Publications

Extracting knowledge from Wikipedia has attracted much attention in recent ten years. One of the most valuable kinds of knowledge is type information, which refers to the axioms stating that an instance is of a certain type. Current approaches for inferring the types of instances from Wikipedia mainly rely on some language-specific rules. Since these rules cannot catch the semantic associations between instances and classes (i.e. candidate types), it may lead to mistakes and omissions in the process of type inference. The authors propose a new approach leveraging attributes to perform language-independent type inference of the instances from Wikipedia. The proposed approach is applied to the whole English and Chinese Wikipedia, which results in the first version of MulType (Multilingual Type Information), a knowledge base describing the types of instances from multilingual Wikipedia. Experimental results show that not only the proposed approach outperforms the state-of-the-art comparison methods, but also MulType contains lots of new and high-quality type information.

With the ever-growing use of textual biomedical data, domain entity annotation has become very important in biomedicine. Previous works on annotating domain entities from biomedical references suffer from several issues, such as a data flexibility problem, language dependency, and limitations with respect to word sense disambiguation. Meanwhile, the Linked Open Data (LOD) Initiative aims at interlinking data from various open knowledge bases. The numbers of entities and properties describing semantic relationships between entities within the linked data cloud have become very large. In this paper, we propose a knowledge-incentive approach for entity annotation in biomedicine, and present Me Detect, a prototype system that we developed based on this approach. With this approach, we over-come the problems of previous works using LOD-based collective annotation. Finally, we present the results of experiments that verify the effectiveness and efficiency of our approach.

Few-shot learning (FSL) presents immense potential in enhancing model generalization and practicality for medical image classification with limited training data; however, it still faces the challenge of severe overfitting in classifier training due to distribution bias caused by the scarce training samples. To address the issue, we propose MedMFG, a flexible and lightweight plug-and-play method designed to generate sufficient class-distinctive features from limited samples. Specifically, MedMFG first re-represents the limited prototypes to assign higher weights for more important information features. Then, the prototypes are variationally generated into abundant effective features. Finally, the generated features and prototypes are together to train a more generalized classifier. Experiments demonstrate that MedMFG outperforms the previous state-of-the-art methods on cross-domain benchmarks involving the transition from natural images to medical images, as well as medical images with different lesions. Notably, our method achieves over 10% performance improvement compared to several baselines. Fusion experiments further validate the adaptability of MedMFG, as it seamlessly integrates into various backbones and baselines, consistently yielding improvements of over 2.9% across all results.

The Linking Open Data (LOD) project is an ongoing effort to construct a global data space, i.e. the Web of Data. One important part of this project is to establish owl:sameAs links among structured data sources. Such links indicate equivalent instances that refer to...

'Graph Pattern Based RDF Data Compression' published in 'Semantic Technology'

No abstract is provided for this article.

Linguistic resources are essential for Language Learning applications. However, available resources are usually created in isolation, thus, they are scattered and need to be linked before they can be used for a specific task such as learning of a foreign language. To address these problems we present a new resource that link linguistic resources of multiple languages using the framework of Linguistic Linked Open Data (LLOD).

No abstract is provided for this article.

In real-world scenarios, dynamic signed networks are ubiquitous where edges have positive and negative sign semantics and evolve over time. Encoding the dynamics and sign semantics of the network simultaneously is challenging. Moreover, over-smoothing is inevitably introduced by the learning of network dynamics. Targeting this gap, we propose Dynamic Signed Network Embedding (DynamiSE), which effectively integrates the balance theory and ordinary differential equation (ODE) into node representation learning to construct a deeper dynamic signed graph neural network and capture the complex sign semantics formed by the two types of edges.

The outbreak and subsequent recurring waves of COVID −19 pose threats on the emergency management and people's daily life, while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic surveillance. Nonetheless, some challenges remain to be addressed in terms of multi-source heterogeneous data fusion, deep mining, and comprehensive applications. The Spatio-Temporal Artificial Intelligence (STAI) technology, which focuses on integrating spatial related time-series data, artificial intelligence models, and digital tools to provide intelligent computing platforms and applications, opens up new opportunities for scientific epidemic control. To this end, we leverage STAI and long-term experience in location-based intelligent services in the work. Specifically, we devise and develop a STAI-driven digital infrastructure, namely, WAYZ Disease Control Intelligent Platform (WDCIP), which consists of a systematic framework for building pipelines from automatic spatio-temporal data collection, processing to AI-based analysis and inference implementation for providing appropriate applications serving various epidemic scenarios. According to the platform implementation logic, our work can be performed and summarized from three aspects: (1) a STAI-driven integrated system; (2) a hybrid GNN-based approach for hierarchical risk assessment (as the core algorithm of WDCIP); and (3) comprehensive applications for social epidemic containment. This work makes a pivotal contribution to facilitating the aggregation and full utilization of spatio-temporal epidemic data from multiple sources, where the real-time human mobility data generated by high-precision mobile positioning plays a vital role in sensing the spread of the epidemic. So far, WDCIP has accumulated more than 200 million users who have been served in life convenience and decision-making during the pandemic.