Generating SQLs from user queries is a long-standing challenge, where the accuracy of initial schema linking significantly impacts subsequent SQL generation performance. However, current schema linking models still struggle with missing relevant schema elements or an excess of redundant ones. A crucial reason for this is that commonly used metrics, recall and precision, fail to capture relevant element missing and thus cannot reflect actual schema linking performance. Motivated by this, we propose enhanced schema linking metrics by introducing a \textbf{restricted missing indicator}. Accordingly, we introduce \textbf{\underline{K}n\underline{a}psack optimization-based \underline{S}chema \underline{L}inking \underline{A}pproach (KaSLA)}, a plug-in schema linking method designed to prevent the missing of relevant schema elements while minimizing the inclusion of redundant ones. KaSLA employs a hierarchical linking strategy that first identifies the optimal table linking and subsequently links columns within the selected table to reduce linking candidate space. In each linking process, it utilizes a knapsack optimization approach to link potentially relevant elements while accounting for a limited tolerance of potentially redundant ones. With this optimization, KaSLA-1.6B achieves superior schema linking results compared to large-scale LLMs, including DeepSeek-V3 with the state-of-the-art (SOTA) schema linking method. Extensive experiments on Spider and BIRD benchmarks verify that KaSLA can significantly improve the SQL generation performance of SOTA Text2SQL models by substituting their schema linking processes. The code is available at https://github.com/DEEP-PolyU/KaSLA.

Retrieval-Augmented Generation (RAG) is a promising paradigm for domain-specific knowledge integration. However, applying generic RAG frameworks to power grid operation and maintenance (O&M) faces critical challenges due to the strict physical constraints of electrical networks. We identify a fundamental Topological Association Modeling Deficiency in existing methods, manifesting as Retrieval Linkage Degradation (where semantic ambiguity severs links between abstract intents and specific technical entities) and Topological Reasoning Deficiency (failing to adhere to rigid physical interlocking rules, causing fatal safety violations). To address these bottlenecks, we introduce the Power Grid Work Ticket (PGWT) dataset to systematically evaluate topological reasoning under real-world noise and sparsity. Furthermore, we propose Topology-Semantic Aligned Retrieval (TSA-Retrieval). This framework harmonizes semantic spaces with topological structures by fusing symbolic pattern matching with subgraph structural encoding to ensure physical validity, while explicitly injecting topology-aware reasoning chains to guide constraint-compliant generation. Extensive experiments demonstrate that TSA-Retrieval significantly outperforms state-of-the-art LLMs and generic RAG baselines, offering a robust and scalable solution for safety-critical applications.

Equipping large language models (LLMs) with external tools has emerged as a promising paradigm for addressing real-world tasks. Nonetheless, with the web-based tool ecosystems rapidly expanding, it is impractical to include all tools within the limited input length of LLMs. To alleviate these issues, researchers have explored incorporating a tool retrieval module to select the most relevant tools or represent tools as unique tokens within LLM parameters. However, most state-of-the-art methods are under transductive settings, assuming all tools have been observed during training. Such a setting deviates from reality as tools on the web are constantly updated and new tools are frequently added to the online ecosystem. When dealing with these unseen tools, which refer to tools not encountered during the training phase, these methods are limited by two key issues, including the large distribution shift and the sensitivity of semantic-only retrieval. To this end, inspired by human cognitive processes of mastering unseen tools through discovering and applying the logical information from prior experience, we introduce a novel Logic-Guided Semantic Bridging framework for inductive tool retrieval, namely, LoSemB, which aims to mine and transfer latent logical information for inductive tool retrieval without costly retraining. Specifically, LoSemB contains a logic-based embedding alignment module to mitigate distribution shifts and a relational augmented retrieval mechanism to overcome the limitations of semantic-only similarity methods. Extensive experiments demonstrate that LoSemB achieves advanced performance in both the inductive and transductive settings.

Click-through rate prediction tasks estimate interaction probabilities using user–item features (i.e., the combined set of user and item features). LLMs have emerged as a promising approach by organizing these features into prompts and fine-tuning LLMs to predict the interaction label. However, our feature-wise and interaction-wise analysis reveals two critical limitations, leading to incomplete relationship modeling between user–item features and interaction labels: (i) LLMs tend to utilize only a subset of available features, neglecting others, and (ii) they struggle with predictions for tail items that appear less frequently in the training samples. To bridge this gap, we propose Feature-Instructed Large language model for Monitoring ( FILM ), which introduces a self-monitoring temperature mechanism that dynamically guides LLMs to focus on informative features, and an auxiliary compaction loss that facilitates better feature-interaction relationship learning for tail items. By integrating these two designs, FILM not only improves feature utilization in LLMs but also enhances predictions for tail items. Furthermore, we demonstrate that FILM -generated interaction embeddings can be transferred to lightweight models, enabling efficient deployment. Extensive experiments demonstrate that FILM achieves significant performance improvements over state-of-the-art baselines by learning better relationships between user–item features and interaction labels and generalizes under different LLM backbones.

Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of newly-coming entities that never appeared in the training set. Such a setting is more in line with reality, as real-world KGs are constantly evolving and introducing new knowledge. Recent studies have shown promising results using message passing over subgraphs to embed newly-coming entities for inductive KGC. However, the inductive capability of these methods is usually limited by two key issues. (i) KGC always suffers from data sparsity, and the situation is even exacerbated in inductive KGC where new entities often have few or no connections to the original KG. (ii) Cold-start problem. It is over coarse-grained for accurate KG reasoning to generate representations for new entities by gathering the local information from few neighbors. To this end, we propose a novel iNfOmax RelAtion Network, namely NORAN, for inductive KG completion. It aims to mine latent relation patterns for inductive KG completion. Specifically, by centering on relations, NORAN provides a hyper view towards KG modeling, where the correlations between relations can be naturally captured as entity-independent logical evidence to conduct inductive KGC. Extensive experiment results on five benchmarks show that our framework substantially outperforms the state-of-the-art KGC methods.

Knowledge graphs (KGs) can structurally organize large-scale information in the form of triples and significantly support many real-world applications. While most KG embedding algorithms hold the assumption that all triples are correct, considerable errors were inevitably injected during the construction process. It is urgent to develop effective error-aware KG embedding, since errors in KGs would lead to significant performance degradation in downstream applications. To this end, we propose a novel framework named Attributed Error-aware Knowledge Embedding (AEKE). It leverages the semantics contained in entity attributes to guide the KG embedding model learning against the impact of erroneous triples. We design two triple-level hypergraphs to model the topological structures of the KG and its attributes, respectively. The confidence score of each triple is jointly calculated based on self-contradictory within the triple, consistency between local and global structures, and homogeneity between structures and attributes. We leverage confidence scores to adaptively update the weighted aggregation in the multi-view graph learning framework and margin loss in KG embedding, such that potential errors will contribute little to KG learning. Experiments on three real-world KGs demonstrate that AEKE outperforms state-of-the-art KG embedding and error detection algorithms.

Knowledge graphs (KGs) could effectively integrate a large number of real-world assertions, and improve the performance of various applications, such as recommendation and search. KG error detection has been intensively studied since real-world KGs inevitably contain erroneous triples. While existing studies focus on developing a novel algorithm dedicated to one or a few data characteristics, we explore advancing KG error detection by assembling a set of state-of-the-art (SOTA) KG error detectors. However, it is nontrivial to develop a practical ensemble learning framework for KG error detection. Existing ensemble learning models heavily rely on labels, while it is expensive to acquire labeled errors in KGs. Also, KG error detection itself is challenging since triples contain rich semantic information and might be false because of various reasons. To this end, we propose to leverage active learning to minimize human efforts. Our proposed framework - KAEL, could effectively assemble a set of off-the-shelf error detection algorithms, by actively using a limited number of manual annotations. It adaptively updates the ensemble learning policy in each iteration based on active queries, i.e., the answers from experts. After all annotation budget is used, KAEL utilizes the trained policy to identify remaining suspicious triples. Experiments on real-world KGs demonstrate that we can achieve significant improvement when applying KAEL to assemble SOTA error detectors. KAEL also outperforms SOTA ensemble learning baselines significantly.

Knowledge graphs (KGs) have been widely used to enhance complex question answering (QA). To understand complex questions, existing studies employ language models (LMs) to encode contexts. Despite the simplicity, they neglect the latent relational information among question concepts and answers in KGs. While question concepts ubiquitously present hyponymy at the semantic level, e.g., mammals and animals, this feature is identically reflected in the hierarchical relations in KGs, e.g., a_type_of. Therefore, we are motivated to explore comprehensive reasoning by the hierarchical structures in KGs to help understand questions. However, it is non-trivial to reason over tree-like structures compared with chained paths. Moreover, identifying appropriate hierarchies relies on expertise. To this end, we propose HamQA, a novel Hierarchy-aware multi-hop Question Answering framework on knowledge graphs, to effectively align the mutual hierarchical information between question contexts and KGs. The entire learning is conducted in Hyperbolic space, inspired by its advantages of embedding hierarchical structures. Specifically, (i) we design a context-aware graph attentive network to capture context information. (ii) Hierarchical structures are continuously preserved in KGs by minimizing the Hyperbolic geodesic distances. The comprehensive reasoning is conducted to jointly train both components and provide a top-ranked candidate as an optimal answer. We achieve a higher ranking than the state-of-the-art multi-hop baselines on the official OpenBookQA leaderboard with an accuracy of 85%.

Knowledge Graph (KG) errors introduce non-negligible noise, severely affecting KG-related downstream tasks. Detecting errors in KGs is challenging since the patterns of errors are unknown and diverse, while ground-truth labels are rare or even unavailable. A traditional solution is to construct logical rules to verify triples, but it is not generalizable since different KGs have distinct rules with domain knowledge involved. Recent studies focus on designing tailored detectors or ranking triples based on KG embedding loss. However, they all rely on negative samples for training, which are generated by randomly replacing the head or tail entity of existing triples. Such a negative sampling strategy is not enough for prototyping practical KG errors, e.g., (Bruce_Lee, place_of_birth, China), in which the three elements are often relevant, although mismatched. We desire a more effective unsupervised learning mechanism tailored for KG error detection. To this end, we propose a novel framework - ContrAstive knowledge Graph Error Detection (CAGED). It introduces contrastive learning into KG learning and provides a novel way of modeling KG. Instead of following the traditional setting, i.e., considering entities as nodes and relations as semantic edges, CAGED augments a KG into different hyper-views, by regarding each relational triple as a node. After joint training with KG embedding and contrastive learning loss, CAGED assesses the trustworthiness of each triple based on two learning signals, i.e., the consistency of triple representations across multi-views and the self-consistency within the triple. Extensive experiments on three real-world KGs show that CAGED outperforms state-of-the-art methods in KG error detection. Our codes and datasets are available at https://github.com/Qing145/CAGED.git.