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.

本文介绍了在知识图谱中关系消歧的问题，详细介绍了一种名为 V-Coder 的新型可适应自编码器，它能够识别连接不同领域实体的本质关系，从而改善链接预测的语义披露问题。