Report on methods and algorithms for bootstrapping Semantic Web content from user repositories and reaching consensus on the use of semantics

The INSEMTIVES projects explores two approaches to tackle the issue of missing semantic content in the semantic web: finding insentives to motivate the user to provide more annotation and minimizing the cold start issue to provide enough critical mass of annotation to the users so they can see the benefits of semantic content. In this deliverable, we discuss solutions for reaching a critical mass of quality semantic annotations. We describe two techniques to solve this issue: a) automatic bootstrapping of annotations from the user’s knowledge and the content of resources and b) consensus reaching techniques to ensure quality annotations that are understood and shared by most of the users of the semantic web.

[1]  J A Drake,et al.  Divergent perspectives on community convergence. , 1997, Trends in ecology & evolution.

[2]  John Riedl,et al.  tagging, communities, vocabulary, evolution , 2006, CSCW '06.

[3]  Pierre Andrews,et al.  Report on methods and algorithms for linking user-generated semantic annotations to Semantic Web and supporting their evolution in time , 2010 .

[4]  Craig A. N. Soules,et al.  Connections: using context to enhance file search , 2005, SOSP '05.

[5]  Hsinchun Chen,et al.  Collaborative systems: solving the vocabulary problem , 1994, Computer.

[6]  Peter Mika.  Ontologies Are Us: A Unified Model of Social Networks and Semantics , 2005, International Semantic Web Conference.

[7]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[8]  Alexandre Passant,et al.  Meaning Of A Tag: A collaborative approach to bridge the gap between tagging and Linked Data , 2008, LDOW.

[9]  Steffen Staab,et al.  Emergent Semantics Principles and Issues , 2004, DASFAA.

[10]  Owen Kaser,et al.  Tag-Cloud Drawing: Algorithms for Cloud Visualization , 2007, ArXiv.

[11]  Fausto Giunchiglia,et al.  Lightweight Parsing of Natural Language Metadata , 2009 .

[12]  Simone Braun,et al.  Ontology Maturing: a Collaborative Web 2.0 Approach to Ontology Engineering , 2007, CKC.

[13]  Bernardo A. Huberman,et al.  The Structure of Collaborative Tagging Systems , 2005, ArXiv.

[14]  Borislav Popov,et al.  Report on the State-of-the-Art and Requirements for Annotation Representation Models , 2010 .

[15]  James A. Hendler.  Web 3.0: Chicken Farms on the Semantic Web , 2008, Computer.

[16]  Francisco Herrera,et al.  A consensus model for multiperson decision making with different preference structures , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[17]  Ralf Krestel,et al.  Tag Recommendation Using Probabilistic Topic Models , 2009, DC@PKDD/ECML.

[18]  Chun Chen,et al.  Personalized tag recommendation using graph-based ranking on multi-type interrelated objects , 2009, SIGIR.

[19]  Filippo Lanubile,et al.  Towards Social Semantic Suggestive Tagging , 2007, SWAP.

[20]  Ramanathan V. Guha,et al.  SemTag and seeker: bootstrapping the semantic web via automated semantic annotation , 2003, WWW '03.

[21]  Amit P. Sheth,et al.  Semantics for the Semantic Web: The Implicit, the Formal and the Powerful , 2005, Int. J. Semantic Web Inf. Syst..

[22]  Michael Hausenblas,et al.  Building Linked Data For Both Humans and Machines , 2008, LDOW.

[23]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[24]  Lawrence Birnbaum,et al.  TagAssist: Automatic Tag Suggestion for Blog Posts , 2007, ICWSM.

[25]  Pierre Andrews,et al.  Specification of Models for Representing Single-user and Community-based Annotations of Web Resources , 2010 .

[26]  Filippo Lanubile,et al.  Community-Driven Ontology Evolution Based on Folksonomies , 2006, OTM Workshops.

[27]  Ingmar Weber,et al.  Personalized, interactive tag recommendation for flickr , 2008, RecSys '08.

[28]  Jon M. Kleinberg,et al.  Mapping the world's photos , 2009, WWW '09.

[29]  Hector Garcia-Molina,et al.  Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems , 2006 .

[30]  Jun Wang,et al.  Convergence analysis for collective vocabulary development , 2006, AAMAS '06.

[31]  Martin Rajman,et al.  Conceptual document indexing using a large scale semantic dictionary providing a concept hierarchy , 2005 .

[32]  Grigoris Antoniou,et al.  Evolving Ontology Evolution , 2006, SOFSEM.

[33]  Xuanjing Huang,et al.  From Web Directories to Ontologies: Natural Language Processing Challenges , 2007, ISWC/ASWC.

[34]  Vlad Tanasescu,et al.  Extreme Tagging: Emergent Semantics through the Tagging of Tags , 2007, ESOE.

[35]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[36]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[37]  James A. Hendler,et al.  The Semantic Web" in Scientific American , 2001 .

[38]  Atanas Kiryakov,et al.  KIM – a semantic platform for information extraction and retrieval , 2004, Natural Language Engineering.

[39]  Simon King,et al.  From context to content: leveraging context to infer media metadata , 2004, MULTIMEDIA '04.

[40]  Amit P. Sheth,et al.  Semantic Enhancement Engine: A Modular Document Enhancement Platform for Semantic Applications over Heterogeneous Content , 2002 .