Abstract-An increasing number of databases have become web accessible through HTML form-based search interfaces. The data units returned from the underlying database are usually encoded into the result pages dynamically for human browsing. For the encoded data units to be machine processable, which is essential for many applications such as deep web data collection and Internet comparison shopping, they need to be extracted out and assigned meaningful labels. In this paper, we present an automatic annotation approach that first aligns the data units on a result page into different groups such that the data in the same group have the same semantic. Then, for each group we annotate it from different aspects and aggregate the different annotations to predict a final annotation label for it. An annotation wrapper for the search site is automatically constructed and can be used to annotate new result pages from the same web database. Our experiments indicate that the proposed approach is highly effective.
COVID‐19 has severely constricted global economic activities. This paper examines the joint effect of capital structure and corporate social responsibility (CSR) activities on firm risk during the COVID‐19 pandemic. We find that firms having excessive debt beyond the optimal level experienced high firm risk during the pandemic and the effect is more prevalent among firms with poor CSR performance. In contrast, firms with a debt level below the optimum are self‐protected regardless of their CSR practices. Our study provides businesses with insights on post‐pandemic directions of capital structure and CSR policies to build up sustainability and resilience in a volatile market.
Many databases have become Web-accessible through form-based search interfaces (i.e., search forms) that allow users to specify complex and precise queries to access the underlying databases. In general, such a Web search interface can be considered as containing an interface schema with multiple attributes and rich semantic/meta information; however, the schema is not formally defined on the search interface. Many Web applications, such as Web database integration and deep Web crawling, require the construction of the schemas. In this paper, we introduce a schema model for complex search interfaces, and present a tool (WISE-iExtractor) for automatically extracting and deriving all the needed information to construct the schemas. Our experimental results on real search interfaces indicate that this tool is highly effective.
An increasing number of databases have become Web accessible through HTML form-based search interfaces. The data units returned from the underlying database are usually encoded into the result pages dynamically for human browsing.
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