384033
Forgetting in Managing Rules and Ontologies
The language of HEX-programs under the answer-set semantics is designed for interoperating with heterogeneous sources via external atoms and for meta-reasoning via higher-order literals in the context of the semantic Web. As an important technique in managing knowledge bases, the notion of forgetting has received increasing interest in the knowledge-representation area. In this paper, we introduce a semantics-based theory of forgetting for HEX-programs and, in turn, for a class of OWL/RDF(S) ontologies which allows to fully employ semantic information in managing ontologies like editing, merging, aligning, and redundancy removal
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.
14817828
Argumentation-based abduction in disjunctive logic programming
Abstract In this paper, we propose an argumentation-based semantic framework, called DAS, for disjunctive logic programming. The basic idea is to translate a disjunctive logic program into an argumentation-theoretic framework. One unique feature of our proposed framework is to consider the disjunctions of negative literals as possible assumptions so as to represent incomplete information. In our framework, three semantics preferred disjunctive hypothesis (PDH), complete disjunctive hypothesis (CDH) and well-founded disjunctive hypothesis (WFDH) are defined by three kinds of acceptable hypotheses to represent credulous, moderate and skeptical reasoning in artificial intelligence (AI), respectively. Furthermore, our semantic framework can be extended to a wider class than that of disjunctive programs (called bi-disjunctive logic programs). In addition to being a first serious attempt in establishing an argumentation-theoretic framework for disjunctive logic programming, DAS integrates and naturally extends many key semantics, such as the minimal models, extended generalized closed world assumption (EGCWA), the well-founded model, and the disjunctive stable models. In particular, novel and interesting argumentation-theoretic characterizations of the EGCWA and the disjunctive stable semantics are shown. Thus the framework presented in this paper does not only provide a new way of performing argumentation (abduction) in disjunctive deductive databases, but also is a simple, intuitive and unifying semantic framework for disjunctive logic programming.
5228937
Merging and Aligning Ontologies in dl-Programs
The language of dl-programs is a latest effort in developing an expressive representation for Web-based ontologies. It allows to build answer set programming (ASP) on top of description logic and thus some attractive features of ASP can be employed in the design of the Semantic Web architecture. In this paper we first generalize dl-programs by allowing multiple knowledge bases and then accordingly, define the answer set semantics for the dl-programs. A novel technique called forgetting is developed in the setting of dl-programs and applied to ontology merging and aligning.
1885288
A Theory of Forgetting in Logic Programming
The study of forgetting for reasoning has attracted considerable attention in AI. However, much of the work on forgetting, and other related approaches such as independence, irrelevance and novelty, has been restricted to the classical logics. This paper describes a detailed theoretical investigation of the notion of forgetting in the context of logic programming. We first provide a semantic definition of forgetting under the answer sets for extended logic programs. We then discuss the desirable properties and some motivating examples. An important result of this study is an algorithm for computing the result of forgetting in a logic program. Furthermore, we present a modified version of the algorithm and show that the time complexity of the new algorithm is polynomial with respect to the size of the given logic program if the size of certain rules is fixed. We show how the proposed theory of forgetting can be used to characterize the logic program updates.
2316989
Observation-based Model for BDI-Agents
We present a new computational model of BDI-agents, called the observation-based BDI-model. The key point of this BDI-model is to express agents' beliefs, desires and intentions as a set of runs (computing paths), which is exactly a system in the interpreted system model, a well-known agent model due to Halpern and his colleagues. Our BDI-model is computationally grounded in that we are able to associate the BDI-agent model with a computer program, and formulas, involving agents' beliefs, desires (goals) and intentions, can be understood as properties of program computations. We present a sound and complete proof system with respect to our BDI-model and explore how symbolic model checking techniques can be applied to model checking BDI-agents. In order to make our BDI-model more flexible and practically realistic, we generalize it so that agents can have multiple sources of beliefs, goals and intentions.
36512646
A Peer-to-Peer Communication System
The demand of communicating anywhere, anytime on any device is growing with the development of modern information technology. Current instant messaging services enable people to be aware of each other's presence and to exchange text information. However, almost all of these services are based on client/server architecture. If servers crash, all connections are lost. As an alternative, peer-to-peer architecture basically relies on clients, but existing systems usually emphasize sharing of computing resources and files. In this paper, we propose a peer-to-peer communication system, which combines the advantages of instant messaging services and peer-to-peer networking. In the system, in addition to being aware of each other's presence, users can simultaneously attend multiple multimedia meetings with each meeting allowing multiple attendees. Users have full control over the exchange of real-time text/voice/video information in these meetings.
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.
2595116
A Class of New Parallel Preconditioning Strategies for Solving Large Sparse Linear Systems
304685
Anonymizing sequential releases
An organization makes a new release as new information become available, releases a tailored view for each data request, releases sensitive information and identifying information separately. The availability of related releases sharpens the identification of individuals by a global quasi-identifier consisting of attributes from related releases. Since it is not an option to anonymize previously released data, the current release must be anonymized to ensure that a global quasi-identifier is not effective for identification. In this paper, we study the sequential anonymization problem under this assumption. A key question is how to anonymize the current release so that it cannot be linked to previous releases yet remains useful for its own release purpose. We introduce the lossy join, a negative property in relational database design, as a way to hide the join relationship among releases, and propose a scalable and practical solution.
52857819
QUICK: graphical user interface to multiple databases
Formulating queries to access multiple databases can be a formidable task, especially when many terms from various databases and complex constraints are involved. To specify a multidatabase query, the user usually has to search through documents for exact database terms and learn the multidatabase language. The report presents QUICK (QUery Interface to CPL-Kleisli), a graphical user interface to multiple databases, CPL is a high-level multidatabase language built on top of an open query system Kleisli. QUICK allows users to handle overwhelming information from different data sources in an intuitive and uniform manner. The query specification is reduced to specifying user's terms in his/her own world, selecting paths and specifying constraints in a graph. QUICK is able to automatically generate a CPL query that corresponds to the user's intent.