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International Conference on Transparent Optical Networks

作者： Miroslaw Klinkowski Krzysztof Walkowiak Marek Jaworski Department of Transmission and Optical Technologies National Institute of Telecommunications Warszawa Poland Department of System and Computer Networks Wroclaw University of Technology Wroclaw Poland

ISBN: (纸本)9781457708817

Optical network architectures with elastic bandwidth provisioning are a very promising approach for next generation optical networks. Such elastic optical networks will enable efficient resource utilization and flexible sub-wavelength and super-channel connection provisioning to support heterogeneous and immense bandwidth demands. In this paper, we focus on the problem of Routing, Modulation Level, and Spectrum Allocation/Assignment (RMLSA) which emerges in such networks. We both formulate RMLSA as an Integer Linear programming (ILP) problem and propose an effective heuristic method called Adaptive Frequency Assignment - Division and Collision Avoidance (AFA-DCA) to be used if the solution of ILP is not attainable.

关键词： Elastic Optical Networks Network Optimization RMLSA Fiber optic networks Resource utilization inductive logic programming frequency assignment Network routing OFF LINE Spectra NETWORK STRUCTURE Bandwidth

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Discovering H-bonding rules in crystals with inductive logic programming

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MOLECULAR PHARMACEUTICS 2006年第6期3卷 665-674页

作者： Ando, Howard Y. Debaspe, Luc Luyten, Walter Van Craenenbroeck, Elke Vandecasteele, Henk Van Meervelt, Luc Ann Arbor Labs Pfizer Global Res & Dev Res Formulat Ann Arbor MI 48105 USA PharmaDM B-3001 Leuven Belgium Katholieke Univ Leuven Dept Chem B-3001 Leuven Belgium

In the domain of crystal engineering, various schemes have been proposed for the classification of hydrogen bonding (H-bonding) patterns observed in 3D crystal structures. In this study, the aim is to complement these schemes with rules that predict H-bonding in crystals from 2D structural information only. Modern computational power and the advances in inductive logic programming (ILP) can now provide computational chemistry with the opportunity for extracting structure-specific rules from large databases that can be incorporated into expert systems. ILP technology is here applied to H-bonding in crystals to develop a self-extracting expert system utilizing data in the Cambridge Structural Database of small molecule crystal structures. A clear increase in performance was observed when the ILP system DMAX was allowed to refer to the local structural environment of the possible H-bond donor/acceptor pairs. This ability distinguishes ILP from more traditional approaches that build rules on the basis of global molecular properties.

关键词： computer aided drug design in silico modeling crystal structure solubility hydrogen bonding machine learning inductive logic programming

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Qualitative knowledge driven approach to inductive logic programming

Qualitative knowledge driven approach to inductive logic pro...

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International Conference on Industrial and Information Systems

作者： Ranasinghe, D. D. M. Karunananda, A. S. Open Univ Sri Lanka Dept Math & Comp Sci Fac Nat Sci Nugegoda Sri Lanka Univ Moratuwa Fac Informat Technol Moratuwa Sri Lanka

ISBN: (纸本)9781424403219

Automating the learning of context specific robust rules from an evolving scene has been a research challenge in the area of cognitive vision systems. A research has been conducted to develop a system that learns context specific rules from an evolving scene by abstracting a model of human visual learning. Our system treats a set of symbolic data generated from an evolving real world scene and background knowledge as input to the system and inductive logic programming techniques are used to learn rules of the scene. The observed visual scene is represented in terms of qualitative spatial and temporal relations and these learnt relations are considered as input examples for inductive learning. A prototype has been developed for learning from various scenes of setting dinner tables. Currently the system is being tested to incorporate learning from different real world scenes thus improving the generalization power as well as combine more spatial and temporal representation and reasoning mechanisms to further enhance human like learning. This work can be adopted in automating a robot learning of object manipulation based on a visual scene.

