Recent years have witnessed increasing attention on the semantic knowledge integration between curated knowledge bases (CKBs) and open knowledge bases (OKBs), which is non-trivial due to the intrinsically heterogeneous features involved in CKBs and OKBs. OKB canonicalization and OKB linking are regarded as two vital tasks to achieve the knowledge integration. Although these two tasks are inherently complementary with each other, previous studies just solve them separately or via superficial interaction. To address this issue, we propose CLUE+, a novel framework that jointly encodes the OKB and CKB into a unified embedding space, to tackle OKB canonicalization and OKB linking simultaneously and make them benefit each other reciprocally. We design an expectation-maximization (EM) based approach to iteratively refine the unified embedding space via performing seed generation and embedding refinement alternately, by leveraging the deep interaction between OKB canonicalization and OKB linking. Curriculum learning is employed to yield high-quality canonicalization seeds and linking seeds adaptively, according to two elaborately designed metrics (i.e., a margin-based linking metric and an entropy-based cluster metric). Additionally, active learning is incorporated to further complement the seed generation process by selectively annotating the most informative noun phrases within low-quality clusters, driven by an innovative acquisition function comprising three key criteria (i.e., uncertainty, diversity and specificity). A thorough experimental study over two public benchmark data sets demonstrates that our proposed CLUE+ consistently outperforms state-of-the-art baselines for the task of OKB canonicalization (resp. OKB linking) in terms of average F1 (resp. accuracy).

Fine-grained emotion classification (FEC) aims to analyze speakers' utterances and distinguish dozens of emotions with subtle differences, allowing for a more nuanced understanding of human emotional states.However, compared to traditional coarse-grained emotion classification, two difficulties arise as the granularity of emotions becomes finer, i.e., the presence of closely confusable emotions which are hard to distinguish, and the biased performance caused by long-tailed emotions.Although addressing both difficulties is vital to FEC, previous studies have predominantly focused on dealing with only one of them.In this paper, we propose TACO, a novel triple-view framework that treats FEC as an instance-label (i.e., utteranceemotion) joint embedding learning problem to tackle both difficulties concurrently by considering three complementary views.Specifically, we design a clustering-guided contrastive loss, which incorporates clustering techniques to guide the contrastive learning process and facilitate more discriminative instance embeddings.Additionally, we introduce the emotion label description as a helpful resource to refine label embeddings and mitigate the poor performance towards under-represented (i.e., longtailed) emotions.Extensive experiments on two widely-used benchmark datasets demonstrate that our proposed TACO achieves substantial and consistent improvements compared to other competitive baseline methods.