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Attribute exploration in FCA is proposed by Baader etc. in the past decade and it is an effective tool applying to description logics to construct ontology on Semantic Web. The authors firstly introduce attribute exploration algorithm, then investigate different cases in which the redundant computation may occur. As new results, the improved attribute exploration algorithm is proposed in terms of relevancy. We also give the proof of the completeness of the improved algorithm, and show how the proposed algorithm avoids redundancy and simplifies computation in some certain cases. We finally present the method to construct an ontology based on attribute exploration algorithm(AEOCM) on the open formal context, and specify the implement-tation procedure of this method in terms of instantiation. [ABSTRACT FROM AUTHOR]

In this paper, the intelligent-based algorithm called Particle Swarm Optimization (PSO) which can be implemented by any programming languages with few parameters to adjust is employed to design a GEO (Geostationary Earth Orbit) satellites tracking system of the moving ground station. The system can be applied to a multimedia communication satellite anywhere and used only for Line-of-Sight communication with satellites. Using the information from a global positioning system, gyroscope and integrated receiver decoder (GEO satellite signal strength) to calculate the errors of change by the step-tracking method and particle swarm optimization algorithm, then the elevation angle and the azimuth angle of the target will be tracked efficiently. [ABSTRACT FROM AUTHOR]

Scalar multiplication dP and gP+hQ are important in encryption, decryption and signature in information security and wireless network. The speed of computation of scalar multiplication is significant for related applications. In this paper, a new signed binary representation (SBR) for integers called complementary code method (CC) is proposed, which has minimum weight and needs less memory. An efficient algorithm using CC method for computing dP is shown also. According to analyzing and comparing to the other methods, this algorithm is the better one in window methods and is the simplest for applying in software and hardware. By applying joint representation in computing gP+hQ, new algorithm using CC method has the least joint weight compared to other methods mentioned in this paper. So, the new SBR can efficiently speed up the computation of scalar multiplication dP and gP+hQ and can be widely used in secure communication for improving the speed of encryption and signature. [ABSTRACT FROM AUTHOR]

Rough set theory has been considered as a useful tool to model the vagueness, imprecision, and uncertainty, and has been applied successfully in many fields. Knowledge reduction is one of the most important problems in rough set theory. However, in real-world most of information systems are based on dominance relations in stead of the classical rough set because of various factors. To acquire brief decision rules from systems based on dominance relations, knowledge reductions are needed. The main aim of this paper is to study the problem. The basic concepts and properties of knowledge reduction based on evidence reasoning theory are discussed. Furthermore, the characterization and knowledge reduction approaches based on evidence reasoning theory are obtained with examples in several kinds of ordered information system, which is every useful in future research works of the ordered information systems. [ABSTRACT FROM AUTHOR]

Ontology mapping is the key point to reach interoperability over ontologies. It can identify the elements corresponding to each other. With the rapid development of ontology applications, domain ontologies became very large in scale. Dealing with the large scale ontology mapping problems is beyond the reach of the existing algorithms. To improve this situation a modularization-oriented approach (called MOM) was proposed in this paper. This approach tries to decompose a large mapping problem into several smaller ones and use a method to reduce the complexity dramatically. Several large and complex ontologies have been chosen and tested to verify this approach. Experimental results indicate that the MOM method can significantly reduce the time cost while keeping the high mapping accuracy. [ABSTRACT FROM AUTHOR]

Due to the complexity and flexibility of natural language, linguistic knowledge representation, automatic acquisition and its application research becomes difficult. In this paper, a combination of ontology with statistical method is presented for linguistic knowledge representation and acquisition from training data. In this study, linguistic knowledge representaiton is firstly defined using ontology theory, and then, linguistical knowledge is automatically acquired by statistical method. In document processing, the semantic evaluation value of the document can be get by linguistic knowledge. The experimention in Chinese information retrieval and text classification shows the proposed method improves the precision of nature language processing. [ABSTRACT FROM AUTHOR]

