Semantic Cloud Generation based on Linked Data for Efficient Semantic Annotation

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1 Semantic Cloud Generation based on Linked Data for Efficient Semantic Annotation - Korea-Germany Joint Workshop for LOD Han-Gyu Ko Dept. of Computer Science, KAIST Korea Advanced Institute of Science and Technology

2 Table Contents 2 Introduction Motivation & Objective Requirements for Semantic Cloud Semantic Annotation Scheme Semantic Cloud Generation Approach Locating Spotting Points Selecting Relations to Traverse Identifying Similarity & Clustering A Case Study Analysis of Generated Semantic Clouds Implementation in the Web-based IPTV Environment Conclusion

3 Motivation 3 To provide semantic annotation as an alternative method for user-generated metadata To overcome the problem of semantic ambiguity of tags with plain texts e.g., Apple the fruit and Apple the company To fulfill the requirements of scalability and usability Problems of previous efforts on semantic annotation [5, 6] Use terms from ontologies created by domain experts Do not provide sufficient options to cover various kinds of semantics Do not necessarily reflect newly created knowledge in an up-to-date manner

4 Objectives 4 To provide a semantic-cloud-based annotation scheme Use semantic clouds as the primary interface Easy to add semantic annotation in resource-constrained environments (e.g., smart phones and IPTVs) To propose the framework of generating efficient semantic clouds To allow users to intuitively recognize candidate concepts with resolving semantic ambiguity To utilize Linked Data to generate semantic clouds

5 Semantic-Cloud-based Annotation Scheme 5

6 Semantic Cloud Generation Framework 6 Linked Data is large-scale & heterogeneous Semantic Web data More than 2.5 billions RDFs from over 200 different datasets [14] The proposed approach need to be incremental and iterative Three phases in the semantic cloud generation:

7 Phase 1: Locating Spotting Points 7 Traversing and finding relevant RDF nodes starting from more important and densely connected RDF nodes is more efficient Basic Principles More abstract nodes have higher degree of connections Relative concept hierarchy can be browsed by using SKOS relationships (skos:broader, skos:narrower)

8 8 Phase 2: Selecting Relations to Traverse Computational complexity for traversing RDF nodes can be reduced by considering relevant relations only Basic Principles Semantically relevant relations can be found by considering user contexts such as interests and preferences User interests interlink with specific relation terms e.g., In case of Movie - relation terms : actor, cinematographer, country, director, music_director, content_rating, story, etc. W3C Recommendations Traverse well-defined and popular terms e.g.) FOAF, DC, SIOC, and SKOS

9 Phase 3: Identifying Similarity & Clustering 9 Semantic similarity between RDF nodes is measured to decide whether to include a visited node into a cloud Basic Principles As the number of common terms becomes larger, the similarity increases As the number of hops from the spotting point becomes larger, the similarity decreases Similarity Formulas TermFreq(l 1, l 2 ) = n(l 1, l 2 ) / n(l 1 ) + n(l 1, l 2 ) / n(l 2 ) l 1 and l 2 : the labels of RDF nodes to compare n(l) : the number of query responses for l n(l 1, l 2 ) : the number of query responses for both l 1, l 2 SemSim(l 1, l 2 ) = TermFreq(l 1, l 2 ) / w h h : the number of hops to traverse w : a weight value

10 A Case Study (1/2) 10 To compare our approach with the traditional approaches of using rdf:type and SKOS relationships Data Preparation RDF triples from Sindice Search API for keyword apple ( Separation of terms despite there is semantic relevance e.g.) Apple Inc. separates from Apple I, Apple IIGS Terms that contain keyword only Balance of contents is not satisfactory (a cloud with only one term) Additional relevant terms that don t contain keyword (Itunes, Macintosh in the pink cloud) Each cloud is more richer than other results (a) rdf:type (b) SKOS parsing (c) Proposed Approach

11 A Case Study (2/2) 11 Implementation in the Web-based IPTV Environment Semantic Cloud Selected Linked Data 4 Annotation Timing Keyword (User input) 2 Cloud Generation Cloud Generation 3 5 Start Button 1

12 12 Finding Spotting Points in Linked Data Manipulating Linked Data in Dataset Level

13 Finding Spotting Points in Linked Data 13 How to Obtain RDF triples from Linked Data Using SPARQL Endpoints Also has problems Some datasets don t provide their endpoints (108 / 221 only) Slow response time (more than 90 seconds in some cases) Using BTC 2010 Data Provided by Falcon-S, Sindice, Swoogle, SWSE, and Watson using the MultiCrawler/SWSE framework!(btc 2010 Linked Data datasets) Didn t work! Using a Collection of Datasets Provide access to a set of copies of datasets via SPARQL endpoint Example For Each Dataset Subject Predicate Object Context Subject Predicate Object Context Duplicate quadruples deletion Namespace-based table classification

