Visual Language is a multitasking on-line system focusing on e-commerce, which involves in generating accurate product descriptions for sellers and providing convenient product retrieval service for customers. To achieve this goal, the system adopts image description technology and multi-modal retrieval technology. By utilizing cross-modal generation technique, we could help sellers on rapid uploading products and customers on rapid retrieval, which could improve the experience of both sellers and customers.

The Resource Description Framework and RDF Schema recommended by the World Wide Web Consortium provide a flexible model for semantically representing information about resources on the Web, which are playing an increasingly important role in intelligent processing of large-scale data. With the widespread acceptance and applications of RDF(S), construction of RDF(S) is of increasing importance. Automatic construction of RDF(S) with diverse data has attracted more attention. In this paper, we propose a novel approach for constructing an RDF(S) with object-oriented databases that are suitable for non-traditional applications. We propose the formal rules of mapping an object-oriented database model into a RDF(S) model based on the formal definitions of these two models. We develop a tool named OODB2RDF to verify our approach.

Knowledge graph (KG) completion aims to infer new facts from incomplete knowledge graphs. Most existing solutions focus on learning from time-aware fact triples and ignore the spatial information. In reality, knowledge graphs can evolve with time as well as the changing locations, such as the flight domain. Therefore, integrating spatiotemporal information into knowledge graph representation is important for the knowledge graph completion. To address this problem, this paper proposes two Spatio Temporal-aware knowledge graph completion models based on the Sequence Encoder, namely STSE and S-TSE, which incorporate the spatial and temporal information into relations. Specifically, the model consists of two steps: spatiotemporal-aware relation encoding and final scoring function evaluation. The first stage composes the spatiotemporal information into different tokens. Then two methods are proposed to obtain the embedding of spatiotemporal-aware relation by utilizing the Recursive Neural Network. The second stage proposes different scoring functions for two models. Empirically evaluation of the proposed models is conducted on spatiotemporal-aware KG completion task on two public datasets. Experimental results demonstrate the effectiveness of the proposal for spatiotemporal knowledge graph completion.

Nowadays, most popular methods for web data extraction (WDE) are top-down ones depending on structure. However, these techniques are not scalable enough when coming to complex pages. Consequently, we put forward a bottom-up approach for WDE based on entity recognition and integration to avoid over dependency to structure of web pages. The approach proposed focuses on primary text sequences labeling first and also gives consideration to repetitive patterns of them as well. We propose a Two-Level extraction model for entity recognition and repetitive pattern extraction algorithm for entity integration. Our approach can effectively reduce the attribute labeling mistakes. Also, we demonstrate our approach by scientifically experimental results. The conclusion is that our approach perform better than the traditional extraction techniques, especially on complex Web pages.

Because the amount of information contained on the Deep Web is much larger than the surface web, how to use it well has become a popular problem to research. When a query is sent to a deep web resource and the data sources return few results or even no result, a proper query relaxation solution should be adopted to get more satisfactory results to users. In this paper, such a query relaxation solution is presented. First, it solves the problem of relaxing attributes which contain multiple key words by value. That is, such attributes are not simply removed in the relaxation, but the query values of the attributes are modified. Second, when a data source returns many result pages, instead of getting all the pages, it evaluates the quality of the results in the current page to decide whether to send another query to fetch the next page. Thus, the number of query times is reduced. Finally, the experimental results demonstrate that both the result quality and the query efficiency are improved.