Cross-domain Knowledge Research Articles

Adaptive Graph Learning with Semantic Promotability for Domain Adaptation.

Domain Adaptation (DA) is used to reduce cross-domain differences between the labeled source and unlabeled target domains. As the existing semantic-based DA approaches mainly focus on extracting consistent knowledge under semantic guidance, they may fail in acquiring (a) personalized knowledge between intra-class samples, and (b) local knowledge of neighbor samples from different categories. Hence, a multi-semantic-granularity and target-sample oriented approach, called Adaptive Graph Learning with Semantic Promotability (AGLSP), is proposed, which consists of three parts: (a) Adaptive Graph Embedding with Semantic Guidance (AGE-SG) that adaptively estimates the promotability of target samples and learns variant semantic and geometrical components from the source and those semantically promotable target samples; (b) Semantically Promotable Sample Enhancement (SPSE) that further increases the discriminability and adaptability of tag granularity by mining the features of intra-class source and semantically promotable target samples with multi-granularities; and (c) Adaptive Graph Learning with Implicit Semantic Preservation (AGL-ISP) that forms the tag granularity by extracting commonalities between the source and those semantically non-promotable target samples. As AGLSP learns more semantics from the two domains, more cross-domain knowledge is transferred. Mathematical proofs and extensive experiments on seven datasets demonstrate the performance of AGLSP.

OTIEA:Ontology-Enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment

Cross-lingual and cross-domain knowledge alignment without sufficient external resources is a fundamental and crucial task for fusing irregular message. Aiming to discover equivalent objects from different knowledge graphs (KGs), embedding-based entity alignment (EA) has been attracting great interest from industry and academic research recently. Most of related methods usually explore the correlation between entities and relations through neighbor nodes, structural information and external resources. However, the complex intrinsic interactions among triple elements and role information are rarely modeled, which leads to the inadequate illustration. In addition, external resources are unavailable in some scenarios especially cross-lingual and cross-domain applications, which reflects the weak scalability. To tackle the above insufficiency, a novel universal EA framework (OTIEA) based on ontology pair and role enhancement mechanism via triple-aware attention is proposed in this paper without introducing external resources. Specifically, an ontology-enhanced triple encoder is designed via mining intrinsic correlations and ontology pair information instead of independent elements. In addition, the EA-oriented representations can be obtained in triple-aware entity decoder by fusing role diversity. Finally, a bidirectional iterative alignment strategy is deployed to expand seed entity pairs. The experimental results on three real-world datasets show that our framework achieves a competitive performance compared with baselines.

Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.

Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.

LLMCDSR: Enhancing Cross-Domain Sequential Recommendation with Large Language Models

Cross-Domain Sequential Recommendation (CDSR) aims to predict users’ preferences based on historical sequential interactions across multiple domains. Existing works focus on the overlapped users who interact in multiple domains to capture the cross-domain correlations. These methods often underperform in practical scenarios featuring both overlapped and non-overlapped users due to the limited cross-domain interactions and knowledge transfer misalignment for non-overlapped users. To address this, we leverage Large Language Models (LLMs) to facilitate CDSR by fully exploiting single-domain interactions. However, LLMs exhibit inherent limitations in handling extensive item repositories and sequential collaborative signals. Moreover, the generation reliability is compromised by the hallucination problem, potentially causing noisy and unstable outputs. To this end, we propose a novel LLMCDSR framework, which employs LLMs to predict unobserved cross-domain interactions, termed pseudo items, within single-domain interactions. Specifically, we first prompt LLMs to execute the Candidate-Free Cross-Domain Interaction Generation task. Then, we devise a Collaborative-Textual Contrastive Pre-Training strategy, learning to infuse collaborative information into textual features. Afterwards, we present a novel Relevance-Aware Meta Recall Network (RMRN) to selectively identify and retrieve high-quality pseudo items from the dataset, where the parameters are optimized in a meta-learning manner. Finally, extensive experiments on two public datasets validate the effectiveness of LLMCDSR in enhancing CDSR.

