  Injecting textual information into knowledge graph (KG) entity
representations has been a worthwhile expedition in terms of improving
performance in KG oriented tasks within the NLP community. External knowledge
often adopted to enhance KG embeddings ranges from semantically rich lexical
dependency parsed features to a set of relevant key words to entire text
descriptions supplied from an external corpus such as wikipedia and many more.
Despite the gains this innovation (Text-enhanced KG embeddings) has made, the
proposal in this work suggests that it can be improved even further. Instead of
using a single text description (which would not sufficiently represent an
entity because of the inherent lexical ambiguity of text), we propose a
multi-task framework that jointly selects a set of text descriptions relevant
to KG entities as well as align or augment KG embeddings with text
descriptions. Different from prior work that plugs formal entity descriptions
declared in knowledge bases, this framework leverages a retriever model to
selectively identify richer or highly relevant text descriptions to use in
augmenting entities. Furthermore, the framework treats the number of
descriptions to use in augmentation process as a parameter, which allows the
flexibility of enumerating across several numbers before identifying an
appropriate number. Experiment results for Link Prediction demonstrate a 5.5%
and 3.5% percentage increase in the Mean Reciprocal Rank (MRR) and Hits@10
scores respectively, in comparison to text-enhanced knowledge graph
augmentation methods using traditional CNNs.
