基于情境感知和本体的E-Learning系统研究

为了合理、有效地描述E-Learning环境中学习者的学习情形,利用本体论具有知识组织清晰、概念和关系定义明确的特性,建立了E-Learning环境中的情境信息本体模型。为提高学习者的学习兴趣和学习效率,对采集到的情境信息进行情境推理后,采用资源调度控制算法调用相应的学习资源服务及时向学习者提供。应用实例验证了基于情境感知的本体E-Learning系统的服务性能。     

Can: A context-aware NAT traversal scheme

Network Address Translation (NAT) is a technique commonly used to share one public IPv4 address among several hosts located behind a NAT device. NAT devices typically block session requests originating from outside, causing NAT traversal problem that prevents the establishment of peer-to-peer (P2P) sessions. There have been many proposals for the NAT traversal problem. However, existing methods induce high connectivity check delay and resource demand when finding a communicating path, calling for a routine that determines the path best suited for a given pair of communicating peers. This study proposes CAN, a Context-Aware NAT traversal scheme which gathers and exchanges network-context information to find the most appropriate path for two communicating peers behind NAT devices. We have implemented CAN and conducted extensive experiments with off-the-shelf NAT devices to compare the performance of CAN with Interactivity Connectivity Establishment (ICE), the most acknowledged approach to creating a session across NATs. Experimental results show that CAN outperforms ICE in terms of direct communication ratio, connectivity check delay and message overload when checking connectivity.     

A multimodel context-aware tourism recommendation service: Approach and architecture

Recommender systems and services are now widely used to support decision-making in the fields characterized by the selection from a large number of alternatives with a significant influence of subjective preferences. A comprehensive multimodel approach to the development of context-aware recommender systems in the field of tourism information support is proposed. In particular, it is proposed to construct a recommender system based on loosely coupled modules, in which both personalized and nonpersonalized recommendation methods are implemented, and the synthesis module, which adapts the module system to the specific conditions of different kinds of initial information.     

A method for noise-robust context-aware pattern discovery and recognition from categorical sequences

An efficient method for weakly supervised pattern discovery and recognition from discrete categorical sequences is introduced. The method utilizes two parallel sources of data: categorical sequences carrying some temporal or spatial information and a set of labeled, but not exactly aligned, contextual events related to the sequences. From these inputs the method builds associative models able to describe systematically co-occurring structures in the input streams. The learned models, based on transitional probabilities of events observed at several different time lags, inherently segment and classify novel sequences into contextual categories. Learning and recognition processes are purely incremental and computationally cheap, making the approach suitable for on-line learning tasks. The capabilities of the algorithm are demonstrated in a keyword learning task from continuous infant-directed speech and a continuous speech recognition task operating at varying noise levels.     

Using ontology databases for scalable query answering,inconsistency detection,and data integration

An ontology database is a basic relational database management system that models an ontology plus its instances. To reason over the transitive closure of instances in the subsumption hierarchy, for example, an ontology database can either unfold views at query time or propagate assertions using triggers at load time. In this paper, we use existing benchmarks to evaluate our method—using triggers—and we demonstrate that by forward computing inferences, we not only improve query time, but the improvement appears to cost only more space (not time). However, we go on to show that the true penalties were simply opaque to the benchmark, i.e., the benchmark inadequately captures load-time costs. We have applied our methods to two case studies in biomedicine, using ontologies and data from genetics and neuroscience to illustrate two important applications: first, ontology databases answer ontology-based queries effectively; second, using triggers, ontology databases detect instance-based inconsistencies—something not possible using views. Finally, we demonstrate how to extend our methods to perform data integration across multiple, distributed ontology databases.     

Toward context-aware computing: experiences and lessons

To minimize distractions, a pervasive-computing environment must be context-aware. The authors define an activity-attention framework for context-aware computing, discuss the spatial and temporal aspects of applications they developed, and introduce a pervasive-computing architecture.     

