Social semantic web mining / Tope Omitola, Sebastián A. Ríos.
The past ten years have seen a rapid growth in the numbers of people signing up to use Web-based social networks (hundreds of millions of new members are now joining the main services each year) with a large amount of content being shared on these networks (tens of billions of content items are shar...
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Full Text (via Morgan & Claypool) |
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| Main Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
Morgan & Claypool,
2015.
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| Series: | Synthesis lectures on the semantic web, theory and technology ;
# 10. |
| Subjects: |
MARC
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| 100 | 1 | |a Omitola, Tope, |e author. |0 http://id.loc.gov/authorities/names/no2015041381. | |
| 245 | 1 | 0 | |a Social semantic web mining / |c Tope Omitola, Sebastián A. Ríos. |
| 264 | 1 | |a San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : |b Morgan & Claypool, |c 2015. | |
| 300 | |a 1 PDF (xv, 138 pages) : |b illustrations. | ||
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| 490 | 1 | |a Synthesis lectures on the semantic web, theory and technology, |x 2160-472X ; |v # 10. | |
| 500 | |a Part of: Synthesis digital library of engineering and computer science. | ||
| 504 | |a Includes bibliographical references (pages 121-135) | ||
| 505 | 0 | |a 1. Introduction and the web -- 1.1 Introduction -- 1.2 The world wide web -- 1.2.1 History and evolution of the web -- 1.2.2 A short explanation of how the web works -- 1.3 Global impact of the internet and the web -- 1.4 Web mining, the social web, and the semantic web -- 1.5 Conclusion -- | |
| 505 | 8 | |a 2. Web mining -- 2.1 Mining the world wide web -- 2.1.1 Different types of web mining processes -- 2.1.2 Directed vs. undirected web mining processes -- 2.1.3 Supervised vs. unsupervised algorithms -- 2.2 Traditional framework for offline website enhancements -- 2.2.1 Resource discovery/data selection -- 2.2.2 Extraction/preprocessing -- 2.2.3 Data generalization -- 2.2.4 Analysis/evaluation -- 2.3 Clustering algorithms for web mining -- 2.3.1 Clustering methods -- 2.3.2 Self-organizing feature maps -- 2.3.3 K-means clustering algorithm -- 2.3.4 Decision trees -- 2.4 Dissimilarity and similarity measures -- 2.5 Latent semantics using LSA techniques -- 2.6 Conclusion -- | |
| 505 | 8 | |a 3. The social web -- 3.1 What is the social web? -- 3.1.1 A brief history of the social web -- 3.1.2 Online social networks -- 3.1.3 Social media creation and sharing -- 3.1.4 Tagging, folksonomies, and hashtags -- 3.1.5 Crowdsourcing and citizen sensors -- 3.1.6 Limitations with social spaces -- 3.2 Conclusion -- | |
| 505 | 8 | |a 4. The semantic web -- 4.1 Introduction -- 4.1.1 From syntax to semantics -- 4.1.2 A great big graph of metadata and vocabularies -- 4.1.3 Issues with vocabulary creation -- 4.1.4 Representation formats -- 4.1.5 Semantic web and SEO -- 4.1.6 Comparisons with microformats and microdata -- 4.2 Conclusion -- | |
| 505 | 8 | |a 5. The social semantic web -- 5.1 Introduction -- 5.2 The social semantic web -- 5.3 Some potential uses of the social semantic web -- 5.4 Integrating existing social spaces on the web -- 5.5 Extension to further social spaces -- 5.6 Standard, interoperable descriptions of social data -- 5.7 The long tail of information domains -- 5.8 Social semantic web vocabularies -- 5.8.1 FOAF friend-of-a-friend -- 5.8.2 hCard and XFN -- 5.8.3 SIOC-semantically interlinked online communities -- 5.8.4 Other ontologies -- 5.9 Conclusion -- | |
| 505 | 8 | |a 6. Social semantic web mining -- 6.1 Introduction -- 6.2 Provenance -- 6.2.1 Rumors and dissimulation in online social networks -- 6.2.2 What is provenance? -- 6.2.3 Modeling provenance -- 6.2.4 Provenance of social data -- 6.2.5 Provenance on the web -- 6.3 SIOCM -- 6.3.1 Ontological representation of online social communities -- 6.4 Conclusion -- | |