In 2001, NKIMath, as the mathematics knowledge component of National Knowledge Infrastructure, was initiated to elaborate in China. In order to help knowledge engineers acquire and manage the mathematical knowledge especially the conceptual knowledge, a knowledge management environment, NKIMathE has been designed and developed. NKIMathE integrates three main components: (1) a platform for knowledge acquisition, syntax checking and organization for mathematical concepts; (2) a module for multi-lingual knowledge translation and transform for mathematical concepts; and (3) a Web-based and a mobile knowledge Q-A platforms for mathematical concepts. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to capture the notion of usefulness in propositional language. We believe that classical implication captures a certain kind of usefulness, and name it strict usefulness. We say that a formula P is strictly useful to a formula Q under a formula set Γif and only if P implies Q under Γ in classical propositional logic. We also believe that classical implication is too strict to capture the whole notion of usefulness. Therefore, we extend it in two ways. The first one is partial usefulness, which means that if P is true, then Q will be partially true under the background of Γ. The second one is probabilistic usefulness, which means that the probability of Q is true will increase by given P is true under Γ. This paper provides semantic definitions of them respectively in propositional language, and discusses the fundamental properties of them. Keywords: Knowledge representation, usefulness, partial implication, probabilistic relevance. [ABSTRACT FROM AUTHOR]

The role of ontologies is to provide a well-defined structure of domain knowledge that acts as the heart of any system of knowledge representation on that domain for the purposes of reasoning, knowledge sharing, and integration. Thus, it is essential to clarify the structure of knowledge in ontologies. In this paper, we discuss how ontology developers can define the identity conditions of classes explicitly, and can utilize them to develop structured taxonomies with adequate consistency. The background of this paper is OntoClean which is a domain independent methodology for ontology modeling using some philosophical notions. We exemplify the classification of sorts with necessary conceptual constraints. Then, we provide an explicit, simplified, and practical ontological analysis system regarding our subsumption constraints. [ABSTRACT FROM AUTHOR]

With the popularization of the Internet, virtual communities offer a new way for knowledge exchange. Previous research focused on the individuals' motivation to knowledge contribution. However, the exchange of knowledge is facilitated not only when individuals are motivated but also when individuals have the ability to engage in it. This study examines the influence of capability to the knowledge contribution in the virtual community as compared to individual motivation. An online questionnaire survey and partial least squares (PLS) were used to analyze and verify the proposed hypotheses. The results indicated that perceived self-efficacy and professional experience positively influence knowledge contribution in the online virtual community. However, individual motivations, which often are regarded as important influential factors in the real world, did not significantly influence knowledge contribution in the online virtual community. [ABSTRACT FROM AUTHOR]

Through the interrelated concept of the job shop production, this paper constructs a dynamic scheduling decision system based on knowledge, and gives five attributes of resource agent and corresponding task, time, cost, quality, load and priority. Using the fuzzy set and rough set, the classified knowledge of the attribute is generated, and is used as the states criteria in the Q-learning. To initialize Q value of the decision attribute, we collect the knowledge from experts. The Q-learning algorithm and initial parameter values are presented in knowledge based scheduling decision model. By the algorithmic analysis, we demonstrate its convergence and credibility. Applying this algorithm, the system will update the knowledge itself continuously, and it will be more intelligent in the changeful environment, also it will avoid the subjectivity and invariance of the expert knowledge. [ABSTRACT FROM AUTHOR]

A Multi-Feature Cube (MF-Cube) query is a complex-data-mining query based on data cubes, which computes the dependent complex aggregates at multiple granularities. Existing computations designed for simple data cube queries can be used to compute distributive and algebraic MF-Cubes queries. In this paper we propose an efficient computation of holistic MF-Cubes queries. This method computes holistic MF-Cubes with PDAP (Part Distributive Aggregate Property). The efficiency is gained by using dynamic subset data selection strategy (Iceberg query technique) to reduce the size of materialized data cube. Also for efficiency, this approach adopts the chunk-based caching technique to reuse the output of previous queries. We experimentally evaluate our algorithm using synthetic and real-world datasets, and demonstrate that our approach delivers up to about twice the performance of traditional computations. [ABSTRACT FROM AUTHOR]

