-
Ding, Y.: Applying weighted PageRank to author citation networks (2011)
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- Date
- 22. 1.2011 13:02:21
- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.236-245
-
Ding, Y.; Zhang, G.; Chambers, T.; Song, M.; Wang, X.; Zhai, C.: Content-based citation analysis : the next generation of citation analysis (2014)
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- Date
- 22. 8.2014 16:52:04
- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.9, S.1820-1833
-
Yan, E.; Ding, Y.: Weighted citation : an indicator of an article's prestige (2010)
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- Abstract
- The authors propose using the technique of weighted citation to measure an article's prestige. The technique allocates a different weight to each reference by taking into account the impact of citing journals and citation time intervals. Weightedcitation captures prestige, whereas citation counts capture popularity. They compare the value variances for popularity and prestige for articles published in the Journal of the American Society for Information Science and Technology from 1998 to 2007, and find that the majority have comparable status.
- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1635-1643
-
Ding, Y.; Jacob, E.K.; Fried, M.; Toma, I.; Yan, E.; Foo, S.; Milojevicacute, S.: Upper tag ontology for integrating social tagging data (2010)
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- Abstract
- Data integration and mediation have become central concerns of information technology over the past few decades. With the advent of the Web and the rapid increases in the amount of data and the number of Web documents and users, researchers have focused on enhancing the interoperability of data through the development of metadata schemes. Other researchers have looked to the wealth of metadata generated by bookmarking sites on the Social Web. While several existing ontologies have capitalized on the semantics of metadata created by tagging activities, the Upper Tag Ontology (UTO) emphasizes the structure of tagging activities to facilitate modeling of tagging data and the integration of data from different bookmarking sites as well as the alignment of tagging ontologies. UTO is described and its utility in modeling, harvesting, integrating, searching, and analyzing data is demonstrated with metadata harvested from three major social tagging systems (Delicious, Flickr, and YouTube).
- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.3, S.505-521
-
Ding, Y.; Chowdhury, G.C.; Foo, S.: Bibliometric cartography of information retrieval research by using co-word analysis (2001)
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- Source
- Information processing and management. 37(2001) no.6, S.817-842
-
Yan, E.; Ding, Y.: Applying centrality measures to impact analysis : a coauthorship network analysis (2009)
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- Source
- Journal of the American Society for Information Science and Technology. 60(2009) no.10, S.2107-2118
-
Klein, M.; Ding, Y.; Fensel, D.; Omelayenko, B.: Ontology management : storing, aligning and maintaining ontologies (2004)
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- Abstract
- Ontologies need to be stored, sometimes aligned and their evolution needs to be managed. All these tasks together are called ontology management. Alignment is a central task in ontology re-use. Re-use of existing ontologies often requires considerable effort: the ontologies either need to be integrated, which means that they are merged into one new ontology, or the ontologies can be kept separate. In both cases, the ontologies have to be aligned, which means that they have to be brought into mutual agreement. The problems that underlie the difficulties in integrating and aligning are the mismatches that may exist between separate ontologies. Ontologies can differ at the language level, which can mean that they are represented in a different syntax, or that the expressiveness of the ontology language is dissimilar. Ontologies also can have mismatches at the model level, for example, in the paradigm, or modelling style. Ontology alignment is very relevant in a Semantic Web context. The Semantic Web will provide us with a lot of freely accessible domain specific ontologies. To form a real web of semantics - which will allow computers to combine and infer implicit knowledge - those separate ontologies should be aligned and linked.
Support for evolving ontologies is required in almost all situations where ontologies are used in real-world applications. In those cases, ontologies are often developed by several persons and will continue to evolve over time, because of changes in the real world, adaptations to different tasks, or alignments to other ontologies. To prevent that such changes will invalidate existing usage, a change management methodology is needed. This involves advanced versioning methods for the development and the maintenance of ontologies, but also configuration management, that takes care of the identification, relations and interpretation of ontology versions. All these aspects come together in integrated ontology library systems. When the number of different ontologies is increasing, the task of storing, maintaining and re-organizing them to secure the successful re-use of ontologies is challenging. Ontology library systems can help in the grouping and reorganizing ontologies for further re-use, integration, maintenance, mapping and versioning. Basically, a library system offers various functions for managing, adapting and standardizing groups of ontologies. Such integrated systems are a requirement for the Semantic Web to grow further and scale up. In this chapter, we describe a number of results with respect to the above mentioned areas. We start with a description of the alignment task and show a meta-ontology that is developed to specify the mappings. Then, we discuss the problems that are caused by evolving ontologies and describe two important elements of a change management methodology. Finally, in Section 4.4 we survey existing library systems and formulate a wish-list of features of an ontology library system.
- Source
- Towards the semantic Web: ontology-driven knowledge management. Eds.: J. Davies, u.a
-
Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009)
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- Source
- Journal of the American Society for Information Science and Technology. 60(2009) no.11, S.2229-2243
-
Ding, Y.; Jacob, E.K.; Zhang, Z.; Foo, S.; Yan, E.; George, N.L.; Guo, L.: Perspectives on social tagging (2009)
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- Source
- Journal of the American Society for Information Science and Technology. 60(2009) no.12, S.2388-2401
-
Ding, Y.: Topic-based PageRank on author cocitation networks (2011)
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- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.3, S.449-466
-
Yan, E.; Ding, Y.; Sugimoto, C.R.: P-Rank: an indicator measuring prestige in heterogeneous scholarly networks (2011)
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- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.3, S.467-477
-
Ding, Y.; Yan, E.: Scholarly network similarities : how bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other (2012)
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- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1313-1326
-
Ni, C.; Shaw, D.; Lind, S.M.; Ding, Y.: Journal impact and proximity : an assessment using bibliographic features (2013)
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- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.802-817
-
Zhang, G.; Ding, Y.; Milojevic, S.: Citation content analysis (CCA) : a framework for syntactic and semantic analysis of citation content (2013)
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- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1490-1503
-
Song, M.; Kim, S.Y.; Zhang, G.; Ding, Y.; Chambers, T.: Productivity and influence in bioinformatics : a bibliometric analysis using PubMed central (2014)
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- Journal of the Association for Information Science and Technology. 65(2014) no.2, S.352-371
-
Hu, B.; Dong, X.; Zhang, C.; Bowman, T.D.; Ding, Y.; Milojevic, S.; Ni, C.; Yan, E.; Larivière, V.: ¬A lead-lag analysis of the topic evolution patterns for preprints and publications (2015)
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- Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2643-2656
-
Zhang, C.; Bu, Y.; Ding, Y.; Xu, J.: Understanding scientific collaboration : homophily, transitivity, and preferential attachment (2018)
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- Journal of the Association for Information Science and Technology. 69(2018) no.1, S.72-86
-
Zhai, Y; Ding, Y.; Wang, F.: Measuring the diffusion of an innovation : a citation analysis (2018)
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- Journal of the Association for Information Science and Technology. 69(2018) no.3, S.368-379
-
Lu, C.; Bu, Y.; Wang, J.; Ding, Y.; Torvik, V.; Schnaars, M.; Zhang, C.: Examining scientific writing styles from the perspective of linguistic complexity : a cross-level moderation model (2019)
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- Journal of the Association for Information Science and Technology. 70(2019) no.5, S.462-475
-
Huang, Y.; Bu, Y.; Ding, Y.; Lu, W.: From zero to one : a perspective on citing (2019)
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- Source
- Journal of the Association for Information Science and Technology. 70(2019) no.10, S.1098-1107