Quantifying Interdisciplinarity in Scientific Articles Using Deep Learning Toward a TRIZ-Based Framework for Cross-Disciplinary Innovation

Interdisciplinary research (IDR) is essential for addressing complex global challenges that surpass the capabilities of any single discipline. However, measuring interdisciplinarity remains challenging due to conceptual ambiguities and inconsistent methodologies. To overcome these challenges, we propose a deep learning approach that quantifies interdisciplinarity in scientific articles through semantic analysis of titles and abstracts. Utilizing the Semantic Scholar Open Research Corpus (S2ORC), we leveraged metadata field tags to categorize papers as either interdisciplinary or monodisciplinary, establishing the foundation for supervised learning in our model. Specifically, we preprocessed the textual data and employed a Text Convolutional Neural Network (Text CNN) architecture to identify semantic patterns indicative of interdisciplinarity. Our model achieved an F1 score of 0.82, surpassing baseline machine learning models. By directly analyzing semantic content and incorporating metadata for training, our method addresses the limitations of previous approaches that rely solely on bibliometric features such as citations and co-authorship. Furthermore, our large-scale analysis of 136 million abstracts revealed that approximately 25% of the literature within the specified disciplines is interdisciplinary. Additionally, we outline how our quantification method can be integrated into a TRIZ-based (Theory of Inventive Problem Solving) methodological framework for cross-disciplinary innovation, providing a foundation for systematic knowledge transfer and inventive problem solving across domains. Overall, this approach not only offers a scalable measurement of interdisciplinarity but also contributes to a framework for facilitating innovation through structured cross-domain knowledge integration.

Ontology-Driven Mixture-of-Domain Documentation: A Backbone Approach Enabling Question Answering for Additive Construction

Advanced construction techniques, such as additive manufacturing (AM) and modular construction, offer promising solutions to address labor shortages, reduce CO2 emissions, and enhance material efficiency. Despite their potential, the adoption of these innovative methods is hindered by the construction industry’s fragmented expertise. Building Information Modeling (BIM) is frequently suggested to integrate this diverse knowledge, but existing BIM-based approaches lack a robust framework for systematically documenting and retrieving the cross-domain knowledge essential for construction projects. To bridge this gap, this paper presents an ontology-driven methodology for documenting and utilizing expert knowledge, with a focus on AM in construction. Based on a well-founded ontological framework, a set of modular ontologies is formalized for individual domains. Additionally, a prototypical documentation tool is developed to elevate recorded information and BIM models as a knowledge graph. This knowledge graph will interface with advanced large language models (LLMs), enabling effective question answering and knowledge retrieval.

FireExpert: Fire Event Identification and Assessment Leveraging Cross-Domain Knowledge and Large Language Model

FireExpert: Fire Event Identification and Assessment Leveraging Cross-Domain Knowledge and Large Language Model

Prompt-Driven Latent Domain Generalization for Medical Image Classification.

Deep learning models for medical image analysis easily suffer from distribution shifts caused by dataset artifact bias, camera variations, differences in the imaging station, etc., leading to unreliable diagnoses in real-world clinical settings. Domain generalization (DG) methods, which aim to train models on multiple domains to perform well on unseen domains, offer a promising direction to solve the problem. However, existing DG methods assume domain labels of each image are available and accurate, which is typically feasible for only a limited number of medical datasets. To address these challenges, we propose a unified DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG). PLDG consists of unsupervised domain discovery and prompt learning. This framework first discovers pseudo domain labels by clustering the bias-associated style features, then leverages collaborative domain prompts to guide a Vision Transformer to learn knowledge from discovered diverse domains. To facilitate cross-domain knowledge learning between different prompts, we introduce a domain prompt generator that enables knowledge sharing between domain prompts and a shared prompt. A domain mixup strategy is additionally employed for more flexible decision margins and mitigates the risk of incorrect domain assignments. Extensive experiments on three medical image classification tasks and one debiasing task demonstrate that our method can achieve comparable or even superior performance than conventional DG algorithms without relying on domain labels. Our code is publicly available at https://github.com/SiyuanYan1/PLDG/tree/main.