Keyword Search Over Relational Databases(KSORD) has been widely studied. While keyword search is helpful to access databases, it has inherent limitations. Keyword search doesn't exploit the semantic relationships between keywords such as hyponymy, meronymy and antonymy, so the recall rate and precision rate are often dissatisfactory. In this paper, we have designed an ontology-based semantic search engine over databases called Si-SEEKER based on our i-SEEKER system which is a KSORD system with our candidate network selection techniques. Si-SEEKER extends i-SEEKER with semantic search by exploiting hierarchical structure of domain ontology and a generalized vector space model to compute semantic similarity between a user query and annotated data. We combine semantic search with keyword search over databases to improve the recall rate and precision rate of the KSORD system. We experimentally evaluate our Si-SEEKER system on the DBLP data set and show that Si-SEEKER is more effective than i-SEEKER in terms of the recall rate and precision rate of retrieval results. [ABSTRACT FROM AUTHOR]

This paper proposes the web information extraction system that extracts the pre-defined information automatically from web documents (i.e. HTML documents) and integrates the extracted information. The system recognizes entities without labels by the probabilistic based entity recognition method and extends the existing domain knowledge semiautomatically by using the extracted data. Moreover, the system extracts the sub-linked information linked to the basic page and integrates the similar results extracted from heterogeneous sources. The experimental result shows that the global precision of seven domain sites is 93.5%. The system using the sub-linked information and the probabilistic based entity recognition enhances the precision significantly against the system using only the domain knowledge. Moreover, the presented system can extract the more various information precisely due to applying the system with flexibility according to domains. Thus, the system can increase the degree of user satisfaction at its maximum and contribute the revitalization of e-business. [ABSTRACT FROM AUTHOR]

Knowledge transfer as an important aspect of knowledge management has been considered as an effective way to promote the knowledge ability and the core competence of an organization. In this paper, a method to evaluate knowledge transfer ability in organization is proposed. Firstly, the main factors which affect the knowledge transfer ability to be found out through the analysis of the relevant research of domestic and international knowledge transfer, then, an index system is set up to evaluate knowledge transfer ability using the method of questionnaire investigation and statistical analysis as knowledge transmission ability, knowledge receptive ability, interactive ability and organizational supporting ability, etc.. According to the index system and the characteristics of linguistic assessment information provided by experts, a multi-index linguistic decision-making method based on linguistic assessment information is proposed using LWD operator and LOWA operator developed in recent years. Finally, an example is given to explain the method. [ABSTRACT FROM AUTHOR]

This paper proposes and tests a new model that helps explain knowledge contribution in virtual communities. Grounded on a communication-based view, we examined key drivers of user intention to share knowledge in virtual communities from three aspects: the knowledge to be shared, the individual self and the environment. In particular, a self-concept-based motivation model was employed to investigate individuals' motivational factors. An empirical study of 363 virtual community users demonstrated the salient and dominant influences of enhanced knowledge self-efficacy and self-image on knowledge contribution intention. Enjoyment in helping others, trust and system usability were also found to be important motivations for knowledge sharing. Implications for both researchers and practitioners are discussed. [ABSTRACT FROM AUTHOR]

Discretization is an important preprocessing technique in data mining tasks. Univariate Discretization is the most commonly used method. It discretizes only one single attribute of a dataset at a time, without considering the interaction information with other attributes. Since it is multi-attribute rather than one single attribute determines the targeted class attribute, the result of Univariate Discretization is not optimal. In this paper, a new Multivariate Discretization algorithm is proposed. It uses ICA (Independent Component Analysis) to transform the original attributes into an independent attribute space, and then apply Univariate Discretization to each attribute in the new space. Data mining tasks can be conducted in the new discretized dataset with independent attributes. The numerical experiment results show that our method improves the discretization performance, especially for the nongaussian datasets, and it is competent compared to PCA-based multivariate method. Keywords: Data mining, Multivariate Discretization, Independent Component Analysis, Nongaussian. [ABSTRACT FROM AUTHOR]

