Searching business information in digital information systems of Russian libraries

The author continues with her study initially presented in the article “The possibilities of information search in electronic platforms of Russian libraries” (A. V. Kulikova. The possibilities of information search in electronic platforms of russian libraries // The Journal of Encyclopaedic Studies. – 2019. – No 2. – P. 30–52). She demonstrates the methods to be applied for business information search related to local encyclopaedic book publications and identifies the principles to find recent publications promptly and to satisfy user demands most effectively. The bibliographic search success depends upon how the user understands the system. Optimum query formulation saves time and excludes information noise. The key characteristics of library digital information retrieval systems are discussed. The computer systems of 113 regional libraries were analyzed within the study. The following automated library information were tested objectively: IRBIS, RUSLAN, OPAC-Global, Foliant, MacWeb. The author does not intend to advertise or subvertise any ALIS. Her main goal is to reveal the convenient and speedy retrieval methods with existing functionalities.

Semi-Automated Identification of Biomedical Literature: A Proof of Concept Study

Background: The typical approach to literature identification involves two discrete and successive steps: (i) formulating a search strategy (i.e., a set of Boolean queries) and (ii) manually identifying the relevant citations in the corpus returned by the query. We have developed a literature identification system (Pythia) that combines the query formulation and citation screening steps and uses modern approaches for text encoding (dense text embeddings) to represent the text of the citations in a form that can be used by information retrieval and machine learning algorithms.Methods: Pythia incorporates a set of natural-language questions with machine-learning algorithms to rank all PubMed citations based on relevance. Pythia returns the 100 top-ranked citations for all questions combined. These 100 articles are exported, and a human screener adjudicates the relevance of each abstract and tags words that indicate relevance. The “curated” articles are then exploited by Pythia to refine the search and re-rank the abstracts, and a new set of 100 abstracts is exported and screened/tagged, until convergence (i.e., no other relevant abstracts are retrieved) or for a set number of iterations (batches). Pythia performance was assessed using seven systematic reviews (three prospectively and four retrospectively). Sensitivity, precision, and the number needed to read were calculated for each review. Results: The ability of Pythia to identify the relevant articles (sensitivity) varied across reviews from a low of 0.09 for a sleep apnea review to a high of 0.58 for a diverticulitis review. The number of abstracts that a reviewer had to read to find one relevant abstract (NNR) was lower than in the manually screened project in four reviews, higher in two, and had mixed results in one. The reviews that had greater overall sensitivity retrieved more relevant citations in early batches, but neither study design, study size, nor specific key question significantly affected retrieval across all reviews.Conclusions: Future research should explore ways to encode domain knowledge in query formulation, possibly by incorporating a "reasoning" aspect to Pythia to elicit more contextual information and leveraging ontologies and knowledge bases to better enrich the questions used in the search.

Interactive Search vs. Automatic Search

This article conducts user evaluation to study the performance difference between interactive and automatic search. Particularly, the study aims to provide empirical insights of how the performance landscape of video search changes, with tens of thousands of concept detectors freely available to exploit for query formulation. We compare three types of search modes: free-to-play (i.e., search from scratch), non-free-to-play (i.e., search by inspecting results provided by automatic search), and automatic search including concept-free and concept-based retrieval paradigms. The study involves a total of 40 participants; each performs interactive search over 15 queries of various difficulty levels using two search modes on the IACC.3 dataset provided by TRECVid organizers. The study suggests that the performance of automatic search is still far behind interactive search. Furthermore, providing users with the result of automatic search for exploration does not show obvious advantage over asking users to search from scratch. The study also analyzes user behavior to reveal insights of how users compose queries, browse results, and discover new query terms for search, which can serve as guideline for future research of both interactive and automatic search.