  Human-curated knowledge graphs provide critical supportive information to
various natural language processing tasks, but these graphs are usually
incomplete, urging auto-completion of them. Prevalent graph embedding
approaches, e.g., TransE, learn structured knowledge via representing graph
elements into dense embeddings and capturing their triple-level relationship
with spatial distance. However, they are hardly generalizable to the elements
never visited in training and are intrinsically vulnerable to graph
incompleteness. In contrast, textual encoding approaches, e.g., KG-BERT, resort
to graph triple's text and triple-level contextualized representations. They
are generalizable enough and robust to the incompleteness, especially when
coupled with pre-trained encoders. But two major drawbacks limit the
performance: (1) high overheads due to the costly scoring of all possible
triples in inference, and (2) a lack of structured knowledge in the textual
encoder. In this paper, we follow the textual encoding paradigm and aim to
alleviate its drawbacks by augmenting it with graph embedding techniques -- a
complementary hybrid of both paradigms. Specifically, we partition each triple
into two asymmetric parts as in translation-based graph embedding approach, and
encode both parts into contextualized representations by a Siamese-style
textual encoder. Built upon the representations, our model employs both
deterministic classifier and spatial measurement for representation and
structure learning respectively. Moreover, we develop a self-adaptive ensemble
scheme to further improve the performance by incorporating triple scores from
an existing graph embedding model. In experiments, we achieve state-of-the-art
performance on three benchmarks and a zero-shot dataset for link prediction,
with highlights of inference costs reduced by 1-2 orders of magnitude compared
to a textual encoding method.