A maximum distribution reduction is meant to preserve not only all deterministic information with respect to decision attributes but also the largest possible decision class for each object of an inconsistent decision table. Hence, it is useful to compute this type of reduction when mining decision tables with data inconsistency. This paper presents a novel algorithm for finding a maximum distribution reduct of an inconsistent decision table. Two functions of attribute sets are introduced to characterize a maximum distribution reduct in a new and simple way and then used as a heuristic in the algorithm to search for a reduction. Complexity analysis of the algorithm is also presented. As an application example, the presented algorithm was applied to mine a real surgery database and some interesting results were obtained. [ABSTRACT FROM AUTHOR]

Workflow resource model describes all kinds of resources that support the execution of workflows. The meta-model for workflow resource model presents the constituents of workflow resource model. It is one of the three correlative sub-meta-models for workflow model. Based on the analysis of existed studies and real cases, an extended meta-model for workflow resource model was introduced by extending and modifying the meta-model for organizational model proposed by WfMC. The detail of entities and their relationships were described. The relationships between workflow resource model and process model were discussed. XML was used to describe the meta-model. In the end, a conclusion and proposals for future research directions were presented. [ABSTRACT FROM AUTHOR]

We present a new model of knowledge, belief, desire and intention, called the interpreted KBDI-system model (or KBDI-model for short). The key point of the interpreted KBDI-system model is that we express an agent's knowledge, belief, desire and intention 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 KBDI-model is computationally grounded in that we are able to associate a KBDI-model with a computer program, and formulas, involving agents' knowledge, belief, desire (goal) and intention, can be understood as properties of program computations. With KBDI-model, we have two different semantics to interpret our logic of knowledge, belief, desire and intention. Moreover, with respect to each semantics, we present a sound and complete proof system. [ABSTRACT FROM AUTHOR]

Methods based on resolution have been widely used for theorem proving since it was proposed. The extension rule (ER) method is a new method for theorem proving, which is potentially a complementary method to resolution-based methods. But the first-order ER approach is incomplete and not realized. This paper gives a complete first-order ER algorithm and describes the implementation of a theorem prover based on it and its application to solving some planning problems. We also report the preliminary computational results on first-order formulation of planning problems. [ABSTRACT FROM AUTHOR]

The traditional rough set theory is a powerful tool to deal with complete information system, and its performance to process incomplete information system is weak, M.Kryszkiewcz has put forward the tolerance relation to handle the problem. however,the method may not be perfect on account of excessively many intersectional elements between classifications. This paper improves the tolerance relation proposed by M.Kryszkiewcz to obtain the γ-tolerance relation and γ-tolerance classes, presents rough set model for incomplete information system based on the γ-tolerance relation. The method of γ-tolerance relation is proved to be more superior to that of M.Kryszkiewcz's tolerance relation. Finally, the conception of γ-attributes reduction is defined, and the algorithm of γ-attribute reduction is provided. Keywords: Rough set, Tolerance relation, γ-tolerance relation, incomplete information system, γ-attribute reduction. [ABSTRACT FROM AUTHOR]

This paper proposes constructing an environment ontology to represent domain knowledge about Web services. The capability of a Web service is considered in terms of the effects it imposes on the environment during execution. Thus, more fundamental and precise semantic specification for service capability than conventional interface-based description language can be obtained. Basic concepts of the ontology include resources residing in the environment. For each environment resource, there is a corresponding hierarchical state machine specifying its dynamic characteristics. Thus, the influence of a machine on its environments can be modelled with the state machines of the environment resources. Rules and algorithms to construct an environment ontology on the basis of generic domain ontology are introduced. And then guidelines for specifying Web service capability semantically based on the constructed environment ontology are given. [ABSTRACT FROM AUTHOR]

