Pinned Repositories
A
gym
A toolkit for developing and comparing reinforcement learning algorithms.
KELM
KELM is from a new perspective that transfers language model word2vec to knowledge embedding scenario. KELM is built as a "context-based" prediction model.
The-code-of-paper-Incorporating-Attributes-Semantics-into-Knowledge-Graph-Embeddings-
More and more works have focused on incorporating different kinds of literals into Knowledge Graph to promote the performance of knowledge embedding. These literals contain numeric literals, text literals, image literals and so on. These additional descriptions are connected to the entities through certain attributes. To incorporate numeric literals, some methods combine the embeddings of literals part with the traditional part - embeddings of entities. However, in the construction of literals embeddings, these existing methods consider the differences of these attributes: one dimension represents one attribute. But they ignore semantic meanings of attributes themselves. In this paper, we propose two methods to incorporate attributes semantics into knowledge graph embeddings from two perspectives: LiteralE-AN and literalE-AT. They concatenate with the embeddings of numeric literals by different ways. Furthermore, their extension model LiteralE-C is also proposed have a more comprehensive representation of attributes semantics. In an empirical study over two standard datasets FB15k and FB15k-237, we evaluate our models for link prediction. We demonstrate they show effective way to improve LiteralE and achieve the state-of-the-art results. In ablation experiments, we find combined models do better than their singular counterparts in most cases.
Limingyang1's Repositories
Limingyang1/The-code-of-paper-Incorporating-Attributes-Semantics-into-Knowledge-Graph-Embeddings-
More and more works have focused on incorporating different kinds of literals into Knowledge Graph to promote the performance of knowledge embedding. These literals contain numeric literals, text literals, image literals and so on. These additional descriptions are connected to the entities through certain attributes. To incorporate numeric literals, some methods combine the embeddings of literals part with the traditional part - embeddings of entities. However, in the construction of literals embeddings, these existing methods consider the differences of these attributes: one dimension represents one attribute. But they ignore semantic meanings of attributes themselves. In this paper, we propose two methods to incorporate attributes semantics into knowledge graph embeddings from two perspectives: LiteralE-AN and literalE-AT. They concatenate with the embeddings of numeric literals by different ways. Furthermore, their extension model LiteralE-C is also proposed have a more comprehensive representation of attributes semantics. In an empirical study over two standard datasets FB15k and FB15k-237, we evaluate our models for link prediction. We demonstrate they show effective way to improve LiteralE and achieve the state-of-the-art results. In ablation experiments, we find combined models do better than their singular counterparts in most cases.
Limingyang1/A
Limingyang1/gym
A toolkit for developing and comparing reinforcement learning algorithms.
Limingyang1/KELM
KELM is from a new perspective that transfers language model word2vec to knowledge embedding scenario. KELM is built as a "context-based" prediction model.