V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs

TL;DR

V-Coder is an adaptive AutoEncoder that improves relation disambiguation and link prediction in knowledge graphs by learning from corrupted data and enhancing semantic clarity of relations.

Contribution

It introduces V-Coder, a novel adaptive AutoEncoder model inspired by ART, for semantic relation disambiguation in knowledge graphs.

Findings

V-Coder effectively recovers links from corrupted data.

Semantic disclosure improves link prediction accuracy.

Model performs well on Freebase, Yago, and NELL datasets.

Abstract

Semantic Web or Knowledge Graphs (KG) emerged to one of the most important information source for intelligent systems requiring access to structured knowledge. One of the major challenges is the extraction and processing of unambiguous information from textual data. Following the human perception, overlapping semantic linkages between two named entities become clear due to our common-sense about the context a relationship lives in which is not the case when we look at it from an automatically driven process of a machine. In this work, we are interested in the problem of Relational Resolution within the scope of KGs, i.e, we are investigating the inherent semantic of relationships between entities within a network. We propose a new adaptive AutoEncoder, called V-Coder, to identify relations inherently connecting entities from different domains. Those relations can be considered as being ambiguous and are candidates for disentanglement. Likewise to the Adaptive Learning Theory (ART), our model learns new patterns from the KG by increasing units in a competitive layer without discarding the previous observed patterns whilst learning the quality of each relation separately. The evaluation on real-world datasets of Freebase, Yago and NELL shows that the V-Coder is not only able to recover links from corrupted input data, but also shows that the semantic disclosure of relations in a KG show the tendency to improve link prediction. A semantic evaluation wraps the evaluation up.