A service-oriented architecture for digital libraries

Petinot, Yves; Giles, C. Lee; Bhatnagar, Vivek; Teregowda, Pradeep; Han, Hao; Councill, Isaac G.

doi:10.1145/1035167.1035205

Cited by 16 publications

(10 citation statements)

References 5 publications

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“…20 Architectures of research-paper recommender systems were published by few authors. The developers of CiteSeer(x) published an architecture that focused on crawling and searching academic PDFs [1,108]. This architecture has some relevance for recommender systems, since many tasks in academic search are related to recommender systems (e.g., crawling and indexing PDFs, and matching user models or search-queries with research papers).…”

Section: Datasets and Architecturesmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of contentbased and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and Linnaeus University, Kalmar, Sweden sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspect...

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Section: Datasets and Architecturesmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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“…As an example of the integration of digital libraries into the semantic web, Petinot et al [23] presented a service oriented architecture of a scientific literature digital library. The architecture enables the integration of services provided by the digital library through an application programming interface.…”

Section: Digital Library Systemmentioning

confidence: 99%

A metadata generation system for scanned scientific volumes

Lü

Kahle

Wang

et al. 2008

Proceedings of the 8th ACM/IEEE-CS Joint Conference on Digital Libraries

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Large scale digitization projects have been conducted at digital libraries to preserve cultural artifacts and to provide permanent access.The increasing amount of digitized resources, including scanned books and scientific publications, requires development of tools and methods that will efficiently analyze and manage large collections of digitized resources. In this work, we tackle the problem of extracting metadata from scanned volumes of journals. Our goal is to extract information describing internal structures and content of scanned volumes, which is necessary for providing effective content access functionalities to digital library users.We propose methods for automatically generating volume level, issue level, and article level metadata based on format and text features extracted from OCRed text. We show the performance of our system on scanned bound historical documents nearly two centuries old. We have developed the system and integrated it into an operational digital library, the Internet Archive, for realworld usage.

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“…A service oriented structure for digital libraries [14] has been proposed to integrate complex information retrieval systems into the semantic web. The integration is enabled by providing Application Programming Interface(API) for services provided by digital library systems.…”

Section: Service Oriented Structure For Digital Librariesmentioning

confidence: 99%