Functional and multivalued dependencies in nested databases generated by record and list constructor

The impact of the list constructor on two important classes of relational dependencies is investigated. Lists represent an inevitable data structure whenever order matters and data is allowed to occur repeatedly. The list constructor is therefore supported by many advanced data models such as genomic sequence, deductive and object-oriented data models including XML. The article proposes finite axiomatisations of functional, multivalued and both functional and multivalued dependencies in nested databases supporting record and list constructor. In order to capture different data models at a time, an abstract algebraic approach based on nested attributes is taken. The presence of the list constructor calls for a new inference rule which allows to infer non-trivial functional dependencies from multivalued dependencies. Further differences to the relational theory become apparent when the independence of the inference rules is investigated. The extension of the relational theory to nested databases allows to specify more real-world constraints and increases therefore the number of application domains. * Results of this article were partly presented at the 3rd International Symposium on Foundations of Information and Knowledge Systems (FoIKS), Vienna, Austria, 2004 [58], and the 23rd International Symposium on Principles of Database Systems (PODS), Paris, France, 2004 [54]. ** Sebastian Link was supported by Marsden Funding, Royal Society of New Zealand.     

Mining interesting association rules from customer databases and transaction databases

In this paper, we examine a new data mining issue of mining association rules from customer databases and transaction databases. The problem is decomposed into two subproblems: identifying all the large itemsets from the transaction database and mining association rules from the customer database and the large itemsets identified. For the first subproblem, we propose an efficient algorithm to discover all the large itemsets from the transaction database. Experimental results show that by our approach, the total execution time can be reduced significantly. For the second subproblem, a relationship graph is constructed according to the identified large itemsets from the transaction database and the priorities of condition attributes from the customer database. Based on the relationship graph, we present an efficient graph-based algorithm to discover interesting association rules embedded in the transaction database and the customer database.     

JCOOLS: A toolkit for generating context-aware applications with JCAF and DROOLS

We present a toolkit named JCOOLS that effectively generates context-aware applications in a ubiquitous environment. With JCOOLS, developers can define contexts and actions as context rules according to the change of context information. Based on the predefined context rules and the underlying DROOLS inference engine, JCOOLS generates responding actions that would execute in the associated end-user applications. In addition, to facilitate the development and deployment of context-aware applications, JCOOLS generates abstract program codes based on the context information for JCAF.     

Falcon-AO: A practical ontology matching system

In this paper, we introduce a general overview of Falcon-AO: a practical ontology matching system with acceptable to good performance and a number of remarkable features. Furthermore, Falcon-AO is one of the best systems in all kinds of tests in the latest three years’ OAEI campaigns. Falcon-AO is written in Java, and is open source.     

LOD search engine: A semantic search over linked data

Journal of Intelligent Information Systems - In the last few years, there has been a significant growth in the amount of data published in RDF and adoption of Linked Data principles. Every day, a...     

虚拟场景生成中的LOD技术综述

虚拟现实技术以其关键技术使参与者产生高度逼真的“现实”感觉。目前的计算机图形学水平,只要有足够的时间,就能生成准确的计算机图像。但是,虚拟场景的生成对图形的实时性要求很高,因此,不得小降低虚拟场景的几何复杂度和图像质量,或采用其它技术来提高虚拟场景的逼真程度和生成速度。LOD技术是虚拟场景自动生成和快速绘制技术研究领域的一个很有前途的研究方向。该文介绍了虚拟场景生成中LOD模型的选择、生成,着重分析了典型模型生成算法和存在的问题,展望了LOD技术未来的研究方向。     

Hybreed: A software framework for developing context-aware hybrid recommender systems

This article introduces Hybreed, a software framework for building complex context-aware applications, together with a set of components that are specifically targeted at developing hybrid, context-aware recommender systems. Hybreed is based on a concept for processing context that we call dynamic contextualization. The underlying notion of context is very generic, enabling application developers to exploit sensor-based physical factors as well as factors derived from user models or user interaction. This approach is well aligned with context definitions that emphasize the dynamic and activity-oriented nature of context. As an extension of the generic framework, we describe Hybreed RecViews, a set of components facilitating the development of context-aware and hybrid recommender systems. With Hybreed and RecViews, developers can rapidly develop context-aware applications that generate recommendations for both individual users and groups. The framework provides a range of recommendation algorithms and strategies for producing group recommendations as well as templates for combining different methods into hybrid recommenders. Hybreed also provides means for integrating existing user or product data from external sources such as social networks. It combines aspects known from context processing frameworks with features of state-of-the-art recommender system frameworks, aspects that have been addressed only separately in previous research. To our knowledge, Hybreed is the first framework to cover all these aspects in an integrated manner. To evaluate the framework and its conceptual foundation, we verified its capabilities in three different use cases. The evaluation also comprises a comparative assessment of Hybreed’s functional features, a comparison to existing frameworks, and a user study assessing its usability for developers. The results of this study indicate that Hybreed is intuitive to use and extend by developers.     