In an open and dynamic environment, due to the changes in the application's domain or the user's requirements, the domain knowledge changes over time and ontology evolves continually. Pi-calculus is a kind of mobile process algebra which can be used for modeling concurrent and dynamic systems. Based on the pi-calculus, this paper proposes a kind of ontology process model used for solving the change implementation and propagation problems in ontology evolution process. This solution is discussed at three levels: the change implementation of single ontology evolution, the push-based synchronization realization for the change propagation in the evolution of multiple dependent ontologies within a single node, and the pull-based synchronization realization for the change propagation of the distributed ontologies evolution. [ABSTRACT FROM AUTHOR]

Words and n-grams are commonly used Chinese text representing units and are proved to be good features for Chinese Text Categorization and Information Retrieval. But the effectiveness of applying these representing units for Chinese Text Clustering is still uncovered. This paper is a comparative study of representing units in Chinese Text Clustering. With K-means algorithm, several representing units were evaluated including Chinese character N-gram features, word features and their combinations. We found Chinese word features, Chinese character unigram features and bi-gram features most effective in our experiments. The combination of features didn't improve the results. Detailed experimental results on several public Chinese Text Categorization datasets are provided in the paper. Keywords: Chinese text Clustering; N-gram feature; Bi-gram feature; Word feature. [ABSTRACT FROM AUTHOR]

When dealing with a cause, cases involving some effect due to that cause are precious as such cases contribute to what the cause is. They must be reasoned upon if inference about causes is to take place. It thus seems like a good logic for causes would arise from a semantics based on collections of cases, to be called configurations, that gather instances of a given cause yielding some effect(s). Two crucial features of this analysis of causation are transitivity, which is endorsed here, and the event-based formulation, which is given up here in favor of a fact-based approach. A reason is that the logic proposed is ultimately meant to deal with both deduction (given a cause, what is to hold?) and abduction (given the facts, what could be the cause?) thus paving the way to the inference of explanations. The logic developed is shown to enjoy many desirable traits. These traits form a basic kernel which can be modified but which cannot be extended significantly without losing the adequacy with the nature of causation rules. [ABSTRACT FROM AUTHOR]

With the knowledge civilization development, the creation of knowledge and technology attracts an increasing interest in scientific research and practice. Universities and research institutes play a vital role in creating and transmitting scientific knowledge. Thus, enhancing the scientific knowledge creation in academia is a significant issue. In the paper, we investigate what aspects of knowledge creation processes in academic research we should support in particular. A questionnaire-based survey was conducted in a Japanese research institute (JAIST). By using a multiple criteria formulation and reference point method, we extract useful information and knowledge from the data base of survey results. Most critical and important problems are discovered by the negative and positive evaluations with respect to the conditions of scientific creativity. The results of the investigation give also valuable information for research and development management in universities and research organizations. Keywords: Scientific knowledge creation, questionnaire-based survey, creativity support. [ABSTRACT FROM AUTHOR]

XML document may contain inconsistencies that violate predefined integrity constraints, and there are two basic concepts for this problem: Repair is the data consistent with the integrity constraints, and also minimally differs from the original one. Consistent data is the data common for every possible repair. In this paper, first we give a general constraint model for XML, which can express functional dependencies, keys and multivalued dependencies. Next we provide a repair framework for inconsistent XML document with three basic update operations: node insertion, node deletion and value modification. Following this approach, we introduce the concept of repair for inconsistent XML document, discuss the chase process to generate repairs, and prove some important properties of the chase process. Finally we give a method to obtain the greatest lower bound of all possible repairs, which is sufficient for consistent data. [ABSTRACT FROM AUTHOR]

