Semanlink - Favoris AND Graph

> we propose to
leverage the factual knowledge from KGs to enhance LLMs,
while still benefiting from circumventing the burdensome
training expenses by using pre-trained LLMs

> Graph Neural Prompting
(GNP), a plug-and-play method to assist pre-trained
LLMs in learning beneficial knowledge from KGs
>
> GNP
encodes the pertinent grounded knowledge and complex
structural information to derive Graph Neural Prompt, an
embedding vector that can be sent into LLMs to provide
guidance and instructions

> - GNP first utilizes
a GNN to capture and encode the
intricate graph knowledge into **entity/node embeddings**. 
> - Then,
a cross-modality pooling module is present to determine
the **most relevant node embeddings in relation to the text
input**, and consolidate these node embeddings into **a holistic
graph-level embedding**.
> - After that, GNP encompasses a
**domain projector** to bridge the inherent disparities between
the graph and text domains.
> - Finally, a **self-supervised link
prediction objective** is introduced to enhance the model
comprehension of relationships between entities and capture
graph knowledge in a self-supervised manner.

2023-09-28 A propos

Knowledge Graph RAG Query Engine - LlamaIndex

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2023-08-17 A propos

Jerry Liu on X: “Graph RAG” in @llama_index: a new method of augmenting LLMs with context from a graph database

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2023-08-17 A propos

GitHub - neo4j/NaLLM: Repository for the NaLLM project

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2023-08-14 A propos

Knowledge Graphs & LLMs: Fine-Tuning Vs. Retrieval-Augmented Generation | by Tomaz Bratanic | Neo4j Developer Blog (June 6 2023)

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2023-08-13 A propos

[2308.00081] Towards Semantically Enriched Embeddings for Knowledge Graph Completion

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2023-08-02 A propos

ML Blog - Improve ChatGPT with Knowledge Graphs

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2023-07-04 A propos

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

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2023-06-18 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

Jerry Liu sur Twitter : "LLMs + knowledge graphs backed by a graph db (@NebulaGraph). New stack for retrieval-augmented generation. Now possible with @llama_index..."

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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

[2304.02711] Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning

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2023-04-07 A propos

harley turan sur Twitter : "a force-directed knowledge graph interface for @OpenAI’s gpt-4..."

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2023-03-31 A propos

[2302.05019] A Comprehensive Survey on Automatic Knowledge Graph Construction

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2023-02-15 A propos

François Chollet sur Twitter : "I'm pretty optimistic that the LLM reliability / factualness issue can be fixed. The key is to use LLMs as a dialog interface and not as a store of knowledge..."

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2023-01-24 A propos

[2301.08210] Everything is Connected: Graph Neural Networks

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2023-01-21 A propos

IBM/zshot: Zero and Few shot named entity & relationships recognition

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2022-12-23 A propos

[2210.13952] KnowGL: Knowledge Generation and Linking from Text

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2022-11-13 A propos

[2210.09338] Deep Bidirectional Language-Knowledge Graph Pretraining

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2022-10-23 A propos

Zshot: Zero and Few shot named entity & relationships recognition

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2022-10-01 A propos

[2006.10713] Zero-Shot Learning with Common Sense Knowledge Graphs

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2022-08-29 A propos

[2208.09982] GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization

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2022-08-24 A propos

[1805.09906] Diffusion Maps for Textual Network Embedding

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2022-08-19 A propos

Connor Shorten sur Twitter : "Wow, incredible to see this from NASA! 🚀 Weaviate's integration of Vector Search with Graph Data and Symbolic Relations is a very interesting combination of technologies!..."

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2022-08-11 A propos

[2207.09980] ReFactorGNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective

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2022-07-23 A propos

StATIK: Structure and Text for Inductive Knowledge Graph Completion - ACL Anthology (2022)

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2022-07-17 A propos

[2205.00820] Entity-aware Transformers for Entity Search

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2022-07-12 A propos

[2205.08012] CascadER: Cross-Modal Cascading for Knowledge Graph Link Prediction

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2022-07-07 A propos

Tara Safavi sur Twitter : "CascadER, a new knowledge graph (KG) link prediction method leveraging structured relations + unstructured text..."

