Semanlink - AI girls and guys ; Entities AND Knowledge Graph Completion

Extraction de relation via la validation de relation

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2019-12-03 About

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 About

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 About