30 A propos

Enriching Word Embeddings Using Knowledge Graph for Semantic Tagging in Conversational Dialog Systems - Microsoft Research (2015)

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> new simple, yet effective approaches to
learn domain specific word embeddings.

## Intro

> Adapting word embeddings, such as jointly capturing
syntactic and semantic information, can further enrich semantic
word representations for several tasks, e.g., sentiment
analysis (Tang et al. 2014), named entity recognition
(Lebret, Legrand, and Collobert 2013), entity-relation extraction
(Weston et al. 2013), etc. (Yu and Dredze 2014)
has introduced a lightly supervised word embedding learning
extending word2vec. They incorporate prior information to the objective
function as a regularization term considering synonymy relations
between words from Wordnet (Fellbaum 1999).

> In this work, we go one step further and investigate if
enriching the word2vec word embeddings trained on unstructured/
unlabeled text with domain specific semantic relations
obtained from knowledge sources (e.g., knowledge
graphs, search query logs, etc.) can help to discover relation
aware word embeddings. Unlike earlier work, **we encode the
information about the relations between phrases, thereby,
entities and relation mentions are all embedded into a low dimensional
vector space**.

## Related work (Learning Word Embeddings with Priors)

- word2vec
- Relational Constrained Model (RTM) (Yu and Dredze 2014)
While CBOW learns lexical word embeddings from provided text, the RTM learns embeddings of words based on their similarity to other words provided by a knowledge resource (eg. wordnet)
- Joint model  (Yu and Dredze 2014)
combines CBOW and RTM through linear combination

2017-10-02 A propos