  The construction of large open knowledge bases (OKBs) is integral to many
knowledge-driven applications on the world wide web such as web search.
However, noun phrases and relational phrases in OKBs often suffer from
redundancy and ambiguity, which calls for the investigation on OKB
canonicalization. Current solutions address OKB canonicalization by devising
advanced clustering algorithms and using knowledge graph embedding (KGE) to
further facilitate the canonicalization process. Nevertheless, these works fail
to fully exploit the synergy between clustering and KGE learning, and the
methods designed for these subtasks are sub-optimal. To this end, we put
forward a multi-task learning framework, namely MulCanon, to tackle OKB
canonicalization. In addition, diffusion model is used in the soft clustering
process to improve the noun phrase representations with neighboring
information, which can lead to more accurate representations. MulCanon unifies
the learning objectives of these sub-tasks, and adopts a two-stage multi-task
learning paradigm for training. A thorough experimental study on popular OKB
canonicalization benchmarks validates that MulCanon can achieve competitive
canonicalization results.