This study develops a framework of conceptual model to manage learning paths in e-learning systems. Since learning objects are rapidly accumulated in e-learning course repositories, managing the relevant relations among learning objects are costly and error-prone. Moreover, conventional learning path management based on databases or XML metadata does not offer a sufficient conceptual model to represent semantics. This study utilizes ontology-based techniques to strengthen learning path management in a knowledgeable manner. Through establishing a conceptual model of learning paths, semantic modeling provides richer data structuring capabilities for organizing learning objects. Empirical findings are presented, which show technologies to enhance completeness of semantic representation and reduce the complexity of the path management efforts. A walkthrough example is given to present ontology building, knowledge inference and the planning of learning paths. Keywords: Ontology, Semantic, Conceptual structure, e-Learning. [ABSTRACT FROM AUTHOR]

Intelligent tutoring systems have being extensively researched, and are viewed as cost-effective alternatives to traditional education. However, it has been long recognized that development of such systems is labor-intensive and time-consuming, and that a certain degree of automation in the development process is necessary. This paper proposes a framework for automating test generation - one of the key components in an intelligent tutoring system. The core of the framework is a domain conceptual model, a collection of testing goals, and a collection of test-generation rules, and the latter two are formulated from an analysis of various modes of error and on the basis of the domain conceptual model. Keywords: Intelligent tutoring system, test generation, domain conceptual model, testing goal, test-generation rules, individualized testing. [ABSTRACT FROM AUTHOR]

We consider the problem of enumerating minimal explanations in propositional theory. We propose a new way of characterizing the enumeration problem in terms of not only the number of explanations, but also the number of unexplanations. Maximal unexplanations are a maximal set of abducible formulas which cannot explain the observation given a background theory. In this paper, we interleavingly enumerate not only minimal explanations but also maximal unexplanations. To best of our knowledge, there has been no algorithm which is characterized in terms of such maximal unexplanations. We propose two algorithms to perform this task and also analyze them in terms of query complexity, space complexity and time complexity. [ABSTRACT FROM AUTHOR]

Current merging methods for stratified knowledge bases are often based on the commensurability assumption, i.e. all knowledge bases share a common scale. However, this assumption is too strong in practice. In this paper, we propose a family of operators to merge stratified knowledge bases without commensurability assumption. Our merging operators generalize the quota operators, a family of important merging operators in classical logic. Both logical properties and computational complexity issues of the proposed operators are studied. [ABSTRACT FROM AUTHOR]

Most work on iterated belief change has focused on iterated belief revision, namely how to compute (K*x)*y. Historically however, belief revision can be defined in terms of belief expansion and belief contraction, where expansion and contraction are viewed as primary operators. Accordingly, our attention to iterated belief change should be focused on constructions like (K+x)+y, (K-x)+y, (K+x)-y and (K-x)-y. The first two of these are relatively straightforward, but the last two are more problematic. Here we consider these latter, and formulate iterated belief change by employing the Levi identity and the Harper Identity as the guiding principles. Keywords: Belief Change, Information State Change, Iterated Belief Contraction. [ABSTRACT FROM AUTHOR]

Basing on the view of dynamic capacity and knowledge-based view, this paper explores the definition and dimensions of knowledge capability. Differing from previous literature that think knowledge capability is the sum total of the knowledge assets of organizations, this paper defines knowledge capability as including both knowledge assets and knowledge operating capacities. And it is proposed that knowledge capability is dynamic, that is to say it will reconstruct with the changing of the environment. Since there are few empirical studies on the relationship between capability and organization performance, this paper suggests a model for further empirical studies on the impact of knowledge capability on organization performance. [ABSTRACT FROM AUTHOR]

A formalism for the logical description of computational agents and multi-agent systems is given. It is explained how it such a formal description can be used to configure and reason about multi-agent systems realizing computational intelligence models. A usage within a real software system Bang 3 is demonstrated. The logical description of multi-agent systems opens Bang 3 for interaction with ontology based distributed knowledge systems like the Semantic Web. [ABSTRACT FROM AUTHOR]

