Abstract
The problem of ‘information content’ of an information system appears elusive. In the field of databases, the information content of a database has been taken as the instance of a database. We argue that this view misses two fundamental points. One is a convincing conception of the phenomenon concerning information in databases, especially a properly defined notion of ‘information content’. The other is a framework for reasoning about information content. In this paper, we suggest a modification of the well known definition of ‘information content’ given by Dretske(Knowledge and the flow of information,1981). We then define what we call the ‘information content inclusion’ relation (IIR for short) between two random events. We present a set of inference rules for reasoning about information content, which we call the IIR Rules. Then we explore how these ideas and the rules may be used in a database setting to look at databases and to derive otherwise hidden information by deriving new relations from a given set of IIR. A prototype is presented, which shows how the idea of IIR-Reasoning might be exploited in a database setting including the relationship between real world events and database values.
Similar content being viewed by others
References
Amble T (1987). Logic programming and knowledge engineering. Addison Wesley, Reading
Arenas M and Libkin L (2005). An information-theoretic approach to normal forms for relational and XML data. J ACM 52: 246–283
Armstrong WW (1974) Dependency structures of database relationships. In: Proceedings of IFIP 74, North- Holland Pub. Co., Amsterdam, pp 580–583
Barwise J and Perry J (1983). Situations and attitudes. MIT Press, Cambridge
Barwise J, Seligman J (1997) Information flow: the logic of distributed systems. Cambridge University Press, London. ISBN 0-521-58386-1
Batini C, Ceri S and Navathe SB (1992). Conceptual database design—an eitity–relationship approach. The Benjamin/Cummings Publishing Company, Inc., USA
Berners-Lee T (1998) What the semantic web can represent, http://www.w3.org/DesignIssues/RDFnot.html. Accessed Jan 2007
Chachoua M and Pacholczyk D (2002). Qualitative reasoning under ignorance and information-relevant extraction. Knowl Inf Syst 4: 483–506
Crowe MK (2007) The Pyrrho database management system, computing and information systems technical reports, 38, University of Paisley, UK. http://www.pyrrhodb.com
Dalkilic MM, Roberston EL (2000) Information dependencies.In: Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, Dallas, Texas, USA pp 245–253
Devlin K (1991). Logic and information. Cambridge University Press, London
Dey AK, Abowd GD (2000) Towards a better understanding of context and context-awareness. In the Workshop on The What, Who, Where, When, and How of Context-Awareness, as part of the 2000 Conference on Human Factors in Computing Systems (CHI 2000), The Hague, The Netherlands, 3 April 2000.
Dretske FI (1981). Knowledge and the flow of information. Blackwell, Oxford
Duží M (2001) Logical foundations of conceptual modelling. In: VŠB-TU Ostrava.
Farquhar A, Fikes R and Rice J (1997). The ontolingua server: a tool for collaborative ontology construction. Int J Hum Comput Stud 46(6): 707–727
Feng J (1998) The ‘Information Content’ problem of a conceptual data schema, SYSTEMIST, vol 20, No.4, pp 221–233, ISSN:0961-8309
Feng J, Crowe M (1999) The notion of ‘Classes of a Path’ in ER schemas. In: Proceedings of third East European Conference on advances in databases and information systems, ADBIS’99. Springer, Berlin.
Floridi L (2005). Is semantic information meaningful data. Philos Phenomenol Res 70(2): 351–370
Genesereth MR, Fikes RE (1998) Knowledge interchange format (KIF). Draft proposed American National Standard, NCITS. T2/98-004
Grosso W, Eriksson H, Fergerson R, Gennari J, Musen M (1999) Knowledge modelling at the millennium (the design and evolution of Protégé-2000). In: 12th Workshop on knowledge acquisition, modeling and management, Banff, Alberta, Canada.