A cross-domain knowledge tracing model based on graph optimal transport

A cross-domain knowledge tracing model based on graph optimal transport

Temporal dual-target cross-domain recommendation framework for next basket recommendation

Next Basket Recommender systems in e-commerce face challenges such as data sparsity, evolving user preferences, and cross-domain transfer limitations. We propose the Temporal Dual-Target Cross-Domain Recommendation Framework (T-DualCRF) to address these issues. T-DualCRF integrates multi-channel embeddings (user feedback, knowledge graphs, temporal features) and a dual-target mechanism for robust cross-domain knowledge transfer. It also employs time-aware embeddings and a temporal heterogeneous graph to model user preference changes. The framework’s hybrid optimization mechanism, combining the Multi-Verse Optimizer and Whale Optimization Algorithm, enhances recommendation accuracy and stability. Experimental results on Amazon datasets show that T-DualCRF significantly outperforms existing models, with improvements of up to 20% in F1-score and 17% in NDCG, effectively mitigating data sparsity and adapting to real-time user behavior changes.

Cross-domain Constituency Parsing by Leveraging Heterogeneous Data

Knowledge transfer is investigated in various natural language processing tasks except cross-domain constituency parsing. In this paper, we leverage heterogeneous data to transfer cross-domain and cross-task knowledge to constituency parsing. Concretely, we first select language modeling, named entity recognition, CCG supertagging and dependency parsing as auxiliary tasks and collect the corpora of these tasks covering various domains as cross-domain and cross-task heterogeneous data. Second, we exploit three types of prefixes: shared, task and domain prefix, to merge cross-domain and cross-task data and decompose the general, task and domain representation in the pretrained language model. Third, we convert the data formats of multi-source heterogeneous datasets and loss objectives of the auxiliary tasks into a consistent formalization closer to constituency parsing. Finally, we jointly train the model to transfer task and domain knowledge to cross-domain constituency parsing. We verify the effectiveness of our proposed model on five target domains of MCTB. Experimental results show that our knowledge transfer model outperforms various baseline models, including conventional chart-based and transition-based parsers and the current large-scale language model for zero-shot and few-shot settings.

Using cross-domain knowledge augmentation to explore comorbidity in electronic health records data

Using cross-domain knowledge augmentation to explore comorbidity in electronic health records data

Cross-Domain Mutual-Assistance Learning Framework for Fully Automated Diagnosis of Primary Tumor in Nasopharyngeal Carcinoma.

Accurate T-staging of nasopharyngeal carcinoma (NPC) holds paramount importance in guiding treatment decisions and prognosticating outcomes for distinct risk groups. Regrettably, the landscape of deep learning-based techniques for T-staging in NPC remains sparse, and existing methodologies often exhibit suboptimal performance due to their neglect of crucial domain-specific knowledge pertinent to primary tumor diagnosis. To address these issues, we propose a new cross-domain mutual-assistance learning framework for fully automated diagnosis of primary tumor using H&N MR images. Specifically, we tackle primary tumor diagnosis task with the convolutional neural network consisting of a 3D cross-domain knowledge perception network (CKP net) for excavated cross-domain-invariant features emphasizing tumor intensity variations and internal tumor heterogeneity, and a multi-domain mutual-information sharing fusion network (M2SF net), comprising a dual-pathway domain-specific representation module and a mutual information fusion module, for intelligently gauging and amalgamating multi-domain, multi-scale T-stage diagnosis-oriented features. The proposed 3D cross-domain mutual-assistance learning framework not only embraces task-specific multi-domain diagnostic knowledge but also automates the entire process of primary tumor diagnosis. We evaluate our model on an internal and an external MR images dataset in a three-fold cross-validation paradigm. Exhaustive experimental results demonstrate that our method outperforms the other algorithms, and obtains promising performance for tumor segmentation and T-staging. These findings underscore its potential for clinical application, offering valuable assistance to clinicians in treatment decision-making and prognostication for various risk groups.

Enhancing few-shot lifelong learning through fusion of cross-domain knowledge

Enhancing few-shot lifelong learning through fusion of cross-domain knowledge

Federating cross-domain BIM-based knowledge graph

Despite decades of the digitization of the construction industry, interoperability challenges prevail. One of which is the lack of sufficient integration between domain-specific BIM models. Therefore, the paper proposes a novel methodology for federating cross-domain BIM-based knowledge graphs. The approach leverages the integration of multiple models into a single knowledge graph, aiming to bridge the existing gaps in data exchange and enhance the semantic richness of building information models. Through a detailed exploration of existing data exchange mechanisms, popular ontologies, and schema limitations, this research addresses the interoperability dilemma. A practical demonstration, encapsulated in the “LBD-Online-Merge” demo application, validates the methodology’s efficacy in real-world scenarios, illustrating its potential to revolutionize building operations management and data preparation for the operational phase of a building’s lifecycle. This study significantly contributes to the academic and practical discourse by providing a robust framework for achieving a more integrated, intelligent building information management ecosystem, setting a new benchmark for future research and applications in constructing semantically rich digital twins of the built environment.

