Meet Concept2Box: Bridging the Gap Between High-Level Concepts and Fine-Grained Entities in Knowledge Graphs – A Dual Geometric Approach

A lot of research has gone into finding ways to represent big sets of connected data, like knowledge graphs. These methods are called Knowledge Graph Embeddings (KGE), and they help us use this data for various practical purposes in the real world. 

Traditional methods have often overlooked a significant aspect of knowledge graphs, which is the presence of two distinct types of information: high-level concepts that relate to the overall structure (ontology view) and specific individual entities (instance view). Typically, these methods treat all nodes in the knowledge graph as vectors within a single hidden space. 

The above image demonstrates a two-view knowledge graph, which comprises (1) an ontology-view knowledge graph containing high-level concepts and meta-relations, (2) an instance-view knowledge graph containing specific, detailed instances and relations, and (3) a collection of connections (cross-view links) between these two views, Concept2Box is designed to acquire dual geometric embeddings. Under this approach, each concept is represented as a geometric box in the latent space, while entities are represented as point vectors.

In contrast to using a single geometric representation that cannot adequately capture the structural distinctions between two perspectives within a knowledge graph and lacks probabilistic meaning in relation to the granularity of concepts, the authors introduce Concept2Box. This innovative approach simultaneously embeds both views of a knowledge graph by employing dual geometric representations. Concepts are represented using box embeddings, enabling the learning of hierarchical structures and complex relationships like overlap and disjointness.

 
The volume of these boxes corresponds to the granularity of concepts. In contrast, entities are represented as vectors. To bridge the gap between concept box embeddings and entity vector embeddings, a novel vector-to-box distance metric is proposed, and both embeddings are learned jointly. Experimental evaluations conducted on both the publicly available DBpedia knowledge graph and a newly created industrial knowledge graph underscore the effectiveness of Concept2Box. Our model is built to handle the differences in how information is structured in knowledge graphs. But in today’s knowledge graphs, which can involve multiple languages, there’s another challenge. Different parts of the knowledge graph not only have different structures but also use different languages, making it even more complex to understand and work with. In the future, we can expect advancements in this domain.

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