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2022-07-07 A propos

[2008.12813] HittER: Hierarchical Transformers for Knowledge Graph Embeddings

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2022-06-30 A propos

[2205.15952] Knowledge Graph -- Deep Learning: A Case Study in Question Answering in Aviation Safety Domain

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2022-06-11 A propos

jboynyc/textnets: Text analysis with networks.

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2022-05-30 A propos

International Workshop on Knowledge Graph Generation from Text (Text2KG) 2022

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2022-05-30 A propos

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

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2022-05-18 A propos

DataBorg - Knowledge management simplified

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2022-05-14 A propos

[2204.11428] Personal Research Knowledge Graphs

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2022-04-30 A propos

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

Tags:

[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?..."

Tags:

2022-04-13 A propos

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

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2022-03-25 A propos

Domain adaptation of word embeddings through the exploitation of in-domain corpora and knowledge bases (PhD Thesis 2021)

Tags:

Thèse de Hicham El Boukkouri, univ Paris-Saclay

[Github](https://github.com/helboukkouri/phd-code)

### Goal

Given a target specialized domain, improve the quality of general-domain
word representations using in-domain corpora and/or knowledge bases

### Contributions

#### a method for specializing general-domain embeddings in a [Low-Resource](tag:nlp_low_resource_scenarios) context.

> - train static representations on the task corpus, 
> - resume the
pre-training of general-domain contextual embeddings on the same task corpus,
> - finally, combine both static and contextual representations into
one final model

#### we tackle the issue of using a general-domain vocabulary in a specialized domain

#### Evaluation of  re-training vs training from scratch on specialized corpora using a specialized vocabulary

training from scratch better, but not that much: re-training from a general model
is still appropriate as it is less expensive and leads to comparable, although
slightly lower, performance

#### Regarding subword-based tokenization systems
> we argue that they are inconvenient in practice -> CharacterBERT, a variant
of BERT that uses ELMo’s character-based system instead of WordPieces. More convenient ti use, superior robustness to misspellings

#### Ways to specialize general-domain representations using knowledge bases

a strong baseline using a simple
method relying on graph embeddings and concatenation, using only is_a relation

> both static and contextual embeddings may effectively be
specialized using this simple approach

#### Knowledge Injection Modules (KIM) that
inject the knowledge representations directly within the BERT-like models' architecture

### Notes

> our experiments focused on a single
setting (i.e. the medical domain and the English language)

> meta-embeddings, an approach that consists in combining
different sets of representations for achieving improved performance

2022-03-23 A propos

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

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2022-03-15 A propos

[2110.10778] Contrastive Document Representation Learning with Graph Attention Networks

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2022-03-10 A propos

SPIKE for Knowledge Base Construction

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2022-02-22 A propos

Yosi Shamay sur Twitter : "a new platform for rapid ad-hoc knowledgebase construction using extractive search...."

Tags:

2022-02-22 A propos

[2110.06176] Mention Memory: incorporating textual knowledge into Transformers through entity mention attention

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2021-10-13 A propos

Link Prediction with Graph Neural Networks and Knowledge Extraction

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2021-09-03 A propos

Knowledge Graphs in Natural Language Processing @ ACL 2021 | by Michael Galkin | Aug, 2021

Tags:

2021-08-06 A propos

Agostina Calabrese sur Twitter : Structured Sentiment Analysis as Dependency Graph Parsing

Tags:

2021-08-02 A propos

[2001.03765] Learning Cross-Context Entity Representations from Text

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2021-06-22 A propos

[2101.00345] Modeling Fine-Grained Entity Types with Box Embeddings

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2021-06-22 A propos

[2102.07043] Reasoning Over Virtual Knowledge Bases With Open Predicate Relations

Tags:

> a method for constructing **a virtual KB (VKB) trained entirely from text**

Open Predicate Query Language (OPQL): constructing a virtual knowledge base (VKB) that supports KB reasoning & open-domain QA, tackling the incompleteness of knowledge bases by constructing a virtual KB only from text

> OPQL constructs
a VKB by **encoding and indexing a set of
relation mentions** in a way that naturally enables
reasoning and can be trained without any structured
supervision.