Enhancing Recommendation Diversity and Novelty with Bi-LSTM and Mean Shift Clustering

In the digital age, personalized recommendation systems have become crucial for information dissemination and user experience. While traditional systems focus on accuracy, they often overlook diversity, novelty, and serendipity. This study introduces an innovative recommendation system model, Time-based Outlier Aware Recommender (TOAR), designed to address the challenges of content homogenization and information bubbles in personalized recommendations. TOAR integrates Neural Matrix Factorization (NeuMF), Bidirectional Long Short-Term Memory Networks (Bi-LSTM), and Mean Shift clustering to enhance recommendation accuracy, novelty, and diversity. The model analyzes temporal dynamics of user behavior and facilitates cross-domain knowledge exchange through feature sharing and transfer learning mechanisms. By incorporating an attention mechanism and unsupervised clustering, TOAR effectively captures important time-series information and ensures recommendation diversity. Experimental results on a news recommendation dataset demonstrate TOAR’s superior performance across multiple metrics, including AUC, precision, NDCG, and novelty, compared to traditional and deep learning-based recommendation models. This research provides a foundation for developing more intelligent and personalized recommendation services that balance accuracy with content diversity.

KGFabric: A Scalable Knowledge Graph Warehouse for Enterprise Data Interconnection

Based on the diversified application scenarios at Ant Group, we built the Ant Knowledge Graph Platform (AKGP). It has constructed numerous domain-specific knowledge graphs related to merchants, companies, accounts, products, and more. AKGP manages trillions of structured knowledge graphs, serving search, recommendation, risk control and other businesses. However, as the demand increasing for various workloads such as graph pattern matching, graph representation learning, and cross-domain knowledge reuse, the existing warehouse systems based on relational DBMS or graph databases are unable to meet the requirements. To address these issues, we propose KGFabric, an industrial-scale knowledge graph management system built on the distributed file system (DFS). KGFabric offers a nearline knowledge storage engine that utilizes a Semantic-enhanced Programmable Graph (SPG) model, which is compatible with the Labeled Property Graph (LPG) model. The data is persistently stored in DFS, such as HDFS, which leverages the POSIX file system API, making it suitable for deployment in multi-cloud environment at low cost. KGFabric provides a native graph-based and hybrid storage format that can serve as a shared backend for parallel graph computing systems, significantly accelerating the analysis of multi-workload. Additionally, KGFabric includes a graph fabric framework that minimizes data duplication and guarantees data security. KGFabric is able to manage Peta-scale data and has supported graph fabric and analysis with over 100 billion relations at Ant Group. We conduct experiments on various datasets to evaluate the performance of KGFabric. Compared with popular relational DBMS and graph databases, the storage space for semantic relations is reduced by over 90%. The performance of graph fabric improves by 21× in real-world workloads. In multi-hop semantic graph analysis, KGFabric enhances performance by 100×.

Co-creation in action: Bridging the knowledge gap in artificial intelligence among innovation champions

Co-creation in action: Bridging the knowledge gap in artificial intelligence among innovation champions

Cross-Domain Knowledge Transfer for Sustainable Heterogeneous Industrial Internet-of-Things Networks.

In this article, a novel cross-domain knowledge transfer method is implemented to optimize the tradeoff between energy consumption and information freshness for all pieces of equipment powered by heterogeneous energy sources within smart factory. Three distinct groups of use cases are considered, each utilizing a different energy source: grid power, green energy source, and mixed energy sources. Differing from mainstream algorithms that require consistency among groups, the proposed method enables knowledge transfer even across varying state and/or action spaces. With the advantage of multiple layers of knowledge extraction, a lightweight knowledge transfer is achieved without the need for neural networks. This facilitates broader applications in self-sustainable wireless networks. Simulation results reveal a notable improvement in the 'warm start' policy for each equipment, manifesting as a 51.32% increase in initial reward compared to a random policy approach.

A patent retrieval method and system based on double classification

A patent retrieval method and system based on double classification