-
Wang, H.; Liu, Q.; Penin, T.; Fu, L.; Zhang, L.; Tran, T.; Yu, Y.; Pan, Y.: Semplore: a scalable IR approach to search the Web of Data (2009)
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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.
- Content
- Vgl.: http://www.sciencedirect.com/science/article/pii/S1570826809000262.
- Source
- Web semantics: science, services and agents on the World Wide Web. 7(2009) no.3, S.177-188
-
Liu, X.; Guo, C.; Zhang, L.: Scholar metadata and knowledge generation with human and artificial intelligence (2014)
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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.
- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.6, S.1187-1201
-
Zhang, L.; Thijs, B.; Glänzel, W.: What does scientometrics share with other "metrics" sciences? (2013)
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- Date
- 25. 6.2013 20:29:05
- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1515-1518
-
Zhang, L.: Grasping the structure of journal articles : utilizing the functions of information units (2012)
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- Date
- 6. 4.2012 18:43:22
- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.3, S.469-480
-
Zhang, L.; Lu, W.; Yang, J.: LAGOS-AND : a large gold standard dataset for scholarly author name disambiguation (2023)
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- Date
- 22. 1.2023 18:40:36
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.2, S.168-185
-
Zhang, L.; Pan, Y.; Zhang, T.: Focused named entity recognition using machine learning (2004)
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- Date
- 15.10.2005 19:57:22
-
Lee, H.-L.; Zhang, L.: Tracing the conceptions and treatment of genre in Anglo-American cataloging (2013)
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- Date
- 29. 5.2015 19:23:26
-
Zhang, L.; Rousseau, R.; Glänzel, W.: Document-type country profiles (2011)
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- Abstract
- A bibliometric method for analyzing and visualizing national research profiles is adapted to describe national preferences for publishing particular document types. Similarities in national profiles and national peculiarities are discussed based on the publication output of the 26 most active countries indexed in the Web of Science annual volume 2007.
- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.7, S.1403-1411
-
Glänzel, W.; Rousseau, D.; Zhang, L.: ¬A visual representation of relative first-citation times (2012)
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- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1420-1425
-
Zhang, L.: Linking information through function (2014)
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- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.11, S.2293-2305
-
Zhang, L.; Liu, Q.L.; Zhang, J.; Wang, H.F.; Pan, Y.; Yu, Y.: Semplore: an IR approach to scalable hybrid query of Semantic Web data (2007)
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- Series
- Lecture notes in computer science; 4825
-
Zhang, L.; Rousseau, R.; Glänzel, W.: Diversity of references as an indicator of the interdisciplinarity of journals : taking similarity between subject fields into account (2016)
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- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.5, S.1257-1265
-
Kulczycki, E.; Huang, Y.; Zuccala, A.A.; Engels, T.C.E.; Ferrara, A.; Guns, R.; Pölönen, J.; Sivertsen, G.; Taskin, Z.; Zhang, L.: Uses of the Journal Impact Factor in national journal rankings in China and Europe (2022)
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- Source
- Journal of the Association for Information Science and Technology. 73(2022) no.12, S.1741-1754
-
Zhang, L.: ¬The knowledge organization education within and beyond the master of library and information science (2023)
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-
Zhang, L.; Gou, Z.; Fang, Z.; Sivertsen, G.; Huang, Y.: Who tweets scientific publications? : a large-scale study of tweeting audiences in all areas of research (2023)
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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.13, S.1485-1497