> can be used
as an **external memory integrated into a language
model**

cf. this earlier paper [[2002.10640] Differentiable Reasoning over a Virtual Knowledge Base](doc:2020/07/2002_10640_differentiable_rea). But does not require an initial structured KB for distant
supervision.

> The key idea in constructing the OPQL VKB is to use a
dual-encoder pre-training process, similar to 
[[1906.03158] Matching the Blanks: Distributional Similarity for Relation Learning](doc:2021/05/1906_03158_matching_the_blank)

Related work section refers to [[1909.04164] Knowledge Enhanced Contextual Word Representations](doc:2020/05/1909_04164_knowledge_enhanced). Also refers to [[2007.00849] Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge](doc:2020/07/2007_00849_facts_as_experts_) (some authors in common)

2021-06-20 A propos

[1410.5859] Towards a Model Theory for Distributed Representations

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2021-06-10 A propos

[2009.12030] AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding

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2021-05-17 A propos

[1911.09419] Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction

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2021-05-17 A propos

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

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2021-04-11 A propos

[2103.12876] Complex Factoid Question Answering with a Free-Text Knowledge Graph

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2021-03-30 A propos

Linking Entities with Knowledge Graphs | by Sigurd Berglann | Strise | Medium

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2021-02-05 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

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

Tags:

> 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

[2009.02835] E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce

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2020-12-14 A propos

TextGraphs 2020

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2020-12-13 A propos

Supporting content decision makers with machine learning | Dec, 2020 | Netflix TechBlog

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2020-12-11 A propos

Knowledge Base Embedding By Cooperative Knowledge Distillation - ACL Anthology

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KD-MKB paper

2020-12-05 A propos

FP Servant sur Twitter : "constructing a personal knowledge graph as a support for learning (and a metaphor of the learning experience)..."

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2020-12-03 A propos

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

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2020-11-26 A propos

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

Tags:

2020-11-24 A propos

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

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2020-11-11 A propos

raphaelsty/ckb: Contextual knowledge bases

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2020-11-09 A propos

Peter Bloem sur Twitter : Large-scale network motif analysis using compression

Tags:

2020-11-06 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

Tags:

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

[2010.05234] A Practical Guide to Graph Neural Networks

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2020-10-15 A propos

[2005.03675] Machine Learning on Graphs: A Model and Comprehensive Taxonomy

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2020-10-03 A propos

[2010.00402] From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering

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2020-10-03 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)

Tags:

"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

[2009.07938] Type-augmented Relation Prediction in Knowledge Graphs

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2020-09-19 A propos

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

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2020-09-14 A propos

Large-scale network motif analysis using compression | peterbloem.nl

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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

[1803.07828] Expeditious Generation of Knowledge Graph Embeddings

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2020-09-02 A propos

[2009.00318] More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings

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2020-09-02 A propos

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

Tags:

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

[2008.08995] Constructing a Knowledge Graph from Unstructured Documents without External Alignment

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2020-08-21 A propos

[2003.11644] MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network

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2020-08-14 A propos

[1607.00653] node2vec: Scalable Feature Learning for Networks

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2020-08-08 A propos

Bringing traditional ML to your Neo4j Graph with node2vec | Dave Voutila

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2020-08-06 A propos

[1911.03903] A Re-evaluation of Knowledge Graph Completion Methods

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2020-07-28 A propos

Knowledge Graphs in Natural Language Processing @ ACL 2020 | by Michael Galkin

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2020-07-12 A propos

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

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2020-07-11 A propos

[2002.10640] Differentiable Reasoning over a Virtual Knowledge Base

Tags:

2020-07-11 A propos

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

Tags:

> 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

End-to-End Learning with Text & Knowledge Bases (Bhuwan Dhingra PhD Thesis)

Tags:

2020-07-06 A propos

Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge (ACL Anthology 2020)

Tags:

