Semanlink - Software AND Knowledge bases

> 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 About

[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 About

Abstract Wikipedia/July 2020 announcement

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2020-07-03 About

Lord of the Wiki Ring: Importing Wikidata into Neo4j and analyzing family trees

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2020-06-17 About

[1911.03814] Scalable Zero-shot Entity Linking with Dense Entity Retrieval

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2020-05-02 About

Sebastian Riedel sur Twitter : Happy to introduce BLINK, the @facebookai open-source entity linker!...

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2020-05-02 About

[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 About

[2002.11402] Detecting Potential Topics In News Using BERT, CRF and Wikipedia

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2020-02-27 About

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

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2020-01-09 About

Nandana Mihindukulasooriya sur Twitter : "I wonder if there is a service for disambiguation and entity linking for @wikidata where I can pass a text and get Wikidata entities mentioned in it!

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2019-07-13 About

Microsoft Concept Graph and Concept Tagging Release

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Microsoft Concept Graph

2019-05-28 About

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

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2019-05-28 About

Technical usability of Wikidata’s linked data

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2019-05-07 About

Learning to Map Wikidata Entities To Predefined Topics

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2019-05-03 About

[1810.09164] Named Entity Disambiguation using Deep Learning on Graphs

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2019-04-26 About

Wikify!: linking documents to encyclopedic knowledge (2007)

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2019-04-16 About

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

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2019-02-17 About

BPEmb: Subword Embeddings

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2019-01-31 About

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

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2019-01-27 About

Wikipedia2Vec

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2018-12-23 About

Capturing meaning: Toward an abstract Wikipedia (ISWC 2018)

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2018-10-16 About

Gaze of time | Timelines and Chronologies curated by the community

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2018-03-06 About

Knowledge Graph and Text Jointly Embedding (2014)

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2018-01-05 About

The Wikipedia Competitor That's Harnessing Blockchain For Epistemological Supremacy | WIRED

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2017-12-17 About

wikidata-query-gui/nearby.html at master · wikimedia/wikidata-query-gui

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2017-11-21 About

Histropedia: “The power of data visualisation combined with free knowledge” – Wikimedia Blog

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Histropedia

2017-11-01 About

Wikidata, a rapidly growing global hub, turns five – Wikimedia Blog

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Wikidata

2017-11-01 About

Histropedia - The Timeline of Everything

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Histropedia

2017-11-01 About

wikidata-taxonomy

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2017-10-29 About

wikimedia/wikidata-query-gui

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Wikidata query service

2017-10-28 About

maxlath/wikidata-sdk: A javascript tool-suite to query Wikidata and simplify its results

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2017-10-28 About

#PageRank-based #Wikidata autocomplete powered by #Apache #Solr

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2017-10-21 About

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 About

Burger King’s new ad forces Google Home to advertise the Whopper - The Verge

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2017-04-13 About

Navigate All the Knowledge with Spring + Neo4j

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2017-02-04 About

Wikidata Class Browser

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