In WWW information integration, eliminating semantic hetero-geneity and implementing semantic combination is one of the key problems. This paper introduces classification ontology into WWW information integration to solve the semantic combination problem of heterogeneity classification architecture in Web information integration. However, there may be many kinds of ontology in a specific domain due to the structure of the websites, domain experts and different goals. So we have to combine all these kinds of ontology into logically unified integrated classification ontology in order to solve the problem of semantic heterogeneity commendably. This paper primarily discusses the method of building integrated classification ontology based on individual ontology, presents the definition of classification ontology, analyses the conceptual mapping and relational mapping between ontologies and solves the level conflict in the equivalent concepts. Keywords: Information integration, Classification ontology, Semantic, Similarity. [ABSTRACT FROM AUTHOR]

Computational Tree Logic (CTL) model update is a new system modification method for software verification. In this paper, a case study is described to show how a prototype model updater is implemented based on the authors' previous work of model update theoretical results [4]. The prototype is coded in Linux C and contains model checking, model update and parsing functions. The prototype is applied to the well known microwave oven example. This case study also illustrates some key features of our CTL model update approach such as the five primitive CTL model update operations and the associated minimal change semantics. This case study can be viewed as the first step towards the integration of model checking and model update for practical system modifications. [ABSTRACT FROM AUTHOR]

Dempster Shafer theory of evidence (DS theory) and possibility theory are two main formalisms in modelling and reasoning with uncertain information. These two theories are inter-related as already observed and discussed in many papers (e.g. [DP82, DP88b]). One aspect that is common to the two theories is how to quantitatively measure the degree of conflict (or inconsistency) between pieces of uncertain information. In DS theory, traditionally this is judged by the combined mass value assigned to the emptyset. Recently, two new approaches to measuring the conflict among belief functions are proposed in [JGB01, Liu06]. The former provides a distance-based method to quantify how close a pair of beliefs is while the latter deploys a pair of values to reveal the degree of conflict of two belief functions. On the other hand, in possibility theory, this is done through measuring the degree of inconsistency of merged information. However, this measure is not sufficient when pairs of uncertain information have the same degree of inconsistency. At present, there are no other alternatives that can further differentiate them, except an initiative based on coherence-intervals ([HL05a, HL05b]). In this paper, we investigate how the two new approaches developed in DS theory can be used to measure the conflict among possibilistic uncertain information. We also examine how the reliability of a source can be assessed in order to weaken a source when a conflict arises. [ABSTRACT FROM AUTHOR]

Representing and reasoning about spatial information is important in artificial intelligence and geographical information science. Relations between spatial entities are the most important kind of spatial information. Most current formalisms of spatial relations focus on one single aspect of space. This contrasts sharply with real world applications, where several aspects are usually involved together. This paper proposes a qualitative calculus that combines a simple directional relation model with the well-known topological RCC5 model. We show by construction that the consistency of atomic networks can be decided in polynomial time. Keywords: Qualitative Spatial Reasoning, topological relations, directional relations, consistency, realization. [ABSTRACT FROM AUTHOR]

With the growth of ontology scale and complexity, the query performance of Ontology Repository Management System (ORMS) becomes more and more important. The paper proposes materialized relations technique which speeds up query processing in ORMS by making the implicit derived relations of ontology explicit. Here the selection of materialized relations is a key problem, because the materialized relations technique trades off required inference time against maintenance cost and storage space. However, the problem has not been discussed formally before. So the paper proposes a QSS model to describe the queries set of ontology formally and gives the benefit evaluation model and the selection algorithm of materialized relations based on QSS model. The method in this paper not only considers the benefit in query response of the materialization technique, but also the storage and maintenance cost of it. In the end, an application case is introduced to prove the selection method of materialized relations is effective. [ABSTRACT FROM AUTHOR]

Formal concepts and concept lattices are two central notions of formal concept analysis. This paper investigates the problem of determining formal concepts based on the congruences on semilattices. The properties of congruences corresponding to formal contexts are discussed. The relationship between the closed sets generated by congruences and the elements of indiscernibility matrices is examined. Consequently, a new approach of determining concept lattices is derived. Keywords: Concept lattice, Congruence, Formal context, Indiscernibility matrix, Semilattice. [ABSTRACT FROM AUTHOR]

