  In contrast to large text corpora, knowledge graphs (KG) provide dense and
structured representations of factual information. This makes them attractive
for systems that supplement or ground the knowledge found in pre-trained
language models with an external knowledge source. This has especially been the
case for classification tasks, where recent work has focused on creating
pipeline models that retrieve information from KGs like ConceptNet as
additional context. Many of these models consist of multiple components, and
although they differ in the number and nature of these parts, they all have in
common that for some given text query, they attempt to identify and retrieve a
relevant subgraph from the KG. Due to the noise and idiosyncrasies often found
in KGs, it is not known how current methods compare to a scenario where the
aligned subgraph is completely relevant to the query. In this work, we try to
bridge this knowledge gap by reviewing current approaches to text-to-KG
alignment and evaluating them on two datasets where manually created graphs are
available, providing insights into the effectiveness of current methods.
