Search (11 results, page 1 of 1)

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Abstract

The Web of Data keeps growing rapidly. However, the full exploitation of this large amount of structured data faces numerous challenges like usability, scalability, imprecise information needs and data change. We present Semplore, an IR-based system that aims at addressing these issues. Semplore supports intuitive faceted search and complex queries both on text and structured data. It combines imprecise keyword search and precise structured query in a unified ranking scheme. Scalable query processing is supported by leveraging inverted indexes traditionally used in IR systems. This is combined with a novel block-based index structure to support efficient index update when data changes. The experimental results show that Semplore is an efficient and effective system for searching the Web of Data and can be used as a basic infrastructure for Web-scale Semantic Web search engines.

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Abstract

This study examines the conceptions and treatment of genre in four sets of modern Anglo-American cataloging rules spanning 171 years. Genre-related rules are first identified through "genre(s)," "form(s)," and "type(s)" keyword searches, and manual examination of the contents, then analyzed by level of treatment genre receives and by user tasks, as defined in the Functional Requirements for Bibliographic Records. While genre is found to be sporadically addressed across the rules, its significance has increased over time. In conclusion, the authors call for a rigorous and functional definition of genre and an integrated approach to genre in cataloging.

Source

Cataloging and classification quarterly. 51(2013) no.8, S.891-912

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Abstract

Scholar metadata have traditionally centered on descriptive representations, which have been used as a foundation for scholarly publication repositories and academic information retrieval systems. In this article, we propose innovative and economic methods of generating knowledge-based structural metadata (structural keywords) using a combination of natural language processing-based machine-learning techniques and human intelligence. By allowing low-barrier participation through a social media system, scholars (both as authors and users) can participate in the metadata editing and enhancing process and benefit from more accurate and effective information retrieval. Our experimental web system ScholarWiki uses machine learning techniques, which automatically produce increasingly refined metadata by learning from the structural metadata contributed by scholars. The cumulated structural metadata add intelligence and automatically enhance and update recursively the quality of metadata, wiki pages, and the machine-learning model.

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Abstract

As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we briefy describe how Semplore is used for searching Wikipedia and an IBM customer's product information.

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Abstract

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.

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Abstract

By analyzing 63 English-speaking institutions that offer ALA-accredited master's programs in library and information studies, this research aims to explore the education for knowl­edge organization (KO) at different levels and across fields. This research examines the KO courses that are the required courses and elective courses in the MLIS programs, that are offered in other master's programs and graduate certificate programs, that are adapted to the undergraduate degree and certificate programs, and that are particularly developed for programs other than MLIS. The findings indicate that the great majority of MLIS programs still have a focus on or a significant component of knowl­edge organization as their required course and include the knowl­edge organization elective courses, particularly library cataloging and classification, on their curriculum. However, there is a variety of the offerings of KO related courses across the programs in an institution or in the same program across the institutions. It shows a promising trend that the traditional and new KO courses play an important role in many other programs, at different levels and across fields. With the conventional, adapted, or innovative content, these courses demonstrate that the principles and skills of knowl­edge organization are applicable to a wide variety of settings, can be integrated with other disciplinary knowl­edge and emerging technologies, and meet the needs of different career pathways and groups of learners.

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Date

15.10.2005 19:57:22

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Abstract

How information resources can be meaningfully related has been addressed in contexts from bibliographic entries to hyperlinks and, more recently, linked data. The genre structure and relationships among genre structure constituents shed new light on organizing information by purpose or function. This study examines the relationships among a set of functional units previously constructed in a taxonomy, each of which is a chunk of information embedded in a document and is distinct in terms of its communicative function. Through a card-sort study, relationships among functional units were identified with regard to their occurrence and function. The findings suggest that a group of functional units can be identified, collocated, and navigated by particular relationships. Understanding how functional units are related to each other is significant in linking information pieces in documents to support finding, aggregating, and navigating information in a distributed information environment.

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Abstract

The purpose of this study is to investigate the validity of tweets about scientific publications as an indicator of societal impact by measuring the degree to which the publications are tweeted beyond academia. We introduce methods that allow for using a much larger and broader data set than in previous validation studies. It covers all areas of research and includes almost 40 million tweets by 2.5 million unique tweeters mentioning almost 4 million scientific publications. We find that, although half of the tweeters are external to academia, most of the tweets are from within academia, and most of the external tweets are responses to original tweets within academia. Only half of the tweeted publications are tweeted outside of academia. We conclude that, in general, the tweeting of scientific publications is not a valid indicator of the societal impact of research. However, publications that continue being tweeted after a few days represent recent scientific achievements that catch attention in society. These publications occur more often in the health sciences and in the social sciences and humanities.

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Date

6. 4.2012 18:43:22

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Date

22. 1.2023 18:40:36