The shortage of labeled data has been a long-standing challenge for relation extraction (RE) tasks. Semi-supervised RE (SSRE) is a promising way through annotating unlabeled samples with pseudolabels as additional training data. However, some pseudolabels on unlabeled data might be erroneous and will bring misleading knowledge into SSRE models. For this reason, we propose a novel adversarial multi-teacher distillation (AMTD) framework, which includes multi-teacher knowledge distillation and adversarial training (AT), to capture the knowledge on unlabeled data in a refined way. Specifically, we first develop a general knowledge distillation (KD) technique to learn not only from pseudolabels but also from the class distribution of predictions by different models in existing SSRE methods. To improve the robustness of the model, we further empower the distillation process with a language model-based AT technique. Extensive experimental results on two public datasets demonstrate that our framework significantly promotes the performance of the base SSRE methods.

This article presents an unsupervised multi-view hierarchical embedding (UMHE) framework to sufficiently reveal the intrinsic topical knowledge in social events. Event-oriented topics are highly related to such events as it can provide explicit descriptions of what have happened in social community. In many real-world cases, however, it is difficult to include all attributes of microblogs, more often, textual aspects only are available. Traditional topic modelling methods have failed to generate event-oriented topics with the textual aspects, since the inherent relations between topics are often overlooked in these methods. Meanwhile, the metrics in original word vocabulary space might not effectively capture semantic distances. Our UMHE framework overcomes the severe information deficiency and poor feature representation. The UMHE first develops a multi-view Bayesian rose tree to preliminarily generate prior knowledge for latent topics and their relations. With such prior knowledge, we design an unsupervised translation-based hierarchical embedding method to make a better representation of these latent topics. By applying self-adaptive spectral clustering on the embedding space and the original space concomitantly, we eventually extract event-oriented topics in word distributions to express social events. Our framework is purely data-driven and unsupervised, without any external knowledge. Experimental results on TREC Tweets2011 dataset and Sina Weibo dataset demonstrate that the UMHE framework can construct hierarchical structure with high fitness, but also yield topic embeddings with salient semantics; therefore, it can derive event-oriented topics with meaningful descriptions.

To date, data generates and arrives in the form of stream to propagate discussions of public events in microblog services. Discovering event-oriented topics from the stream will lead to a better understanding of the change of public concern. However, as the massive scale of the data stream, traditional static topic models, such as LDA, are no longer fit for topic detection and tracking tasks. In this paper, we propose a central topic model (CenTM), where a Multi-view Clustering algorithm with Two-phase Random Walk (MC-TRW) is devised to aggregate the LDA's latent topics into central topics. Furthermore, we leverage the aggregation of central topics alternately with MC-TRW and sequential topic inference to improve the scalability in the stream fashion, so as to derive the dynamic central topic model (DCenTM). Specifically, our model is able to uncover the intrinsic characteristics of the central topics and predict the trend of their intensity along a life cycle. Experimental results demonstrate that the proposed central topic model is event-oriented and of high generalization, it therefore can dispose the topic trend prediction effectively and precisely in massive data stream.

Providing integrated access to multiple, distributed, heterogeneous databases and other information sources has become one of the leading issues in database research and the industry. One of the most effective approaches is to extract and integrate information of interest from each source in advance and store them in a centralized repository (known as a data warehouse). When a query is posed, it is evaluated directly at the warehouse without accessing the original information sources. One of the techniques that this approach uses to improve the efficiency of query processing is materialized view(s). Essentially, materialized views are used for data warehouses, and various methods for relational databases have been developed. In this paper, we first discuss an object deputy approach to realize materialized object views for data warehouses which can also incorporate object-oriented databases. A framework has been developed using Smalltalk to prepare data for data warehousing, in which an object deputy model and database connecting tools have been implemented. The object deputy model can provide an easy-to-use way to resolve inconsistency and conflicts while preparing data for data warehousing, as evidenced by our empirical study.