Probabilistic, object-oriented logics for annotation-based retrieval in digital libraries

In this paper we introduce POLAR, a probabilistic object-oriented logical framework for annotation-based information retrieval. In POLAR, the knowledge about digital objects, annotations and their relationships in a digital library repository can be modelled considering certain characteristics of annotations and annotated objects. Insights about these characteristics are gained by an analysis of the annotation models behind existing systems and a discussion of an object-oriented, logical view on relevant objects in a digital library. Retrieval methods applied in a digital library should take annotations into account to satisfy users' information needs. POLAR thus supports a wide range of flexible and powerful annotation-based fact and content queries by making use of knowledge and relevance augmentation. An evaluation of our approach on email discussions shows performance improvements when annotation characteristics are considered

[1]  Nicola Ferro,et al.  Annotations: Enriching a Digital Library , 2003, ECDL.

[2]  Ingo Frommholz.  Annotation-Based Document Retrieval with Probabilistic Logics , 2007, ECDL.

[3]  Hans-Peter Frei,et al.  The Use of Semantic Links in Hypertext Information Retrieval , 1995, Inf. Process. Manag..

[4]  Maristella Agosti,et al.  Information Retrieval and Hypertext , 1996, Information Retrieval and Hypertext.

[5]  Stefano Levialdi,et al.  MADCOW: a multimedia digital annotation system , 2004, AVI.

[6]  Nicola Ferro,et al.  Annotations in Digital Libraries and Collaboratories - Facets, Models and Usage , 2004, ECDL.

[7]  Henrik Nottelmann,et al.  PIRE: An Extensible IR Engine Based on Probabilistic Datalog , 2005, ECIR.

[8]  C.C. Marshall,et al.  Exploring the relationship between personal and public annotations , 2004, Proceedings of the 2004 Joint ACM/IEEE Conference on Digital Libraries, 2004..

[9]  Erich J. Neuhold,et al.  COLLATE – A collaboratory supporting research on historic European films , 2004, International Journal on Digital Libraries.

[10]  Nicola Ferro,et al.  Annotations as Context for Searching Documents , 2005, CoLIS.

[11]  T. Kamps,et al.  Annotation-based Document Retrieval with Four-Valued Probabilistic Datalog , 2004 .

[12]  Panos Constantopoulos,et al.  Research and Advanced Technology for Digital Libraries , 2001, Lecture Notes in Computer Science.

[13]  Nicola Orio,et al.  Annotating illuminated manuscripts: an effective tool for research and education , 2005, Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '05).

[14]  Bill N. Schilit,et al.  From reading to retrieval: freeform ink annotations as queries , 1999, SIGIR '99.

[15]  Norbert Fuhr,et al.  HySpirit - A Probabilistic Inference Engine for Hypermedia Retrieval in Large Databases , 1998, EDBT.

[16]  C. J. van Rijsbergen,et al.  A Non-Classical Logic for Information Retrieval , 1997, Comput. J..

[17]  Marja-Riitta Koivunen,et al.  Annotea: an open RDF infrastructure for shared Web annotations , 2001, WWW '01.

[18]  Eric Brill,et al.  Learning effective ranking functions for newsgroup search , 2004, SIGIR '04.

[19]  Dalia Guerreiro,et al.  Research and Advanced Technology for Digital Libraries , 1997, Lecture Notes in Computer Science.

[20]  Catherine C. Marshall,et al.  Toward an ecology of hypertext annotation , 1998, HYPERTEXT '98.

[21]  Norbert Fuhr,et al.  DOLORES: a system for logic-based retrieval of multimedia objects , 1998, SIGIR '98.

[22]  Robert Wilensky,et al.  Multivalent Annotations , 1997, ECDL.

[23]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[24]  Diego Calvanese,et al.  The Description Logic Handbook: Theory, Implementation, and Applications , 2003, Description Logic Handbook.

[25]  Ellen M. Voorhees,et al.  The fourteenth text retrieval conference TREC 2005 , 2006 .

[26]  Norbert Fuhr,et al.  Evaluating Strategic Support for Information Access in the DAFFODIL System , 2004, ECDL.

[27]  Ingo Frommholz.  Applying the Annotation View on Messages for Discussion Search , 2005, TREC.

[28]  Thomas Roelleke.  POOL: probabilistic object oriented logical representation and retrieval of complex objects: a model for hypermedia retrieval , 1999 .