The related theories of Web and Web mining in semantic are concluded and analyzed in this *** description on RDF resource is analyzed on the semantic step,and the clustering method for RDFMS data clustering based on S...
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The related theories of Web and Web mining in semantic are concluded and analyzed in this *** description on RDF resource is analyzed on the semantic step,and the clustering method for RDFMS data clustering based on Semantic distance is proposed,with the detailed description for the algorithm and *** the discussion on the data mining techniques for semantic Web mining,the design on inductive logic programming is proposed for the data mining techniques that are suitable to the semantic ***,how to apply to the description of the algorithm is given through the specific examples to verify the feasibility for the data mining in the semantic Web environment.
This paper explores two different methods of learning dialectal morphology from a small parallel corpus of standard and dialect-form text, given that a computational description of the standard morphology is available...
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ISBN:
(纸本)9781618392473
This paper explores two different methods of learning dialectal morphology from a small parallel corpus of standard and dialect-form text, given that a computational description of the standard morphology is available. The goal is to produce a model that translates individual lexical dialectal items to their standard dialect counterparts in order to facilitate dialectal use of available NLP tools that only assume standard-form input. The results show that a learning method based on inductive logic programming quickly converges to the correct model with respect to many phonological and morphological differences that are regular in nature.
Statistical relational learning (SRL) addresses one of the central open questions of AI: the combination of relational or first-order logic with principled probabilistic and statistical approaches to inference and lea...
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Statistical relational learning (SRL) addresses one of the central open questions of AI: the combination of relational or first-order logic with principled probabilistic and statistical approaches to inference and learning. This thesis approaches SRL from an inductive logic programming (ILP) perspective and starts with developing a general framework for SRL: probabilistic ILP. Based on this foundation, the thesis shows how to incorporate the logical concepts of objects and relations among these objects into Bayesian networks. As time and actions are not just other relations, it afterwards develops approaches to probabilistic ILP over time and for making complex decision in relational domains. Finally, it is shown that SRL approaches naturally yield kernels for structured data. The resulting approaches are illustrated using examples from genetics, bioinformatics, and planning domains.
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 flexib...
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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.
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...
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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.
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 cont...
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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.
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 o...
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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.
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...
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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.
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, a...
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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.
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 t...
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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.
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