Semanlink - TheWebConf AND Graph Embeddings

[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 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

International Workshop on Deep Learning for Graphs and Structured Data Embedding

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2019-04-30 About

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 About

TUTORIAL: Representation Learning on Networks - TheWebConf 2018

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2018-01-27 About

WORKSHOP: BigNet @ WWW 2018 Workshop on Learning Representations for Big Networks

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2018-01-27 About