Blockeel, H. and L. De Raedt (1996). Relational knowledge discovery in databases. In Proceedings of the Sixth International Workshop on Inductive Logic Programming, Volume 1314 of Lecture Notes in Artificial Intelligence, pp. 199--212. SpringerVerlag.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....those used in KDD Cup 2001. The idea of shifting from ILP techniques to a search space consisting of database queries using SQL instead of logical clauses as the language bias and results from database theory as the next step in the relational data mining field had been already proposed in (Blockeel and De Raedt, 1997a) There, Blockeel et al. borrow techniques from ILP to tackle the problem of finding relationships between relations using relational algebra as the language bias. An equivalent algorithm using SQL is straightforward. That algorithm has not been implemented yet. Chapter 2 discusses some of the ....

....easy to use, can be used by a range of ILP systems, and can represent the complexity of the problem in a clear and simple way. In that way, even the non expert users will be able to model their problems and use a wide range of ILP engines, choosing that one that best fits their current needs. In (Blockeel and De Raedt, 1997a) three ways of bridging ILP and relational databases are presented. These are possible solutions to the problem of efficiency issues mentioned before. They are briefly described in order of adaptation of the ILP system to the database system. The simplest one is pre processing of the ....

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Blockeel, H., and De Raedt, L. Relational Knowledge Discovery in Databases. In Proceedings of the 6 th International Workshop on Inductive Logic Programming, volume 1314 of Lecture Notes in Artificial Intelligence, Springer- Verlag, 1997.

....to have all answers generated by the database. Up to now, we have not investigated this kind of coupling. But such a direct mapping between database tables queries and predicates might be investigated in the future. Other ways in which an ILP system can be linked to a database are described in [Blockeel and De Raedt, 1996]. D.4 Implementation details of the Ell package The EII is available as a separate package which can be included in an ILP system. The package is implemented in Prolog and C, and consists of the following files: extended input. gert The main file for the Eli. It defines the following Prolog ....

H. Blockeel and L. De Raedt. Relational know- ledge discovery in databases. In Proceedings of the Sixth International Workshop on Inductive Logic Programming, volume 1314 of Lecture Notes in Artificial Intelligence, pages 199-212. Springer-Verlag, 1996. 215

....in particular the database, untouched. Before we move on a third evaluation measure, let us once more establish the link with relational database terminology. In SQL syntax the absolute frequency of Q with respect to a relational database can be obtained with the following query, inspired by [18, 5]: select count(distinct ) from select fields that correspond to the variables in key from relations in Q where conditions expressed in Q For instance, for query Q 1 , the following SQL query select count(distinct ) from select Customer.Id from Customer, Parent, Buys where Customer.Id = ....

H. Blockeel and L. De Raedt. Relational knowledge discovery in databases. In Proceedings of the Sixth International Workshop on Inductive Logic Programming, Lecture Notes in Artificial Intelligence, pages 199--212. Springer-Verlag, Berlin, 1996.

.... extensively in the field of Inductive Logic Programming (ILP) 6, 11] However, these approaches are mostly based on data stored as Prolog programs, and little attention has been given to data stored in relational database and to how knowledge of the data model can help to guide the search process [1, 12, 19]. Nor has a lot of attention been given to efficiency and scalability issues. Conceptually, however, there are many parallels between ILP and multi relational data mining. Where ILP can be seen as learning from a set of predicates, multi relational data mining can be seen as learning from a ....

Blockeel, H., De Raedt, L. Relational knowledge discovery in databases, In Stephen Muggleton, editor, Proceedings of the Sixth International Workshop on Inductive Logic Programming (ILP'96), 199-211, Volume 314 of Lecture Notes in Artificial Intelligence, Springer Verlag, 1996

....keypred(KeyVars) r [ fkeypred(KeyVars) Qg)j; i.e. the number of bindings of the key variables with which the query Q is true. Once more, we establish the link with relational database terminology. In SQL syntax the frequency of Q can be obtained with the following query, inspired by [35, 7]: SELECT count(distinct ) FROM SELECT fields that correspond to KeyVars FROM relations in Q WHERE conditions expressed in Q We next show how restrictions on the Datalog database r and the language L of Datalog queries lead to some of the existing frequent pattern discovery settings. 2.3 Item ....

H. Blockeel and L. De Raedt. Relational knowledge discovery in databases. In Proceedings of the 6th International Workshop on Inductive Logic Programming, volume 1314 of Lecture Notes in Artificial Intelligence, pages 199--212. Springer-Verlag, 1996.

