  Knowledge graphs (KGs) consist of links that describe relationships between
entities. Due to the difficulty of manually enumerating all relationships
between entities, automatically completing them is essential for KGs. Knowledge
Graph Completion (KGC) is a task that infers unseen relationships between
entities in a KG. Traditional embedding-based KGC methods, such as RESCAL,
TransE, DistMult, ComplEx, RotatE, HAKE, HousE, etc., infer missing links using
only the knowledge from training data. In contrast, the recent Pre-trained
Language Model (PLM)-based KGC utilizes knowledge obtained during pre-training.
Therefore, PLM-based KGC can estimate missing links between entities by reusing
memorized knowledge from pre-training without inference. This approach is
problematic because building KGC models aims to infer unseen links between
entities. However, conventional evaluations in KGC do not consider inference
and memorization abilities separately. Thus, a PLM-based KGC method, which
achieves high performance in current KGC evaluations, may be ineffective in
practical applications. To address this issue, we analyze whether PLM-based KGC
methods make inferences or merely access memorized knowledge. For this purpose,
we propose a method for constructing synthetic datasets specified in this
analysis and conclude that PLMs acquire the inference abilities required for
KGC through pre-training, even though the performance improvements mostly come
from textual information of entities and relations.