It is relatively easy to detect exact matches in Web documents; however, detecting similar content in distinct Web documents with different words and sentence structures is a much more difficult task. A reliable tool for determining the degree of similarity between any two Web documents could help filter or retain Web documents with similar content. Most methods for detecting similarity between documents rely on some kind of textual fingerprinting or a process of looking for exactly matched substrings. This may not be sufficient as changing the sentence structure or replacing words with synonyms can cause sentences with similar/same content to be treated as different. In this paper, we develop a sentence-based Fuzzy Set Information Retrieval (IR) approach, using word clusters that capture the similarity between different words for discovering similar documents. Our approach has the advantages of detecting documents with similar, but not necessarily the same, sentences based on fuzzy-word sets. The three different fuzzy-word clustering techniques that we have considered include the correlation cluster, the association cluster, and the metric cluster, which generate the word-to-word correlation values. Experimental results show that by adopting the metric cluster, our similarity detection approach has high accurate rate in detecting similar documents and improves previous Fuzzy Set IR approaches based solely on the correlation cluster. [ABSTRACT FROM AUTHOR]

We propose a novel system for collaborative knowledge sharing and recommendation based on taxonomic partial reputations on web-based personal knowledge directories. And we developed a prototype of the proposed system as a web-based user interface for personal knowledge management. This system presents a personal knowledge directory to a registered user. Such a directory has a personal ontology to integrate and classify the knowledge collected by a user from the Web. And the knowledge sharing activities among registered users generate partial reputation factors of knowledge items, their domain nodes, users and groups. Then new users can obtain the knowledge items proper to their needs, by referring such reputation values of those elements. In addition, users can also take the stem that is a set of common knowledge items over the domains designated by them. Thus proposed system can prevent cold-start problem because our knowledge recommendation mechanisms depend on the results of the collaborative knowledge sharing activities among users. [ABSTRACT FROM AUTHOR]

This paper presents a series of text-mining algorithms for managing knowledge directory, which is one of the most crucial problems in constructing knowledge management systems today. In future systems, the constructed directory, in which knowledge objects are automatically classified, should evolve so as to provide a good indexing service, as the knowledge collection grows or its usage changes. One challenging issue is how to combine manual and automatic organization facilities that enable a user to flexibly organize obtained knowledge by the hierarchical structure over time. To this end, I propose three algorithms that utilize text mining technologies: semi-supervised classification, semi-supervised clustering, and automatic directory building. Through experiments using controlled document collections, the proposed approach is shown to significantly support hierarchical organization of large electronic knowledge base with minimal human effort. [ABSTRACT FROM AUTHOR]

Ontology matching is an essential operation in many application domains, such as the Semantic Web, ontology merging or integration. So far, quite a few ontology matching approaches or matchers have been proposed. It has been observed that combining the results of multiple matchers is a promising technique to get better results than just using one matcher at a time. Many aggregation operators, such as Max, Min, Average and Weighted, have been developed. The limitations of these operators are studied. To overcome the limitations and provide a semantic interpretation for each aggregation operator, in this paper, we propose a linguistic combination system (LCS), where a linguistic aggregation operator (LAO), based on the ordered weighted averaging (OWA) operator, is used for the aggregation. A weight here is not associated with a specific matcher but a particular ordered position. A large number of LAOs can be developed for different uses, and the existing aggregation operators Max, Min and Average are the special cases in LAOs. For each LAO, there is a corresponding semantic interpretation. The experiments show the strength of our system. [ABSTRACT FROM AUTHOR]

Access control is an important issue related to the security on the Semantic Web. Role-Based Access Control (RBAC) is commonly considered as a flexible and efficient model in practice. In this paper, we provide an OWL-based approach for RBAC in the Semantic Web context. First we present an extended model of RBAC with negative authorization, providing detailed analysis of conflicts. Then we use OWL to formalize the extended model. Additionally, we show how to use an OWL-DL reasoner to detect the potential conflicts in the extended model. [ABSTRACT FROM AUTHOR]