关键词： cognitive systems inductive logic programming learning by example cognitive vision systems dinner table setting human visual learning qualitative knowledge robot learning visual scene

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Multi-class Mode of Action Classification of Toxic Compounds Using logic Based Kernel Methods

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MOLECULAR INFORMATICS 2010年第8-9期29卷 655-664页

作者： Lodhi, Huma Muggleton, Stephen Sternberg, Mike J. E. Brunel Univ Sch Informat Syst Comp & Math Uxbridge UB8 3PH Middx England Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England Univ London Imperial Coll Sci Technol & Med Div Mol Biosci Struct Bioinformat Grp London SW7 2AZ England

Toxicity prediction is essential for drug design and development of effective therapeutics. In this paper we present an in silico strategy, to identify the mode of action of toxic compounds, that is based on the use of a novel logic based kernel method. The technique uses support vector machines in conjunction with the kernels constructed from first order rules induced by an inductive logic programming system. It constructs multi-class models by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. In order to evaluate the effectiveness of the approach for chemoinformatics problems like predictive toxicology, we apply it to toxicity classification in aquatic systems. The method is used to identify and classify 442 compounds with respect to the mode of action. The experimental results show that the technique successfully classifies toxic compounds and can be useful in assessing environmental risks. Experimental comparison of the performance of the proposed multi-class scheme with the standard multi-class inductive logic programming algorithm and multi-class Support Vector Machine yields statistically significant results and demonstrates the potential power and benefits of the approach in identifying compounds of various toxic mechanisms.

关键词： Predictive toxicology Support vector machines inductive logic programming Multi-class classification Molecular modelling Drug design

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Concept learning in description logics using refinement operators

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MACHINE LEARNING 2010年第1-2期78卷 203-250页

作者： Lehmann, Jens Hitzler, Pascal Univ Leipzig Dept Comp Sci D-04103 Leipzig Germany Wright State Univ Kno E Sis Ctr Dayton OH 45435 USA

With the advent of the Semantic Web, description logics have become one of the most prominent paradigms for knowledge representation and reasoning. Progress in research and applications, however, is constrained by the lack of well-structured knowledge bases consisting of a sophisticated schema and instance data adhering to this schema. It is paramount that suitable automated methods for their acquisition, maintenance, and evolution will be developed. In this paper, we provide a learning algorithm based on refinement operators for the description logic ALCQ including support for concrete roles. We develop the algorithm from thorough theoretical foundations by identifying possible abstract property combinations which refinement operators for description logics can have. Using these investigations as a basis, we derive a practically useful complete and proper refinement operator. The operator is then cast into a learning algorithm and evaluated using our implementation DL-Learner. The results of the evaluation show that our approach is superior to other learning approaches on description logics, and is competitive with established ILP systems.

关键词： Description logics Refinement operators inductive logic programming Semantic web OWL Structured machine learning

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Fast learning of relational kernels

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MACHINE LEARNING 2010年第3期78卷 305-342页

作者： Landwehr, Niels Passerini, Andrea De Raedt, Luc Frasconi, Paolo Univ Potsdam Dept Comp Sci D-14482 Potsdam Germany Univ Trento Dipartimento Ingn & Sci Informaz I-38100 Povo TN Italy Katholieke Univ Leuven Dept Comp Wetenschappen B-3001 Heverlee Belgium Univ Florence Dipartimento Sistemi & Informat Machine Learning & Neural Networks Grp I-50139 Florence Italy

We develop a general theoretical framework for statistical logical learning with kernels based on dynamic propositionalization, where structure learning corresponds to inferring a suitable kernel on logical objects, and parameter learning corresponds to function learning in the resulting reproducing kernel Hilbert space. In particular, we study the case where structure learning is performed by a simple FOIL-like algorithm, and propose alternative scoring functions for guiding the search process. We present an empirical evaluation on several data sets in the single-task as well as in the multi-task setting.

