Noun phrases (NPs) in open knowledge bases (OKBs) are not canonicalized, leading to scattered knowledge that necessitates the exploration of the OKB canonicalization task (i.e., clustering synonymous noun phrases into the same group and assigning them a unique identifier). However, existing OKB canonicalization methods typically adhere to a traditional embedding-centered pipeline, which fails to exploit the direct interaction between NPs for pairwise NP similarity calculations, resulting in suboptimal performance and instead relying extensively on external resources. To address these limitations, we introduce a groundbreaking retrieve-read-group paradigm that enables fine-grained pairwise NP similarity calculations by effectively leveraging the direct NP interaction via the reading stage, thereby relieving the reliance on external resources. As an instantiation of this paradigm, we propose DUVK, a novel self-supervised framework that fully integrates the dual-view knowledge involved in OKBs from the relational view and the semantic view. In the retriever component of DUVK, a dual-view cross-training strategy is designed to make two view-specific encoders mutually reinforce each other by capitalizing on the complementary knowledge delivered from both views. Experimental results demonstrate that, even without the need of any external resources, DUVK outperforms all state-of-the-art competitors that rely on such resources.

Conventional fine-grained entity typing (FET) operates under the closed-set assumption, wherein all classified types are limited within a predefined type taxonomy derived from a knowledge base.As the world evolves, new entities of unknown types inevitably emerge in open environments, falling beyond the scope of the existing type taxonomy.To deal with this problem, in this paper, we investigate a novel and critical task: open-set entity typing (OSET), which aims to not only classify entity mentions within the known type taxonomy but also detect those outside it, termed as unknowntype instances.However, owing to the lack of exposure to unknown-type instances during training, existing FET models are susceptible to misclassify them as known types, limiting their practical effectiveness for this new OSET task.Moreover, manually collecting and annotating large-scale unknown-type instances is both time-consuming and labor-intensive in open environments.To mitigate this issue, we propose a two-stage generation model that automatically produces large-scale, high-quality and diverse pseudo unknown-type instances, beneficial for the tailor-designed unified openset classifier to effectively distinguish between known and unknown types.Furthermore, an innovative unknown-aware hierarchical contrastive learning strategy is designed to facilitate a clear delineation between closely related known types and unknown types.Extensive experiments on two newly established benchmark datasets demonstrate that our proposed framework significantly surpasses all baselines in addressing the OSET task.

Distance Comparison Operations (DCOs), which decide whether the distance between a data vector and a query is within a threshold, are a critical performance bottleneck in vector similarity search. Recent DCO methods that avoid full-dimensional distance computations promise significant speedups, but their readiness for production vector database systems remains an open question. To address this, we conduct a comprehensive benchmark of 8 DCO algorithms across 10 datasets (with up to 100M vectors and 12,288 dimensions) and diverse hardware configurations (CPUs with/without SIMD, and GPUs). Our study reveals that these methods are not silver bullets: their efficiency is highly sensitive to data dimensionality, degrades under out-of-distribution queries, and is unstable across hardware. Yet, our evaluation also demonstrates often-overlooked merits: they can accelerate index construction and data updates. Despite these benefits, their unstable performance, which can be slower than a full-dimensional scan, leads us to conclude that recent algorithmic advancements in DCO are not yet ready for production deployment.

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.

Vectors are used to represent unstructured data with their embeddings and associated attributes. Similarity search over large-scale vector datasets has gained significant interest from both industry and academia. It aims to identify the k nearest neighbors to a query object from vectors that satisfy a given attribute filter constraint. Despite its popularity, most solutions focus on single-sourced data and overlook the need for vector retrieval across federated datasets. To fill this gap, we introduce a new problem, federated vector similarity search with filters, which enables privacy-preserving vector retrieval over multi-sourced data held by mutually untrusted providers. While some solutions can be adapted, they struggle with low recall, excessive search latency, or high communication cost. To address these challenges, we propose FedVS, a privacy-preserving framework enhanced with indexing and pruning based on Trusted Execution Environment (TEE). We also provide a comprehensive theoretical analysis, including complexity, security, and approximation guarantees for recall. Moreover, we deploy our solution over real-world vector databases and conduct extensive experiments. The results demonstrate that our solution outperforms state-of-the-art methods in both effectiveness and efficiency.

Federated continual learning (FCL) aims to maintain the model's performance on old tasks (i.e., stability) while enhancing its ability to acquire knowledge from current tasks (i.e., plasticity). With the development of pre-trained models (PTMs), fine-tuning PTMs on clients has become a promising approach to leveraging their extensive knowledge in FCL. In this paper, we propose MultiFCL, a novel FCL framework that fine-tunes PTMs to adapt to FCL while preserving their strong generalization capabilities. Specifically, to ensure the stability, MultiFCL introduces lightweight adapters for task adaption, which are subsequently frozen to prevent catastrophic forgetting. Moreover, by utilizing the semantic features of old tasks, MultiFCL performs multi-modal initialization of new task class prototypes. To enhance the plasticity, MultiFCL employs a multi-expert training mechanism that integrates multi-scale feature learning with multi-teacher dynamic self-distillation. Through intra-client and inter-client expert communication, MultiFCL facilitates cross-task and cross-client knowledge fusion. Experimental results demonstrate that MultiFCL achieves state-of-the-art performance across multiple datasets and settings, showcasing its effectiveness in FCL scenarios.

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). 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).

In recent years, with the rapid development of renewable energy, power energy storage has become a bottom neck of its development, and superconducting magnetic energy storage (SMES) seems to be a viable solution. A topology based on common DC voltage bus is proposed for high power voltage source converter (VSC)-SMES. The mathematical model of SMES is constructed and the detailed control strategy is derived. According to the characteristics of SMES, dual mode switching control strategy is adopted. Simulations under different conditions are carried out with PSIM simulation tool. The fast response of SMES and effectiveness of dual-mode switch control are verified by simulation waveforms and the active and reactive power can be controlled independently.