On the Use of Web Search to Improve Scientific Collections (2020)

Abstract: 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 such scientific portals. Within our framework, publicly-available research paper titles and author names are used as queries to a Web search engine. We were able to obtain ~267,000 unique research papers through our fully-automated framework using ~76,000 queries, resulting in almost 200,000 more papers than the number of queries. Moreover, through a combination of title and author name search, we were able to recover 78% of the original searched titles.

## introduction scientific portals such as google scholar, semantic scholar, acl anthology, citeseer x , and arnet-miner, provide access to scholarly publications and comprise indispensable resources for researchers who search for literature on specific subject topics. moreover, many applications such as document and citation recommendation @xcite , expert search @xcite , topic classification @xcite , and keyphrase extraction and generation @xcite involve web-scale analysis of up-todate research collections. open-access, autonomous systems such as citeseer x and arnetminer acquire and index freelyavailable research articles from the web @xcite . researchers' homepages and paper repository urls are crawled for maintaining the research collections in these portals, using focused crawling. needless to say, the crawl seed lists cannot be comprehensive in the face of the ever changing scholarly web. not only do new authors and publication venues emerge, but also existing researchers may stop publishing or they may change affiliations, resulting in outdated seed urls. given this challenge, how can we automatically augment the document collections in open-access scientific portals? to address this question, in this paper, we propose a novel framework (based on web search) for both automatically acquiring and processing research documents. to motivate our framework, we recall how a web user typically searches for research papers or authors. as with regular document search, a user typically issues web search queries comprising of representative keywords or paper titles for finding publications on a topic. similarly, if the author is known, a "navigational query" @xcite may be employed to locate the homepage where the paper is likely to be hosted. to illustrate this process, figure 1 shows an anecdotal example of a search using google for the title and authors of a research article. as can be seen from the figure, the intended research paper and the researchers' homepages (highlighted in sets 2 and 3) are accurately retrieved. moreover, among the top-5 results shown for the title query (set 1), four of the five results are research papers on the same topic (i.e., the first four results). the document at the springer link is not available for free, whereas the last document corresponds to course slides. the additional three papers are potentially retrieved because scientific paper titles comprise a large fraction of keywords @xcite , and hence, the words in these titles serve as excellent keywords that can retrieve not only the intended paper, but also other relevant documents. our framework mimics precisely the above search and scrutinize the approach adopted by scholarly web users. freely-available information from the web for specific subject disciplines 1 is used to frame title and author name queries in our framework. our contributions are as follows: the accuracy and efficiency of our search/crawl framework is contingent on the accuracy of two components: (1) the homepage identifier, and (2) the paper/non-paper classifier. ## datasets the datasets used in the evaluation of our framework and its components are summarized in ## experiments and results in this section, we describe our experiments on homepage identification and paper classification along with their performance within the search then crawl then process paper acquisition framework. performance measures. we use the standard measures precision, recall, and f1 for summarizing the results of author homepage identification and paper classification @xcite . unlike classification where we consider the true and predicted labels for each instance (webpage), in ranksvm the prediction is per query @xcite . that is, the results with respect to a query are assigned ranks based on scores from the ranksvm and the result at rank-1 is chosen as the predicted homepage. ## related work web crawling is a well-studied problem in information retrieval, focusing on issues of scalability, effectiveness, efficiency, and freshness @xcite . despite its simplicity, research has shown that breadth-first search crawling produces high-quality collections in early stages of the crawl @xcite . focused crawling was introduced by @xcite to deal with the information overload on the web in order to build specialized collections focused on specific topics. since the introduction of focused crawling, many variations have been proposed @xcite . in contrast to focused crawling, our framework is able to acquire research documents that are not limited to a specific taxonomy. prior research has also focused on enhancing digital libraries content to better satisfy the needs of digital library users @xcite @xcite . several works studied the coverage in scientific portals such as microsoft academic, google scholar, scopus and the web of science (hug and brändle, 2017; harzing and alakangas, 2017). multiple works focus on better ranking of retrieved documents for a given query @xcite @xcite . homepage finding and document classification are well-studied in information retrieval. the homepage finding track in trec 2001 resulted in various machine learning systems for finding homepages @xcite @xcite . @xcite and @xcite treated homepage finding as a binary classification task and used various url and webpage content features for classification. in the context of scientific digital libraries, document classification into classes related to subject-topics (for example, "machine learning," "databases") was studied previously @xcite . in contrast with existing work, we investigate features from web search engine results and formulate researcher homepage identification as a learning to rank task. in addition, we are the first to interleave various components of web search, crawl, and document processing to build an efficient paper acquisition framework. ## conclusion and future directions we proposed a framework for automatically acquiring research papers from the web. we showed the experiments illustrating the state-of-the-art performance for two major modules of our framework: a homepage identifier and a paper classifier. through an experiment using a large collection of ≈ 76, 000 queries (titles + authors names), our framework was able to automatically acquire an overall collection of ≈ 267, 000 unique research papers and was able to recover 78% of the original searched titles, i.e., ≈ 34, 000 papers from the 43, 496 original searched titles. we also showed that our approach is not meant to replace existing crawling strategies, but can be used in conjunction, to enhance the content of digital libraries. in the future, it would be interesting to apply our framework to other domains, and study the integration of topic classification.