As the complexity of the information available on the web increases, the role of user-data interaction paradigms is becoming increasingly critical for the success of web information retrieval. Recent years have witnessed significant advances in techniques for indexing and querying web data. However, in the same period, limited advancements have been made in developing paradigms and researching algorithmic issues associated with the design of interfaces for web-search. In this paper, we propose a novel paradigm for enabling multiple-perspective query and interaction in web search. Underlying the proposed metaphor are information and pattern analysis techniques that help determine semantic correlations between web pages, identify and extract information critical for intuitive understanding and hypothesis generation, and support effective and multiple-perspective interactions between users and the data. We provide a comprehensive study on the effectiveness and efficiency of the proposed approach in query-retrieval scenarios involving complex information goals. Our investigations point to the importance of developing novel ways to mediate interactions during web-search and will be useful in the development of the next generation of real-world solutions for web information retrieval.