Aim to leverage both contextual representation of input text (deep LMs)  and  knowledge derived
from curated KBs ([Wordnet](tag:wordnet)) to improve [slot tagging](tag:slot_tagging) in the presence of [out-of-vocab](tag:oov) words ([few-shot scenario](tag:few_shot_learning))

Method:

1. retrieve potentially relevant KB entities and
encode them into distributed representations that
describe global graph-structured information
2. BERT encoder
layer to capture context-aware representations of
the sequence and attend to the KB embeddings
using multi-level graph attention
3. integrate
BERT embeddings and the KB embeddings
to predict the slot type

Contributions:

1. feasibility of applying lexical ontology
to facilitate recognizing OOV words. First to consider the large-scale background
knowledge for enhancing context-aware
slot tagging models.
2. a knowledge integration mechanism that uses multi-level graph
attention to model explicit lexical relations.
3.experiments on two benchmark datasets

> our method makes a notable difference in a
scenario where samples are linguistically diverse,
and large vocab exists.

(Better improvements when using RNN than BERT, because BERT already contains a lot of background knowledge)

2020-07-04 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

Denny Britz sur Twitter : "I built a little frontend for my AI/ML papergraph tool..."

Tags:

2020-05-27 A propos

Label unstructured data using Enterprise Knowledge Graphs 2

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2020-05-22 A propos

Peter Bloem sur Twitter : "One of the messages from Ruffinelli et al 2020..."

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2020-05-19 A propos

[2003.08001] Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study

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2020-05-15 A propos

[1909.04164] Knowledge Enhanced Contextual Word Representations

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2020-05-13 A propos

[1907.04829] BAM! Born-Again Multi-Task Networks for Natural Language Understanding

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2020-05-12 A propos

[1912.08422] Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation

Tags:

2020-05-12 A propos

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

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2020-05-11 A propos

[1706.00384] Deep Mutual Learning

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2020-05-11 A propos

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

Tags:

2020-05-11 A propos

Aakash Kumar Nain sur Twitter : "I want to start reading about Graph NNs but I have two questions in my mind: 1. Applications of GNNs 2. Which paper should I start with?" / Twitter

Tags:

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

[2004.14843] Knowledge Graph Embeddings and Explainable AI

Tags:

2020-05-04 A propos

Disambiguating KBpedia Knowledge Graph Concepts

Tags:

2020-05-03 A propos

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

Tags:

2020-04-30 A propos

Iterative Entity Alignment with Improved Neural Attribute Embedding

Tags:

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

Tags:

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

[2004.06842] Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph

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2020-04-17 A propos

[1909.03193] KG-BERT: BERT for Knowledge Graph Completion

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2020-03-22 A propos

[1911.02168] CoKE: Contextualized Knowledge Graph Embedding

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2020-03-22 A propos

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

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2020-03-15 A propos

Martynas Jusevicius sur Twitter : "Is there a solution for entity recognition that would use a local #KnowledgeGraph to look for matches? Ideally any SPARQL datasource..."

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2020-03-13 A propos

AmbiverseNLU: A Natural Language Understanding suite by Max Planck Institute for Informatics

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2020-03-13 A propos

[1909.07606] K-BERT: Enabling Language Representation with Knowledge Graph

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2020-03-08 A propos

[2003.02320] Knowledge Graphs

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2020-03-07 A propos

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

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2020-03-03 A propos

davidsbatista/BREDS: "Bootstrapping Relationship Extractors with Distributional Semantics" (Batista et al., 2015) - code for EMNLP'15 paper

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2020-01-11 A propos

[2001.01447] Improving Entity Linking by Modeling Latent Entity Type Information

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2020-01-09 A propos

Advancing Natural Language Processing (NLP) for Enterprise Domains

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2020-01-07 A propos

(((ل()(ل() 'yoav)))) sur Twitter : "is there a convincingly successful application of graph convolutions in NLP you can point me to?"

