39282722
Parallel Multilevel Sparse Approximate Inverse Preconditioners in Large Sparse Matrix Computations
2595116
A Class of New Parallel Preconditioning Strategies for Solving Large Sparse Linear Systems
26247223
Combining Description Logic and Defeasible Logic for the Semantic Web
The importance of integrating rules and ontologies for the Semantic Web has been well addressed by many researchers. Defeasible Logic is a simple but efficient nonmonotonic language which can handle both defeasibility and priority. In this paper we propose a novel approach to combining Defeasible Logic with Description Logics by introducing the Description Defeasible Logic (DDL). DDL is similar to Defeasible Logic but it also contains queries to the Description Logic knowledge base. DDL allows nonmonotonic reasoning to be built on top of ontologies, and to a certain degree, allows ontologies to be built on top of nonmonotonic reasoning. We give some basic properties of DDL, one of which shows that DDL is a tractable language provided that the underlying Description Logic is tractable.
6776536
Forgetting and Conflict Resolving in Disjunctive Logic Programming
We establish a declarative theory of forgetting for disjunctive logic programs. The suitability of this theory is justified by a number of desirable properties. In particular, one of our results shows that our notion of forgetting is completely captured by the classical forgetting. A transformation-based algorithm is also developed for computing the result of forgetting. We also provide an analysis of computational complexity. As an application of our approach, a fairly general framework for resolving conflicts in inconsistent knowledge bases represented by disjunctive logic programs is defined. The basic idea of our framework is to weaken the preferences of each agent by forgetting certain knowledge that causes inconsistency. In particular, we show how to use the notion of forgetting to provide an elegant solution for preference elicitation in disjunctive logic programming.
14032548
Profile-based string kernels for remote homology detection and motif extraction
We introduce novel profile-based string kernels for use with support vector machines (SVMs) for the problems of protein classification and remote homology detection. These kernels use probabilistic profiles, such as those produced by the PSI-BLAST algorithm, to define position-dependent mutation neighborhoods along protein sequences for inexact matching of k-length subsequences ("k-mers") in the data. By use of an efficient data structure, the kernels are fast to compute once the profiles have been obtained. For example, the time needed to run PSI-BLAST in order to build the profiles is significantly longer than both the kernel computation time and the SVM training time. We present remote homology detection experiments based on the SCOP database where we show that profile-based string kernels used with SVM classifiers strongly outperform all recently presented supervised SVM methods. We also show how we can use the learned SVM classifier to extract "discriminative sequence motifs" - short regions of the original profile that contribute almost all the weight of the SVM classification score - and show that these discriminative motifs correspond to meaningful structural features in the protein data. The use of PSI-BLAST profiles can be seen as a semi-supervised learning technique, since PSI-BLAST leverages unlabeled data from a large sequence database to build more informative profiles. Recently presented "cluster kernels " give general semi-supervised methods for improving SVM protein classification performance. We show that our profile kernel results are comparable to cluster kernels while providing much better scalability to large datasets.
437107
Template-based privacy preservation in classification problems
In this paper, we present a template-based privacy preservation to protect against the threats caused by data mining abilities. The problem has dual goals: preserve the information for a wanted classification analysis and limit the usefulness of unwanted sensitive inferences that may be derived from the data. Sensitive inferences are specified by a set of "privacy templates". Each template specifies the sensitive information to be protected, a set of identifying attributes, and the maximum association between the two. We show that suppressing the domain values is an effective way to eliminate sensitive inferences. For a large data set, finding an optimal suppression is hard, since it requires optimization over all suppressions. We present an approximate but scalable solution. We demonstrate the effectiveness of this approach on real life data sets.
15783530
A Top-Down Procedure for Disjunctive Well-Founded Semantics
Skepticism is one of the most important semantic intuitions in artificial intelligence. The semantics formalizing skeptical reasoning in (disjunctive) logic programming is usually named well-founded semantics. However, the issue of defining and computing the well-founded semantics for disjunctive programs and databases has proved to be far more complex and difficult than for normal logic programs. The argumentation-based semantics WFDS is among the most promising proposals that attempts to define a natural well-founded semantics for disjunctive programs. In this paper, we propose a top-down procedure for WFDS called D-SLS Resolution, which naturally extends the Global SLS-resolution and SLI-resolution. We prove that D-SLS Resolution is sound and complete with respect to WFDS. This result in turn provides a further yet more powerful argument in favor of the WFDS.