A model of information retrieval based on a terminological logic
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic Datalog—a logic for powerful retrieval methods
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
The HySpirit retrieval platform
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Learning probabilistic datalog rules for information classification and transformation
Proceedings of the tenth international conference on Information and knowledge management
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Probabilistic, object-oriented logics for annotation-based retrieval in digital libraries
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Compiling Comp Ling: practical weighted dynamic programming and the Dyna language
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
The VLDB Journal — The International Journal on Very Large Data Bases
Declarative information extraction using datalog with embedded extraction predicates
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Translation as weighted deduction
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The Knowledge Engineering Review
PIRE: an extensible IR engine based on probabilistic datalog
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Mathematical Specification and Logic Modelling in the context of IR
Proceedings of the 2013 Conference on the Theory of Information Retrieval
| Hi-index | 0.00 |
Nowadays information systems are required to be more adaptable and flexible than before to deal with the rapidly increasing quantity of available data and changing information needs. Text Classification (TC) is a useful task that can help to solve different problems in different fields. This paper investigates the application of descriptive approaches for modelling classification. The main objectives are increasing abstraction and flexibility so that expert users are able to customise specific strategies for their needs. The contribution of this paper is two-fold. Firstly, it illustrates that the modelling of classifiers in a descriptive approach is possible and it leads to a close definition w.r.t. mathematical formulations. Moreover, the automatic translation from PDatalog to mathematical formulation is discussed. Secondly, quality and efficiency results prove the approach feasibility for real-scale collections.