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2019-12-19 A propos

NLP at Scale for Maintenance and Supply Chain Management

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2019-12-07 A propos

CONCEPTUAL GROUNDING FOR TEXT REPRESENTATION LEARNING

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2019-12-03 A propos

Extraction de relation via la validation de relation

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2019-12-03 A propos

Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling (ACL 2019)

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2019-11-17 A propos

Graph Neural Networks for Natural Language Processing tutorial at EMNLP 2019

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2019-11-08 A propos

[1910.09760] Question Answering over Knowledge Graphs via Structural Query Patterns

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2019-11-06 A propos

Improving long-form question answering by compressing search results

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2019-10-24 A propos

Sebastian Ruder sur Twitter : network embeddings in the biomedical domain. @eurnlp #EurNLP2019

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2019-10-11 A propos

[1910.03524] Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs

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2019-10-09 A propos

[1909.01066] Language Models as Knowledge Bases?

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2019-09-05 A propos

Accelerating Towards Natural Language Search with Graphs

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2019-08-30 A propos

Knowledge Graphs and Natural Language Processing. The Year of the Graph Newsletter, July/August 2019 | Linked Data Orchestration

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Knowledge Graphs and NLP

2019-08-29 A propos

[1808.02590] A Tutorial on Network Embeddings

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2019-08-25 A propos

[1904.02342] Text Generation from Knowledge Graphs with Graph Transformers

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2019-08-23 A propos

[1905.07854] KGAT: Knowledge Graph Attention Network for Recommendation

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2019-08-23 A propos

Neural Knowledge Acquisition via Mutual Attention between Knowledge Graph and Text (2018)

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2019-08-23 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

[1905.07129] ERNIE: Enhanced Language Representation with Informative Entities

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2019-08-05 A propos

Knowledge graphs in Natural Language Processing @ ACL 2019 - Michael Galkin

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2019-08-05 A propos

Learning Structured Embeddings of Knowledge Bases (2011)

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2019-08-03 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

A2N: Attending to Neighbors for Knowledge Graph Inference - ACL 2019

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2019-07-31 A propos

[1901.00596] A Comprehensive Survey on Graph Neural Networks

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2019-07-15 A propos

[1905.10070] Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification

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2019-06-22 A propos

A Hybrid Graph Model for Distant Supervision Relation Extraction | Springer for Research & Development (ESWC 2019)

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2019-05-30 A propos

[1709.07604] A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications

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2019-05-29 A propos

Microsoft Concept Graph and Concept Tagging Release

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Microsoft Concept Graph

2019-05-28 A propos

Microsoft Concept Graph: Mining Semantic Concepts for Short Text Understanding | MIT Press Journals (2019)

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2019-05-28 A propos

Dandelion API - Semantic Text Analytics as a service

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2019-04-24 A propos

thunlp/OpenKE: An Open-Source Package for Knowledge Embedding (KE)

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2019-04-23 A propos

A Neural QA Model for DBpedia (GSoC 2019)

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2019-03-26 A propos

[1903.05823] Deep Patent Landscaping Model Using Transformer and Graph Embedding

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2019-03-18 A propos

[1903.05872] Interactive Concept Mining on Personal Data -- Bootstrapping Semantic Services

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2019-03-17 A propos

[1901.11504] Multi-Task Deep Neural Networks for Natural Language Understanding

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2019-02-17 A propos

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction - ACL Anthology

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2019-02-09 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

Romain Vial (Hyperlex) at Paris NLP meetup, slides

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2019-01-24 A propos

Wikipedia2Vec

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2018-12-23 A propos

[1807.07984] Attention Models in Graphs: A Survey

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2018-11-14 A propos

Graph Attention Networks (2018)

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2018-11-14 A propos

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

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2018-11-04 A propos

Ontotext | Semantic Technology Developer

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2018-10-13 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

HyperE: Hyperbolic Embeddings for Entities

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2018-07-27 A propos

[1806.05662] GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations

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2018-06-23 A propos

[1806.01261] Relational inductive biases, deep learning, and graph networks

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2018-06-13 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

Tutorial on Poincaré Embeddings (Jupyter Notebook )

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2018-05-20 A propos

Implementing Poincaré Embeddings | RARE Technologies

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2018-05-20 A propos

Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning

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2018-05-10 A propos

TUTORIAL: Graph-based Text Representations (SLIDES)

