Search (12 results, page 1 of 1)

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Abstract

Users of research databases, such as CiteSeerX, Google Scholar, and Microsoft Academic, often search for papers using a set of keywords. Unfortunately, many authors avoid listing sufficient keywords for their papers. As such, these applications may need to automatically associate good descriptive keywords with papers. When the full text of the paper is available this problem has been thoroughly studied. In many cases, however, due to copyright limitations, research databases do not have access to the full text. On the other hand, such databases typically maintain metadata, such as the title and abstract and the citation network of each paper. In this paper we study the problem of predicting which keywords are appropriate for a research paper, using different methods based on the citation network and available metadata. Our main goal is in providing search engines with the ability to extract keywords from the available metadata. However, our system can also be used for other applications, such as for recommending keywords for the authors of new papers. We create a data set of research papers, and their citation network, keywords, and other metadata, containing over 470K papers with and more than 2 million keywords. We compare our methods with predicting keywords using the title and abstract, in offline experiments and in a user study, concluding that the citation network provides much better predictions.

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Abstract

Tag recommendation strategies that exploit term co-occurrence patterns with tags previously assigned to the target object have consistently produced state-of-the-art results. However, such techniques work only for objects with previously assigned tags. Here we focus on tag recommendation for objects with no tags, a variation of the well-known \textit{cold start} problem. We start by evaluating state-of-the-art co-occurrence based methods in cold start. Our results show that the effectiveness of these methods suffers in this situation. Moreover, we show that employing various automatic filtering strategies to generate an initial tag set that enables the use of co-occurrence patterns produces only marginal improvements. We then propose a new approach that exploits both positive and negative user feedback to iteratively select input tags along with a genetic programming strategy to learn the recommendation function. Our experimental results indicate that extending the methods to include user relevance feedback leads to gains in precision of up to 58% over the best baseline in cold start scenarios and gains of up to 43% over the best baseline in objects that contain some initial tags (i.e., no cold start). We also show that our best relevance-feedback-driven strategy performs well even in scenarios that lack user cooperation (i.e., users may refuse to provide feedback) and user reliability (i.e., users may provide the wrong feedback).

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Abstract

Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase-level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two-thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self-formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2-second response times at the highest loads tested).

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Source

B.I.T.online. 22(2019) H.2, S.163-166

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Date

1. 2.2016 18:25:22

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Abstract

Große Aufmerksamkeit richtet sich im Moment auf das Potential von automatisierten Methoden in der Sacherschließung und deren Interaktionsmöglichkeiten mit intellektuellen Methoden. In diesem Kontext befasst sich der vorliegende Beitrag mit den folgenden Fragen: Was sind die Anforderungen an bibliothekarische Metadaten aus Sicht der Wissenschaft? Was wird gebraucht, um den Informationsbedarf der Fachcommunities zu bedienen? Und was bedeutet das entsprechend für die Automatisierung der Metadatenerstellung und -pflege? Dieser Beitrag fasst die von der Autorin eingenommene Position in einem Impulsvortrag und der Podiumsdiskussion beim Workshop der FAG "Erschließung und Informationsvermittlung" des GBV zusammen. Der Workshop fand im Rahmen der 22. Verbundkonferenz des GBV statt.

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Abstract

Spätestens seit dem Deutschen Bibliothekartag 2018 hat sich die Diskussion zu den automatischen Verfahren der Inhaltserschließung der Deutschen Nationalbibliothek von einer politisch geführten Diskussion in eine Qualitätsdiskussion verwandelt. Der folgende Beitrag beschäftigt sich mit Fragen der Qualität von Inhaltserschließung in digitalen Zeiten, wo heterogene Erzeugnisse unterschiedlicher Verfahren aufeinandertreffen und versucht, wichtige Anforderungen an Qualität zu definieren. Dieser Tagungsbeitrag fasst die vom Autor als Impulse vorgetragenen Ideen beim Workshop der FAG "Erschließung und Informationsvermittlung" des GBV am 29. August 2018 in Kiel zusammen. Der Workshop fand im Rahmen der 22. Verbundkonferenz des GBV statt.

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Source

B.I.T.online. 22(2019) H.6, S.465-469

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Date

19. 8.2017 9:24:22

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Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Date

20. 1.2015 18:30:22