  Although product graphs (PGs) have gained increasing attentions in recent
years for their successful applications in product search and recommendations,
the extensive power of PGs can be limited by the inevitable involvement of
various kinds of errors. Thus, it is critical to validate the correctness of
triples in PGs to improve their reliability. Knowledge graph (KG) embedding
methods have strong error detection abilities. Yet, existing KG embedding
methods may not be directly applicable to a PG due to its distinct
characteristics: (1) PG contains rich textual signals, which necessitates a
joint exploration of both text information and graph structure; (2) PG contains
a large number of attribute triples, in which attribute values are represented
by free texts. Since free texts are too flexible to define entities in KGs,
traditional way to map entities to their embeddings using ids is no longer
appropriate for attribute value representation; (3) Noisy triples in a PG
mislead the embedding learning and significantly hurt the performance of error
detection. To address the aforementioned challenges, we propose an end-to-end
noise-tolerant embedding learning framework, PGE, to jointly leverage both text
information and graph structure in PG to learn embeddings for error detection.
Experimental results on real-world product graph demonstrate the effectiveness
of the proposed framework comparing with the state-of-the-art approaches.
