Search

This special issue features the selected works of authors who have presented papers at the 2022 iteration of the Joint Conference on Digital Libraries (JCDL) in Cologne, Germany. The motto of the conference was "Bridging Worlds" and was run as a fully hybrid event. Ten papers covering all aspects of Digital Libraries, namely Natural Language Processing, Information Retrieval, User Behavior, Scholarly Communication, Classification, Information Extraction are included in this issue. [ABSTRACT FROM AUTHOR]

In this paper, we present a system for summarization of scientific and structured documents that has three components: section mixture models are used for estimation of the weights of terms; a hypothesis test to select a subset of these terms; and a sentence extractor based on techniques for combinatorial optimization. The section mixture models approach is an adaptation of a bigram mixture model based on the main sections of a scientific document and a collection of citing sentences (citances) from papers that reference the document. The model was adapted from earlier work done on Biomedical documents used in the summarization task of the 2014 Text Analysis Conference (TAC 2014). The mixture model trained on the Biomedical data was used also on the data for the Computational Linguistics scientific summarization task of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (CL-SciSumm 2016). This model gives rise to machine-generated summaries with ROUGE scores that are nearly as strong as those seen on the Biomedical data and was also the highest scoring submission to the task of generating a human summary. For sentence extraction, we use the OCCAMS algorithm (Davis et al., in: Vreeken, Ling, Zaki, Siebes, Yu, Goethals, Webb, Wu (eds) ICDM workshops, IEEE Computer Society, pp 454-463, 2012) which takes the sentences from the original document and the assignment of weights of the terms computed by the language models and outputs a set of minimally overlapping sentences whose combined term coverage is maximized. Finally, we explore the importance of an appropriate background model for the hypothesis test to select terms to achieve the best quality summaries. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive survey on knowledge retrieval of historical concepts using semantic web technologies. The scope of this discussion encompasses research material in the field of computing and humanities including but not limited to Cultural Heritage, Museum and Digital Library research. Semantic web techniques used to describe, store, search, query and retrieve historic concepts such as RDF, RDFs, OWL, and Ontologies are discussed along with other extensively researched methods from domains like Natural Language Processing and Computer Vision. The discussion elaborates on Ontological-enabled information retrieval and the concept of new knowledge discovery. This work presents a comprehensive set of problems identified in the field of information retrieval and their solutions through the semantic web along with an insight in the future directions and the open-ended questions in the domain of retrieval of historic concept. [ABSTRACT FROM AUTHOR]

Research on how clinicians search shows that they pose queries according to three common clinical tasks: searching for diagnoses, searching for treatments and searching for tests. We hypothesise, therefore, that structuring an information retrieval system around these three tasks would be beneficial when searching for evidence-based medicine (EBM) resources in medical digital libraries. Task-oriented (diagnosis, test and treatment) information was extracted from free-text medical articles using a natural language processing pipeline. This information was integrated into a retrieval and visualisation system for EBM search that allowed searchers to interact with the system via task-oriented filters. The effectiveness of the system was empirically evaluated using TREC CDS—a gold standard of medical articles and queries designed for EBM search. Task-oriented information was successfully extracted from 733,138 articles taken from a medical digital library. Task-oriented search led to improvements in the quality of search results and savings in searcher workload. An analysis of how different tasks affected retrieval showed that searching for treatments was the most challenging and that the task-oriented approach improved search for treatments. The most savings in terms of workload were observed when searching for treatments and tests. Overall, taking into account different clinical tasks can improve search according to these tasks. Each task displayed different results, making systems that are more adaptive to the clinical task type desirable. A future user study would help quantify the actual cost-saving estimates. [ABSTRACT FROM AUTHOR]