14 14 Finding Spotting Points in Linked Data Which one covers the concepts for keyword most? (Coverage) Keyword Apple Domain: 2/9 (22%) Dataset: 7/20 (35%) Triples: 23,747/144,549 (16.4%) Domain: 6/9 (67%) Dataset: 10/20 (50%) Triples: 24,690/144,549 (17.1%) Domain: 3/9 (33%) Dataset: 4/20 (20%) Triples: 25,830/144,549 (17.9%) Domain: 7/9 (78%) Dataset: 11/20 (55%) Triples: 25,015/144,549 (17.3%)

15 Finding Spotting Points in Linked Data 15 Examples of Ambiguous Tag from Del.icio.us Tags apple tiger opera Categories mac, apple, computers, osx, technology, IT food, health, apple, nutrition, fruit, green Photos, nature, animal, tiger, cute, animals Sports, video, tiger, woods, golf, games music, art, opera, culture, design, download software, browser, opera, web, tools, internet Experimental Results (a) Domain (b) Dataset (c) Triple

16 Summary 16 Define the requirements for well-organized semantic clouds Small number of clouds Balance of contents in the cloud No ambiguity among clouds Propose a Semantic Cloud Generation Framework based on Linked Data Locating spotting points Selecting relations to traverse Identifying Similarity & Clustering Show that the proposed approach provides high quality of clouds via a case study

17 Conclusion 17 Contributions Efficient handling of large-scale Semantic Web data (Linked Data) Generating semantic clouds that enable users to Specify semantics by using simply keywords Intuitively recognize semantic options Easily resolve semantic ambiguity Future Works Empirical studies to decide followings Optimal number of spotting point Maximum number of hops to traverse Threshold value to decide whether a RDF is merged in the same cloud Quantitative evaluation in terms of improved accuracy Discussion about graph traverse performance Measure the usability of the proposed approach June 20, 2011

18 References 18 [1] Christian B., Tom H., Berners-Lee T.: Linked Data The Story So Far. International Journal on Semantic Web and Information Systems, vol. 5, issue 3, 1-22 (2009) [2] Bayerl P.S., Lungen H., Gut U., Paul K.I.: Methodology for reliable schema development and evaluation of manual annotations. Knowledge Markup and Semantic Annotation at the International Conference on Knowledge Capture 2003 (2003) [3] Vehvilaiinen A., Hyvonen E., Alm O.: A Semi-Automatic Semantic Annotation and Authoring Tool for a Library Help Desk Service. In Proceedings of the 1st Semantic Authoring and Annotation Conference 2006 (2006) [4] Kiryakov A., Popov B., Ognyanoff Dl., Manov D., Kirilov A., Goranov M.: Semantic Annotation, Indexing, and Retrieval. In ELSEVIER Journal of Web Semantics 2004 (2004) [5] Reeve L., Han H.: Survey of Semantic Annotation Platforms. In ACM Symposium on Applied Computing (2005) [6] Uren V., Cimiano P., Iria J., Handschuh S., Vargas-Vera M., Motta E., Ciravegna F.: Semantic annotation for knowledge management: Requirements and a survey of the state of the art. In ELSEVIER Journal of Web Semantics (2005) [7] In-Young Ko, Sang-Ho Choi, Han-Gyu Ko.: A Blog-centered IPTV Environment for Enhancing Contents Provision, Consumption, and Evolution. In Proceedings of the 10th International Conference on Web Engineering 2010 LNCS, vol. 6189, (2010) [8] Lord F.M.: Optimal Number of Choices per Item A Comparison of Four Approaches. In Journal of Educational Measurement, vol. 14, no. 1, (1977) [9] Ding L., Finin T., Joshi A., Pank R., Cost S.R., Peng Y., Reddivari P., Doshi V., Sachs J.: Swoogle: a search and metadata eigine for the semantic web. In Proceedings of the CIMK 2004 (2004) [10] Lord F.M.: Optimal Number of Choices per Item A Comparison of Four Approaches. In Journal of Educational Measurement, vol. 14, no. 1, (1977) [11] Tummarello, G., Delbru, R., Oren, E.: Sindice.com: Weaving the Open Linked Data. In Proceedings of the 6th International Semantic Web and 2nd Asian Conference on Asian Semantic Web Conference. LNCS, vol. 4825, (2007) [12] Delbru R., Rakhmawati N.A., Tummarello G.: Sindice at SemSearch In Proceedings of the 19th International World Wide Web Conference, Raleigh, North Carolina, USA, April (2010) [13] W3C SWEO Community Project Linking Open Data [14] Linking Open Data Statistics [15] Faviki [16] Roberto M., Azzurra R., Tommaso D. N., Eugenio D.: Semantic tag cloud generation via Dbpedia, In Proceedings of the 11 th International Conference, EC-Web 2010, Bilbao, Spain, September (2010) August

19 19 Questions?

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