Efficient θ-subsumption based on graph algorithms

Scheffer, Tobias; Herbrich, Ralf; Wysotzki, Fritz

doi:10.1007/3-540-63494-0_57

Cited by 23 publications

(44 citation statements)

References 14 publications

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“…Scheffer et al [71] propose an improved implementation of k-locality, based on the construction of an intermediate data structure, the substitution graph . Along the same lines, Santos Costa et al .…”

Section: Exploiting Particular Clause Structuresmentioning

confidence: 99%

Scalability and efficiency in multi-relational data mining

Blockeel

Sebag

2003

SIGKDD Explor. Newsl.

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Efficiency and Scalability have always been important concerns in the field of data mining, and are even more so in the multi-relational context, which is inherently more complex . The issue has been receiving an increasing amount of attention during the last few years, and quite a number of theoretical results, algorithms and implementations have been presented that explicitly aim at improving the efficiency and Scalability of multi-relational data mining approaches . With this article we attempt to present a structured overview .

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Section: Exploiting Particular Clause Structuresmentioning

confidence: 99%

Scalability and efficiency in multi-relational data mining

Blockeel

Sebag

2003

SIGKDD Explor. Newsl.

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“…Since inductive logic programming involves several computationally hard problems (e.g., Scheffer, Herbrich, & Wysotzki, 1996), we have focused on the transformation-based RELAGGS algorithm (Krogel & Wrobel, 2001). It follows pioneering ILP approaches to transformation-based learning (Lavrac, Džeroski, & Grobelnik, 1991) and succeeding work (Kramer, Lavrač, & Flach, 2001) and combines propositionalization with SQL aggregation functions (Krogel & Wrobel, 2001;Knobbe, de Haas, & Siebes, 2001).…”

Section: Related Results and Lessons Learnedmentioning

confidence: 99%

Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional Genomics

Krogel

Scheffer

2004

Machine Learning

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Abstract. We focus on the problem of predicting functional properties of the proteins corresponding to genes in the yeast genome. Our goal is to study the effectiveness of approaches that utilize all data sources that are available in this problem setting, including relational data, abstracts of research papers, and unlabeled data. We investigate a propositionalization approach which uses relational gene interaction data. We study the benefit of text classification and information extraction for utilizing a collection of scientific abstracts. We study transduction and co-training for using unlabeled data. We report on both, positive and negative results on the investigated approaches. The studied tasks are KDD Cup tasks of 2001 and 2002. The solutions which we describe achieved the highest score for task 2 in 2001, the fourth rank for task 3 in 2001, the highest score for one of the two subtasks and the third place for the overall task 2 in 2002.

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“…Stateless checking of MatchPE(S, R) in iSeM 1.0 is adopted from LARKS [4]: Preconditions and effects specified as SWRL rules are translated into PROLOG as in [14] and then used to compute the required logical implications by means of θ -subsumption checking stateless, that is without any instances (ABox), as given in [15]:…”

Section: Stateless Logical Specification Matchingmentioning

confidence: 99%