Induction as a Search Procedure

Konstantopoulos, Stasinos; Camacho, Rui; Fonseca, Nuno A.; Costa, Vítor Santos

doi:10.4018/9781599047058.ch007

Cited by 2 publications

(2 citation statements)

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“…ILP literature is rich in alternative specifications of this general task definition, each with its own strengths and weaknesses in terms of applicability to real problems and efficiency of the algorithms that approach it. We direct the interested reader elsewhere for fuller discussions of ILP and its various ILP settings [1] and focus on the most commonly used example setting under definite semantics: under definite semantics ILP both background and constructed hypotheses are definite Horn clauses and in the example setting of this semantics the observations are further restrcited to fully ground positive and negative examples.…”

Section: A Inductive Logic Programmingmentioning

confidence: 99%

Formulating description logic learning as an Inductive Logic Programming task

Konstantopoulos

Charalambidis

2010

International Conference on Fuzzy Systems

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Abstract-We describe an Inductive Logic Programming (ILP) approach to learning descriptions in Description Logics (DL) under uncertainty. The approach is based on implementing many-valued DL proofs as propositionalizations of the elementary DL constructs and then providing this implementation as background predicates for ILP. The proposed methodology is tested on a many-valued variation of eastbound-trains and Iris, two well known and studied Machine Learning datasets. I. INTRODUCTIONDescription logics (DL) are a family of logics that has found many applications in conceptual and semantic modelling, and is one of the key technologies behind semantic web applications.Fuzzy and, in general, many-valued extensions of DL semantics have given significant boost to their importance in both related fields: semantic conceptualization gains a means of expressing the uncertainty that is inherent in real-world modelling problems and uncertainty inference gains access to the vast conceptualization effort that has been carried out in the context of the semantic web.Despite, however, the rapid progress in inference methods for many-valued DL, there has been very limited success in applying machine learning methodologies to this family of logics, and especially to its more expressive members (such as those covering OWL and OWL 2) that are routinely used in web intelligence applications.In this paper we first introduce the machine learning discipline of Inductive Logic Programming and then discuss previous work on applying ILP to learning DL (Section II). These approaches tend to propose adaptation of ILP algorithms so that they cover DL, but are restricted to the less expressive members of the DL family. Instead, we investigate a novel approach whereby we re-formulate DL inference within the ILP paradigm, effectively mapping our problem to an equivalent problem within the domain of application of ILP (Section III). We evaluate our approach by applying an unadapted ILP system to a DL learning task under this mapping (Section IV), and close the paper by drawing conclusions and outlining future research directions (Section V).

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Section: A Inductive Logic Programmingmentioning

confidence: 99%

Formulating description logic learning as an Inductive Logic Programming task

Konstantopoulos

Charalambidis

2010

International Conference on Fuzzy Systems

Self Cite

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“…This also includes constructing theories that capture exceptional cases in datasets, where exceptions are a small minority of a dataset. A detailed account of ILP may be found in (Dzeroski and Lavrac 2001;Konstantopoulos et al 2008), for example.…”

Section: Machine Learningmentioning

confidence: 99%

Logic-based event recognition

Artikis

Skarlatidis

Portet

et al. 2012

The Knowledge Engineering Review

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Today's organisations require techniques for automated transformation of their large data volumes into operational knowledge. This requirement may be addressed by employing event recognition systems that detect events/activities of special significance within an organisation, given streams of 'low-level' information that is very difficult to be utilised by humans. Consider, for example, the recognition of attacks on nodes of a computer network given the TCP/IP messages, the recognition of suspicious trader behaviour given the transactions in a financial market, and the recognition of whale songs given a symbolic representation of whale sounds. Various event recognition systems have been proposed in the literature. Recognition systems with a logic-based representation of event structures, in particular, have been attracting considerable attention, because, among others, they exhibit a formal, declarative semantics, they have proven to be efficient and scalable, and they are supported by machine learning tools automating the construction and refinement of event structures. In this paper we review representative approaches of logic-based event recognition and discuss open research issues of this field. We illustrate the reviewed approaches with the use of a real-world case study: event recognition for city transport management.

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Induction as a Search Procedure

Cited by 2 publications

References 47 publications

Formulating description logic learning as an Inductive Logic Programming task

Formulating description logic learning as an Inductive Logic Programming task

Logic-based event recognition

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