Semanlink - sl:creationTime : 2020-10-02T00:37:27Z AND Text-Aware KG embedding AND Text-Aware KG embedding

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Text-Aware KG embedding - Zero shot -

sl:creationTime : 2020-10-02T00:37:27Z AND Text-Aware KG embedding AND Text-Aware KG embedding

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 About