摘要翻译：
Deep Web数据库包含了90%以上的Web相关信息。尽管它们很重要，用户并没有从这个金库中获利。许多deep web服务在价格、服务质量和设施方面提供有竞争力的服务。随着服务数量的迅速增长，用户很难同时请求多个web服务。在本文中，我们设想了这样一个系统：用户可以使用一个查询接口构造一个查询，然后系统将查询转换到其他查询接口。然而，界面是由设计者创建的，目的是让用户直观地解释，机器无法从给定的界面解释查询。提出了一种模拟用户解释能力并从deep web查询接口中提取查询的新方法。我们的方法已经在两个标准数据集上证明了良好的性能。
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英文标题：
《VIQI: A New Approach for Visual Interpretation of Deep Web Query
  Interfaces》
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作者：
Radhouane Boughamoura, Lobna Hlaoua and Mohamed Nazih Omri
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Information Retrieval        信息检索
分类描述：Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引，字典，检索，内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Deep Web databases contain more than 90% of pertinent information of the Web. Despite their importance, users don't profit of this treasury. Many deep web services are offering competitive services in term of prices, quality of service, and facilities. As the number of services is growing rapidly, users have difficulty to ask many web services in the same time. In this paper, we imagine a system where users have the possibility to formulate one query using one query interface and then the system translates query to the rest of query interfaces. However, interfaces are created by designers in order to be interpreted visually by users, machines can not interpret query from a given interface. We propose a new approach which emulates capacity of interpretation of users and extracts query from deep web query interfaces. Our approach has proved good performances on two standard datasets.
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PDF链接：
https://arxiv.org/pdf/1205.0917