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Books on the topic 'Heterogeneous Textual Data Mining'

Author: Grafiati

Published: 10 May 2025

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1

P, Deepak, and Anna Jurek-Loughrey, eds. Linking and Mining Heterogeneous and Multi-view Data. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-01872-6.

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2

Inmon, William H. Tapping into unstructured data: Integrating unstructured data and textual analytics into business intelligence. Upper Saddle River, NJ: Prentice Hall, 2008.

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3

P, Deepak, and Anna Jurek-Loughrey. Linking and Mining Heterogeneous and Multi-view Data. Springer, 2018.

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4

Yu, Philip S., and Chuan Shi. Heterogeneous Information Network Analysis and Applications. Springer, 2018.

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5

Yu, Philip S., and Chuan Shi. Heterogeneous Information Network Analysis and Applications. Springer, 2017.

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6

Mds'13: 2013 Workshop on Mining Data Semantics in Heterogeneous Information Networks. Association for Computing Machinery, 2013.

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7

Textual Data Science with R. Taylor & Francis Group, 2019.

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8

Bécue-Bertaut, Mónica. Textual Data Science with R. Taylor & Francis Group, 2019.

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9

Bécue-Bertaut, Mónica. Textual Data Science with R. Taylor & Francis Group, 2019.

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10

Bécue-Bertaut, Mónica. Textual Data Science with R. Taylor & Francis Group, 2019.

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11

Inmon, Bill. Turning Text into Gold: Taxonomies and Textual Analytics. Technics Publications, LLC, 2017.

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12

Inmon, William, and Anthony Nesavich. Tapping into Unstructured Data: Integrating Unstructured Data and Textual Analytics into Business Intelligence. Pearson Education, 2007.

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13

Harth, Andreas, Ralf Schenkel, and Katja Hose. Linked Data Management. Taylor & Francis Group, 2016.

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14

Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications. Association for Computing Machinery, 2013.

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15

Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications. Association for Computing Machinery, 2012.

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16

Harth, Andreas, Ralf Schenkel, and Katja Hose. Linked Data Management. Taylor & Francis Group, 2016.

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17

Linked Data Management. Taylor & Francis Inc, 2014.

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18

Smiraglia, Richard P., and Andrea Scharnhorst, eds. Linking Knowledge. Ergon – ein Verlag in der Nomos Verlagsgesellschaft, 2021. http://dx.doi.org/10.5771/9783956506611.

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Abstract:

The growth and population of the Semantic Web, especially the Linked Open Data (LOD) Cloud, has brought to the fore the challenges of ordering knowledge for data mining on an unprecedented scale. The LOD Cloud is structured from billions of elements of knowledge and pointers to knowledge organization systems (KOSs) such as ontologies, taxonomies, typologies, thesauri, etc. The variant and heterogeneous knowledge areas that comprise the social sciences and humanities (SSH), including cultural heritage applications are bringing multi-dimensional richness to the LOD Cloud. Each such application arrives with its own challenges regarding KOSs in the Cloud. With contributions by Sören Auer, Gerard Coen, Kathleen Gregory, Mohamad Yaser Jaradeh, Daniel Martínez Ávila, Philipp Mayr, Allard Oelen, Cristina Pattuelli, Tobias Renwick, Andrea Scharnhorst, Ronald Siebes, Aida Slavic, Richard P Smiraglia, Markus Stocker, Rick Szostak, Marnix van Berchum, Charles van den Heuvel, J. Bradford Young, Veruska Zamborlini and Marcia Zeng.

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