Hu T and Sung S (2006). Finding centroid clusterings with entropy-based criteria. Knowl Inf Syst 10(4): 505–514
Hu W, Feng J (2002) Some considerations for a semantic analysis of conceptual data schemata. In: Ragsdell E et al (eds) Systems theory and practice in the knowledge age. Kluwer Academic/Plenum Publishers, New York. ISBN 0-306-47247-3
Hull R (1986). Relative information capacity of simple relational database schemata. SIAM J Comput 15(3): 856–886
Jurisica I, Mylopoulos J and Yu E (2004). Ontologies for knowledge management: an information systems perspective. Knowl Inf Syst 6: 380–401
Kalfoglou Y, Schorlemmer M (2003) IF-Map: an ontology mapping method based on information flow theory. J Data Semant I. Lecture Notes in Computer Science 2800, pp 98–127
Lassila O, Swick R (1999) Resource description framework (RDF) model and syntax specification. http://www.w3.org/TR/1999/REC-rdf-syntax-19990222/. W3C Recommendation.
Lee TT (1987a). An information-theoretic analysis of relational databases—part I: data dependencies and information metric. IEEE Trans Softw Eng 13(10): 1049–1061
Lee TT (1987b). An information-theoretic analysis of relational databases—part II: information structure of database schemas. IEEE Trans Softw Eng 13(10): 1061–1072
Levene M (1998). On the information content of semi-structured databases. Acta Cybern 13: 257–275
Li D (1984). A Prolog database system. Research Studies Press, New York
Lucas R (1988). Database applications using Prolog. Halsted Press, New York
Miller RJ, Ioannidis YE, Ramakrishnan R(1993) The use of information capacity in schema integration and translation. In: Proceedings of the International Conference on very large data bases, Dublin, Ireland, pp 120–133
Miller RJ, Ioannidis YE and Ramakrishnan R (1994). Schema equivalence in heterogeneous systems, bridging theory and practice. Inf Syst 19(1): 3–31
Ng G, Chan K, Erdogan S and Singh h (2000). Neural network learning using entropy cycle. Knowl Inf Syst 2: 53–72
Noy N and Klein M (2004). Ontology evolution: not the same as schema evolution. Knowl Inf Syst 6: 428–440
Nurcan S, Kouloumdjian J (1991) An advanced knowledge base management system based on the integration of logic programming and relational databases. In: Proceedings of IEEE, CompEuro ’91, Bologna, Ital, pp 740–744
Shannon CE (1948) A mathematical theory of communication. Bell Sys Tech J 27:379–423,623–656
Shimojima A (1996) On the efficacy of representation. Ph.D. Thesis, The Department of Philosophy, Indiana University
Sieg A, Mobasher B, Burke R, Prabu G, Lytinen S (2005) Representing user information context with ontologies. In: Proceedings of the 3rd International Conference on universal access in human–computer interaction, Las Vegas, NV
Smith MK, Welty C, McGuinness DL (2004) OWL web ontology language guide. http://www.w3.org/TR/2004/REC-owl-guide-20040210/. W3C Recommendation
Wielemaker J (2007) SWI-Prolog semantic web library, HCS, University of Amsterdam. http://www.swi-prolog.org. Accessed on Jan 2007
Wille R (1997) Introduction to formal concept analysis. In: Negrini G (ed), Modelli e modellizzazione. Models and modelling. Consiglio Nazionale delle Ricerche, Instituto di Studi sulli Ricerca e Documentazione Scientifica, Roma, 39-51
Xu H, Feng J (2002) Towards a definition of the ‘Information Bearing Capability’ of a Conceptual Data Schema. In: Systems theory and practice in the knowledge age. Ragsdell E et al (eds) Kluwer Academic/Plenum Publishers, New York. ISBN 0-306-47247-3
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Xu, K., Feng, J. & Crowe, M. Defining the notion of ‘Information Content’ and reasoning about it in a database. Knowl Inf Syst 18, 29–59 (2009). https://doi.org/10.1007/s10115-008-0129-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-008-0129-3