Semantically Smooth Knowledge Graph Embedding

By Shu Guo, Quan Wang, Lihong Wang, Li Guo. 2015

152 citations recorded by the source corpus.

This paper considers the problem of embedding Knowledge Graphs (KGs) consisting of entities and relations into lowdimensional vector spaces. Most of the existing methods perform this task based solely on observed facts. The only requirement is that the learned embeddings should be compatible within each individual fact. In this paper, aiming at further discovering the intrinsic geometric structure of the embedding space, we propose Semantically Smooth Embedding (SSE). The key idea of SSE is to take full advantage of additional semantic information and enforce the embedding space to be semantically smooth, i.e., entities belonging to the same semantic category will lie close to each other in the embedding space. Two manifold learning algorithms Laplacian Eigenmaps and Locally Linear Embedding are used to model the smoothness assumption. Both are formulated as geometrically based regularization terms to constrain the embedding task. We empirically evaluate SSE in two benchmark tasks of link prediction and triple classification, and achieve significant and consistent improvements over state-of-the-art methods. Furthermore, SSE is a general framework. The smoothness assumption can be imposed to a wide variety of embedding models, and it can also be constructed using other information besides entities’ semantic categories.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “[51] optimize amargin-based ranking loss (Section 4.”
Context-Dependent Knowledge Graph Embedding (2015, background): “The second is NELL186 released by Guo et al. (2015) 4 , containing the most frequent 186 relations in NELL (Carlson et al., 2010) and the associated entities.”
Incorporating Knowledge Graph Embeddings into Topic Modeling (2017, unknown): “Knowledge graph embedding aims at embedding entities and relationships of knowledge graphs in vector spaces (Bordes et al. 2011; 2013; Wang et al. 2014b; 2014a; Guo et al. 2015; Xie et al. 2016).”
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, background): “SSE [67] incorporates semantic categories of entities to embed entities belonging to the same category smoothly in semantic space.”
SSE: Semantically Smooth Embedding for Knowledge Graphs (2017, background): “Index Terms—Knowledge graph embedding, semantic smoothness, Laplacian eigenmaps, locally linear embedding Ç”
Knowledge Graphs: Opportunities and Challenges (2023, background): “For example, Guo et al. (2015) take advantage of additional entity type information, which is the semantic category of each entity, to obtain the correlation between the entities and to tackle the data sparsity issue.”
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal (2022, unknown): “Itbuildstherewardfunctionbasedontheembeddingsgenerated bytheRGCN[74],whichmakesthosepath-basedmodelsmore explainable.”
Knowledge Graph Embedding: A Survey of Approaches and Applications (2017, background): “, entity types [25], [26], relation paths [27], [28], [29], textual descriptions [30], [31], [32], [33], and even logical rules [23], [34], [35], to learn more pre-”
GAKE: Graph Aware Knowledge Embedding (2016, background): “There are some other works incorporate additional information, such as text(Toutanova and Chen, 2015; Toutanova et al., 2015) and entity types(Guo et al., 2015).”
TransG : A Generative Model for Knowledge Graph Embedding (2015, background): “(Guo et al., 2015) aims at further discovering the geometric structure of the embedding space to make it semantically smooth.”
SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions (2016, background): “SSE (Guo et al. 2015) aims at discovering the geometric structure of embedding topologies and then based on these assumptions, designs a semantically smoothing score function.”
From One Point to a Manifold: Knowledge Graph Embedding for Precise Link Prediction (2015, methodology): “, 2015a], and semantic smoothness of the embedding space [Guo et al., 2015].”