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2018-05-10 A propos

TUTORIAL: Graph-based Text Representations: Boosting Text Mining, NLP and Information Retrieval with Graphs

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2018-05-10 A propos

TUTORIAL: Representation Learning on Networks - TheWebConf 2018

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Network representation learning (NRL): Approaches that automatically learn to encode network structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction

**Goal of representation learning for networks: efficient task-independant feature learning for ML in networks.** But it's hard. DL toolbox are designed for single sequences or grids (for instance CNN for images, RNN or word2vec are fixed size), but networks are far more complex!

from the abstract:

> In this tutorial, we will cover key advancements in NRL over the last decade, with an emphasis on fundamental advancements made in the last two years. We will discuss classic matrix factorization-based methods (e.g., Laplacian eigenmaps), random-walk based algorithms (e.g., DeepWalk and node2vec), as well as very recent advancements in graph convolutional networks (GCNs). We will cover methods to embed individual nodes (see [node embeddings](/tag/node_embeddings)) as well as approaches to embed entire (sub)graphs, and in doing so, we will present a unified framework for NRL.

2018-05-05 A propos

[1804.04526] EventKG: A Multilingual Event-Centric Temporal Knowledge Graph

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2018-04-15 A propos

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

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2018-04-14 A propos

ML-knn: A lazy learning approach to multi-label learning (2007)

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a lazy learning approach derived from the traditional [k-nearest neighbors algorithm](tag:k_nearest_neighbors_algorithm)

> for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, [Maximum a posteriori (MAP)](tag:maximum_a_posteriori_estimation) principle is utilized to determine the label set for the unseen instance.

Implemented in [scikit-multilearn](http://scikit.ml/api/skmultilearn.adapt.mlknn.html), in [java](https://github.com/lefman/mulan-extended/blob/master/mulan/src/mulan/classifier/lazy/MLkNN.java)

> the first lazy approach proposed specifically for multi-label classification. This is also a binary relevance approach which considers each label independently as a binary classification problem. Instead of a standard k-NN method, however, MLkNN uses the maximum a-posteriori (MAP) (Kelleher et al., 2015) approach combined with k-NN. [src](https://pdfs.semanticscholar.org/af9b/33da37d290c063cd826ab5923d96892a9767.pdf)

2018-03-18 A propos

GitHub - anvaka/word2vec-graph: Exploring word2vec embeddings as a graph of nearest neighbors

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2018-03-12 A propos

[1710.04099] Wembedder: Wikidata entity embedding web service

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2018-02-13 A propos

[1603.08861] Revisiting Semi-Supervised Learning with Graph Embeddings

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2018-02-13 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

Translating Embeddings for Modeling Multi-relational Data (2013)

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This work focuses on modeling multi-relational
data from KBs (Wordnet and Freebase in this paper), with the goal of providing an efficient
tool to complete them by automatically adding new facts, without requiring extra knowledge.

**Embedding entities and relationships of multirelational
data**: a method which **models relationships by interpreting them as translations** operating on the
low-dimensional embeddings of the entities. Motivation:
- hierarchical relationships are extremely common in KBs and translations are the natural transformations for representing them.
- cf. word embeddings and the “capital of” relationship between countries and cities, which are (coincidentally rather than willingly) represented by the model as translations in the embedding space. This suggests that there may exist embedding spaces in which 1-to-1 relationships between entities of different types may, as well, be represented by translations. The intention of our model is to enforce such a structure of the embedding space.

[Good blog post by PY Vandenbussche](http://pyvandenbussche.info/2017/translating-embeddings-transe/)

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

[1705.08039] Poincaré Embeddings for Learning Hierarchical Representations

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2017-12-16 A propos

Towards a Seamless Integration of Word Senses into Downstream NLP Applications (2017)

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2017-10-21 A propos

Efficient unsupervised keywords extraction using graphs

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2017-10-04 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

ConceptNet

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2017-09-18 A propos

[1506.01094] Traversing Knowledge Graphs in Vector Space

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