....as required by KDD challenge, we have to expect that the basic ILP framework would be inadequate. In fact, recent papers describe solutions for interfacing ILP systems to relational databases by mapping logical formulas onto SQL programs (Lindner and Morik, 1995; Brockhausen and Morik, 1996; Blockeel and De Raedt, 1996). The second basic difference, namely the extensive use of internal disjunction, is not at the level of language bias, because any formula containing internal disjunctions can always be translated into a set of standard clauses. The difference emerges at the level of the very learning methodology, ....

Blockeel, H. and De Raedt, L. (1996). Relational knowledge discovery in databases. In Proc. of the MLnet Familiarization Workshop, pages 111--124, Bari, Italy.

.... y For a quick introduction to the theory of ILP see [Gro96] D R A F T comments welcome] 5 A less conservative approach would be to consider the integration of a system like Claudien [DD95] and address the integration of this clausal induction system as suggested by Blockeel and de Raedt [BD96] In contrast to the conservative approach of [DD95] Blockeel and de Raedt note the obvious difficulties with simpler propositional ILP approaches, and begin to address the required integration of a more sophisticated ILP method within a data base query system. Yet another issue requiring much ....

H. Blockeel and Luc De Raedt. Relational knowledge discovery in databases. In Johannes F¨urnkranz and Bernhard Pfahringer, editors, 13th International Conference on Machine Learning Workshop on Data Mining with Inductive Logic Programming. [see http://www.ai.univie.ac.at/ilp kdd/schedule.html], July 2 1996.

....natural language documents. To actually use inductive logic programming to learn rules for this task, one would have to be able to robustly produce a first order representation of the original documents. However, relational learning does not have to limited to first order logic representations [ Blockeel and deRaedt, 1996 ] Therefore, we have chosen instead to apply ILP based techniques to a rule representation more suited to the task. Using only a corpus of documents paired with filled templates, Rapier (Robust Automated Production of Information Extraction Rules) learns unbounded Eliza like patterns [ ....

Henrik Blockeel and Luc deRaedt. Relational knowledge discovery in databases. In Proceedings of the Sixth International Workshop on Inductive Logic Programming ", pages 1--13, 1996.

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Blockeel, H. and L. De Raedt (1996). Relational knowledge discovery in databases. In Proceedings of the Sixth International Workshop on Inductive Logic Programming, Volume 1314 of Lecture Notes in Artificial Intelligence, pp. 199--212. SpringerVerlag.

No context found.

H. Blockeel and L. De Raedt. Relational knowledge discovery in databases. In Proceedings of the Sixth International Workshop on Inductive Logic Programming, pages 199-212. Springer-Verlag, 1996.

....that the computational complexity of querying all examples (in random order) will be at least quadratic in the number of examples. Scalability to large knowledge bases is obtained in practical ILP systems either (1) by connecting the ILP system to an external database management system (DBMS) [1, 15, 13] or (2) by using independent examples that can be loaded one by one, i.e. the Learning from Interpretations setting [3] in which each example is de ned as a small relational database (typically implemented as a set of facts, i.e. an interpretation) The bene t of (2) over (1) is that (1) must ....

H. Blockeel and L. De Raedt. Relational knowledge discovery in databases. In Proceedings of the Sixth International Workshop on Inductive Logic Programming, volume 1314 of Lecture Notes in Arti cial Intelligence, pages 199-212. SpringerVerlag, 1996.

.... in the field of Inductive Logic Programming (ILP) 6, 15] However, these approaches are mostly based on data stored as Prolog programmes, and little attention has been given to data stored in relational database and to how knowledge of the data model can help to guide the search process [1, 16, 21]. Nor has a lot of attention been given to efficiency and scalability issues. Our approach combines the achievements of the KDD field with some of those of the ILP field. We will demonstrate how existing ILP algorithms [2, 4] which have shown their practical applicability, can be implemented as ....

Blockeel, H., De Raedt, L. Relational knowledge discovery in databases, Proceedings of the Sixth International Workshop on Inductive Logic Programming, Volume 314 of Lecture Notes in Artificial Intelligence, Springer Verlag, 1996

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Blockeel, H. and De Raedt, L.: Relational Knowledge Discovery in Databases. In: Proceedings of the sixth internal workshop of Inductive Logic Programming, volume 1312 of Lecture Notes in Artificial Intelligence, 199-212, Springer-Verlag (1996)

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Blockeel, H., and De Raedt, L.: Relational Knowledge Discovery in Databases. In: Proceedings of the 6th International Workshop on Inductive Logic Programming, volume 1314 of Lecture Notes in Artificial Intelligence, Springer- Verlag (1997)

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