| 505 | 8 | |a 7. Social semantic web mining of communities -- 7.1 Introduction -- 7.2 Purpose evolution -- 7.3 Goals as a measure of purpose accomplishment -- 7.4 Mining goals from texts: concept-based text mining -- 7.4.1 Fuzzy logic for goals classification -- 7.4.2 Identification and definition of goals -- 7.5 Real application of community purpose monitoring -- 7.5.1 The plexilandia community -- 7.5.2 Concept-based text mining application -- 7.5.3 Analysis of results -- 7.5.4 Results evaluation -- 7.5.5 Applying SIOCM to store goal definitions -- 7.5.6 Extracting topic-filtered networks using SIOCM -- 7.6 Conclusion -- | |
| 505 | 8 | |a 8. Social semantic web mining of groups -- 8.1 Introduction -- 8.2 Previous work -- 8.2.1 Topic-based social network analysis -- 8.2.2 Social network analysis on the dark web -- 8.3 Methodology for group key member discovery -- 8.3.1 Basic notation -- 8.3.2 Topic modeling -- 8.3.3 Network configuration -- 8.3.4 Topic-based network filtering -- 8.3.5 Network construction -- 8.4 Experimental setup and results -- 8.4.1 Results and discussion -- 8.5 Conclusion -- | |
| 505 | 8 | |a 9. Social semantic web mining of users -- 9.1 Introduction -- 9.2 Modeling a distributed user profile with interests -- 9.3 Related work mining user profiles on the social semantic web -- 9.4 Representing user interest profiles -- 9.5 Leveraging the provenance of user data -- 9.6 Interests on the web of data -- 9.7 Interest mining on the social web -- 9.7.1 Bag-of-words vs. disambiguated entities -- 9.7.2 Time decay of interests -- 9.7.3 Categories vs. resources -- 9.7.4 Provenance-based features -- 9.8 An architecture for aggregating user profiles of interests on the social web -- 9.8.1 Evaluation of aggregated user profiles -- 9.9 Conclusion -- | |
| 505 | 8 | |a 10. Conclusions -- 10.1 Summary -- 10.2 Future work -- Bibliography -- Authors' biographies. | |
| 520 | 3 | |a The past ten years have seen a rapid growth in the numbers of people signing up to use Web-based social networks (hundreds of millions of new members are now joining the main services each year) with a large amount of content being shared on these networks (tens of billions of content items are shared each month). With this growth in usage and data being generated, there are many opportunities to discover the knowledge that is often inherent but somewhat hidden in these networks. Web mining techniques are being used to derive this hidden knowledge. In addition, the Semantic Web, including the Linked Data initiative to connect previously disconnected datasets, is making it possible to connect data from across various social spaces through common representations and agreed upon terms for people, content items, etc. In this book, we detail some current research being carried out to semantically represent the implicit and explicit structures on the Social Web, along with the techniques being used to elicit relevant knowledge from these structures, and we present the mechanisms that can be used to intelligently mesh these semantic representations with intelligent knowledge discovery processes. We begin this book with an overview of the origins of the Web, and then show how web intelligence can be derived from a combination of web and Social Web mining. We give an overview of the Social and Semantic Webs, followed by a description of the combined Social Semantic Web (along with some of the possibilities it affords), and the various semantic representation formats for the data created in social networks and on social media sites. | |
| 588 | |a Title from PDF title page (viewed on February 22, 2015) | ||
| 650 | 0 | |a Web usage mining. |0 http://id.loc.gov/authorities/subjects/sh00009186. | |
| 650 | 0 | |a Online social networks. |0 http://id.loc.gov/authorities/subjects/sh2006006990. | |
| 650 | 0 | |a Semantic Web. |0 http://id.loc.gov/authorities/subjects/sh2002000569. | |
| 700 | 1 | |a Ríos, Sebastián A., |e author. |0 http://id.loc.gov/authorities/names/no2015041335. | |
| 700 | 1 | |a Breslin, John G. |q (John Gerard), |e author. |0 http://id.loc.gov/authorities/names/no2010003650. | |
| 776 | 0 | 8 | |i Print version: |z 9781627053983. |
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