关键词： Statistical relational learning inductive logic programming Kernel methods Dynamic propositionalization Kernel learning Multi-task learning

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DLP Learning from Uncertain Data

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Tsinghua Science and Technology 2010年第6期15卷 650-656页

作者：朱曼高志强漆桂林季秋 School of Computer Science and Engineering Southeast University

Description logic programs （DLP） are an expressive but tractable subset of OWL. This paper ana-lyzes the important under-researched problem of learning DLP from uncertain data. Current studies have rarely explored the plentiful uncertain data populating the semantic web. This algorithm handles uncertain data in an inductive logic programming framework by modifying the performance evaluation criteria. A pseudo-log-likelihood based measure is used to evaluate the performance of different literals under uncer-tainties. Experiments on two datasets demonstrate that the approach is able to automatically learn a rule-set from uncertain data with acceptable accuracy.

关键词： description logic programs inductive logic programming Markov logic networks

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Learning rules from multisource data for cardiac monitoring

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2010年第1-2期3卷 133-155页

作者： Fromont, Elisa Quiniou, Rene Cordier, Marie-Odile Univ St Etienne Univ Lyon UMR CNRS 5516 Lab Hubert Curien 18 Rue Prof Benoit Lauras F-42000 Saint Etienne France INRIA IRISA F-35042 Rennes France Univ Rennes 1 IRISA F-35042 Rennes France

This paper formalises the concept of learning symbolic rules from multisource data in a cardiac monitoring context. Our sources, electrocardiograms and arterial blood pressure measures, describe cardiac behaviours from different viewpoints. To learn interpretable rules, we use an inductive logic programming (ILP) method. We develop an original strategy to cope with the dimensionality issues caused by using this ILP technique on a rich multisource language. The results show that our method greatly improves the feasibility and the efficiency of the process while staying accurate. They also confirm the benefits of using multiple sources to improve the diagnosis of cardiac arrhythmias.

关键词： multisource data ILP inductive logic programming declarative bias cardiac arrhythmias

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Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation

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LANGUAGE RESOURCES AND EVALUATION 2010年第4期44卷 295-313页

作者： Specia, Lucia Stevenson, Mark Volpe Nunes, Maria das Gracas Univ Sheffield Dept Comp Sci Regent Court 211 Portobello Sheffield S1 4DP S Yorkshire England Wolverhampton Univ Res Inst Informat & Language Proc Wolverhampton WV1 1SB England Univ Sao Paulo BR-13560970 Sao Carlos SP Brazil

Corpus-based techniques have proved to be very beneficial in the development of efficient and accurate approaches to word sense disambiguation (WSD) despite the fact that they generally represent relatively shallow knowledge. It has always been thought, however, that WSD could also benefit from deeper knowledge sources. We describe a novel approach to WSD using inductive logic programming to learn theories from first-order logic representations that allows corpus-based evidence to be combined with any kind of background knowledge. This approach has been shown to be effective over several disambiguation tasks using a combination of deep and shallow knowledge sources. Is it important to understand the contribution of the various knowledge sources used in such a system. This paper investigates the contribution of nine knowledge sources to the performance of the disambiguation models produced for the SemEval-2007 English lexical sample task. The outcome of this analysis will assist future work on WSD in concentrating on the most useful knowledge sources.

关键词： Word sense disambiguation Knowledge sources inductive logic programming

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Implementing Pattern Mining Using Extended Attribute Expression on Relational DB

Implementing Pattern Mining Using Extended Attribute Express...

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3rd International Conference on Knowledge Discovery and Data Mining

作者： Makino, Toshiyuki Inuzuka, Nobuhiro Nagoya Inst Technol Showa Ku Nagoya Aichi 4668555 Japan

ISBN: (纸本)9780769539232

Multi-relational data mining (MRDM) is to enumerate frequently appeared patterns in data, the patterns which are appeared not only in a relational table but over a collection of tables. Although a database usually consists of many relational tables, most of data mining approaches treat patterns only on a table. An approach based on ILP (inductive logic programming) is a promising approach and it treats patterns on many tables. Pattern miners based on the ILP approach produce expressive patterns and are wide-applicative but computationally expensive. MAPIX[2] has an advantage that it constructs patterns by combining atomic properties extracted from sampled examples. By restricting patterns into combinations of the atomic properties it gained efficiency compared with other algorithms. In order to scale MAPIX to treat large dataset on standard relational database systems, this paper studies implementation issues.

关键词： Multi-relational data mining inductive logic programming Relational database

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