A Search/Crawl Framework for Automatically Acquiring Scientific Documents
This provides an automated alternative to crawl-based methods for scientific portals, though it is incremental as it builds on existing search technologies.
The paper tackles the problem of acquiring scientific documents for digital libraries by proposing a search-driven framework that uses paper titles and author names as queries to a Web search engine, achieving approximately 0.665 million documents from about 0.076 million queries.
Despite the advancements in search engine features, ranking methods, technologies, and the availability of programmable APIs, current-day open-access digital libraries still rely on crawl-based approaches for acquiring their underlying document collections. In this paper, we propose a novel search-driven framework for acquiring documents for scientific portals. Within our framework, publicly-available research paper titles and author names are used as queries to a Web search engine. Next, research papers and sources of research papers are identified from the search results using accurate classification modules. Our experiments highlight not only the performance of our individual classifiers but also the effectiveness of our overall Search/Crawl framework. Indeed, we were able to obtain approximately 0.665 million research documents through our fully-automated framework using about 0.076 million queries. These prolific results position Web search as an effective alternative to crawl methods for acquiring both the actual documents and seed URLs for future crawls.