Safe operation is a key concern for industrial robots. However, due to hardware failures and unstable data transmission issues, the multivariate time-series data generated by these axes often contain missing or corrupted signals, which severely hinders downstream tasks such as fault diagnosis. Additionally, the substantial volume of industrial data demands considerable time for training time-series imputation models and subsequent classification models. To address these challenges, this study proposes a multitask approach that serves both the data imputation and fault diagnosis tasks for industrial robots. Specifically, the parameters trained in the imputation model can be transferred to the fault diagnosis model, enhancing its performance and efficiency. A multitask method named Bidirectional-Graph Attention Networks Parallel Encoder (Bi-GATPE) is proposed, which employs a bidirectional graph attention network to capture the spatial dependencies among the various variables of industrial robots. Subsequently, a parallel encoder with Diagonal-Filter Attention is designed to model temporal correlations. This dual approach improves the accuracy and training speed for both the imputation and fault diagnosis tasks. Experimental studies based on real industrial robot datasets demonstrate that by modifying the feature fusion layer of the imputation task and sharing the trained parameters with the fault diagnosis task, the proposed method significantly accelerates the convergence of the fault diagnosis model while also improving diagnostic accuracy. The experiments also indicate that our method shows merits in the imputation and fault diagnosis tasks. The source code of Bi-GATPE is available at:https://github.com/miten073/Bi-GATPE.

Large language models (LLMs) have shown remarkable performances in generic question-answering (QA) but often suffer from domain gaps and outdated knowledge in smart manufacturing (SM). Retrieval-augmented generation (RAG) based on LLMs has emerged as a potential approach by incorporating an external knowledge base. However, conventional vector-based RAG delivers rapid responses but often returns contextually vague results, while knowledge graph (KG)-based methods offer structured relational reasoning at the expense of scalability and efficiency. To address these challenges, a hybrid KG-Vector RAG framework that systematically integrates structured KG metadata with unstructured vector retrieval is proposed. Firstly, a metadata-enriched KG was constructed from domain corpora by systematically extracting and indexing structured information to capture essential domain-specific relationships. Secondly, semantic alignment was achieved by injecting domain-specific constraints to refine and enhance the contextual relevance of the knowledge representations. Lastly, a layered hybrid retrieval strategy was employed that combined the explicit reasoning capabilities of the KG with the efficient search power of vector-based similarity methods, and the resulting outputs were integrated via prompt engineering to generate comprehensive, context-aware responses. Evaluated on design for additive manufacturing (DfAM) tasks, the proposed approach achieved 77.8% exact match accuracy and 76.5% context precision. This study establishes a new paradigm for industrial LLM systems, which demonstrates that hybrid symbolic-neural architectures can overcome the precision-scalability trade-off in mission-critical manufacturing applications. Experimental results indicated that integrating structured KG information with vector-based retrieval and prompt engineering can enhance retrieval accuracy, contextual relevance, and efficiency in LLM-based Q&A systems for SM.

Translating natural language inquiries into executable Cypher queries (text-to-Cypher) is a persistent bottleneck for non-technical teams relying on knowledge graphs (KGs) in fast-changing industrial settings. Rule and template converters need frequent updates as schemas evolve, while supervised and fine-tuned parsers require recurring training. This study proposes a schema-guided prompting approach, namely text-to-Cypher with semantic schema (T2CSS), to align large language models (LLMs) with domain knowledge for producing accurate Cypher. T2CSS distils a domain ontology into a lightweight semantic schema and uses adaptive filtering to inject the relevant subgraph and essential Cypher rules into the prompt for constraining generation and reducing schema-agnostic errors. This design keeps the prompt focused and within context length limits while providing the necessary domain grounding. Comparative experiments demonstrate that T2CSS with GPT-4 outperformed baseline models and achieved 86 % accuracy in producing correct Cypher queries. In practice, this study reduces retraining and maintenance effort, shortens turnaround times, and broadens KG access for non-experts. • A T2CSS prompting approach that guides LLMs with the domain schema is proposed. • A systematic semantic schema to cover multifaceted concepts is designed. • An information filtering mechanism to select the relevant information is proposed. • Results achieve 86 % accuracy in translating user inquiries to Cypher statements.

Sequential Recommendation (SR) aims to understand user preferences by analyzing historical interactions with items. Recent approaches have shifted from the time domain to the frequency domain to potentially enhance preference modeling. While fast Fourier transform is a common choice for frequency transform, it may introduce issues like the Gibbs phenomenon, leading to potentially suboptimal model performance. To address this, we introduce discrete cosine transform into sequential recommendation and present a novel multi-frequency transformation sequential recommendation, named HMFTRec, within a hierarchical framework. Specifically, we develop a discrete cosine transform module base on channel attention. A hierarchical spectrum framework that combines Fourier and discrete cosine transforms is introduced to capture finer-grained frequency domain information and mitigate the Gibbs phenomenon to some extent. Furthermore, contrastive learning is employed to potentially enhance the quality of user embeddings learned from the frequency domain. Extensive experiments conducted on four widely recognized benchmark datasets demonstrate that our model significantly outperforms state-of-the-art approaches.

Nested Named Entity Recognition (NNER) focuses on addressing overlapped entity recognition. Compared to Flat Named Entity Recognition (FNER), annotated resources are scarce in the corpus for NNER. Data augmentation is an effective approach to address the insufficient annotated corpus. However, there is a significant lack of exploration in data augmentation methods for NNER. Due to the presence of nested entities in NNER, existing data augmentation methods cannot be directly applied to NNER tasks. Therefore, in this work, we focus on data augmentation for NNER and resort to more expressive structures, Composited-Nested-Label Classification (CNLC) in which constituents are combined by nested-word and nested-label, to model nested entities. The dataset is augmented using the Composited-Nested-Learning (CNL). In addition, we propose the Confidence Filtering Mechanism (CFM) for a more efficient selection of generated data. Experimental results demonstrate that this approach results in improvements in ACE2004 and ACE2005 and alleviates the impact of sample imbalance.

Distantly supervised relation extraction (DSRE) aims to automatically identify relation facts from unstructured text. Most current DSRE works solve the noise problem based on the bag-level, but the denoising ability of these methods decreases when the bag consists of fewer sentences. In this study, we propose a Distantly supervised Relation extraction with Label Prompt (DRLP) framework. We use textual labels (such as label names) as label prompts to alleviate the problem of decreased denoising ability by utilizing the information of entities and relations in label names. During the training process, label prompts are directly connected to the sentences in the bag to provide a more comprehensive bag representation, and label prompts are randomly deleted based on the number of sentences in the bag. Moreover, we design a residual selective attention mechanism that minimizes the influence of spurious features and optimizes the utilization of label information. Our framework is evaluated on NYT-10d and NYT-10m, the results indicate that our method outperforms the state-of-the-art methods.