BiOSS: A system for biomedical ontology selection

In biomedical informatics, ontologies are considered a key technology for annotating, retrieving and sharing the huge volume of publicly available data. Due to the increasing amount, complexity and variety of existing biomedical ontologies, choosing the ones to be used in a semantic annotation problem or to design a specific application is a difficult task. As a consequence, the design of approaches and tools addressed to facilitate the selection of biomedical ontologies is becoming a priority. In this paper we present BiOSS, a novel system for the selection of biomedical ontologies. BiOSS evaluates the adequacy of an ontology to a given domain according to three different criteria: (1) the extent to which the ontology covers the domain; (2) the semantic richness of the ontology in the domain; (3) the popularity of the ontology in the biomedical community. BiOSS has been applied to 5 representative problems of ontology selection. It also has been compared to existing methods and tools. Results are promising and show the usefulness of BiOSS to solve real-world ontology selection problems. BiOSS is openly available both as a web tool and a web service.     

Understanding ontology evolution: A change detection approach

In this article, we propose a change detection approach in the context of an ontology evolution framework for OWL DL ontologies. The framework allows ontology engineers to request and apply changes to the ontology they manage. Furthermore, the framework assures that the ontology and its depending artifacts remain consistent after changes have been applied. Innovative is that the framework includes a change detection mechanism that allows generating automatically a detailed overview of changes that have occurred based on a set of change definitions. In addition, different users (such as maintainers of depending artifacts) may have their own set of change definitions, which results into different overviews of the changes, each providing a different view on how the ontology has been changed. Using these change definitions, also different levels of abstraction are supported. Both features will enhance the understanding of the evolution of an ontology for different users.     

A crowdsourcing approach to building a legal ontology from text

This article focuses on the problems of application of artificial intelligence to represent legal knowledge. The volume of legal knowledge used in practice is unusually large, and therefore the ontological knowledge representation is proposed to be used for semantic analysis, presentation and use of common vocabulary, and knowledge integration of problem domain. At the same time some features of legal knowledge representation in Ukraine have been taken into account. The software package has been developed to work with the ontology. The main features of the program complex, which has a Web-based interface and supports multi-user filling of the knowledge base, have been described. The crowdsourcing method is due to be used for filling the knowledge base of legal information. The success of this method is explained by the self-organization principle of information. However, as a result of such collective work a number of errors are identified, which are distributed throughout the structure of the ontology. The results of application of this program complex are discussed in the end of the article and the ways of improvement of the considered technique are planned.     

Uncertain deductive databases: A hybrid approach

Uncertainty in deductive databases and logic programming has been modeled using a variety of (numeric and non-numeric) formalisms in the past, including probabilistic, possibilistic, and fuzzy set-theoretic approaches, and many valued logic programming. In this paper, we consider a hybrid approach to the modeling of uncertainty in deductive databases. Our model, called deductive IST (DIST) is based on an extension of the Information Source Tracking (IST) model, recently proposed for relational databases. The DIST model permits uncertainty to be modeled and manipulated in essentially qualitative terms with an option to convert qualitative expressions of uncertainty into numeric form (e.g., probabilities). An uncertain deductive database is modeled as a Horn clause program in the DIST framework, where each fact and rule is annotated with an expression indicating the “sources” contributing to this information and their nature of contribution. (1) We show that positive DIST programs enjoy the least model/least fixpoint semantics analogous to classical logic programs. (2) We show that top-down (e.g., SLD-resolution) and bottom-up (e.g., magic sets rewriting followed by semi-naive evaluation) query processing strategies developed for datalog can be easily extended to DIST programs. (3) Results and techniques for handling negation as failure in classical logic programming can be easily extended to DIST. As an illustration of this, we show how stratified negation can be so extended. We next study the problem of query optimization in such databases and establish the following results. (4) We formulate query containment in qualitative as well as quantitative terms. Intuitively, our qualitative sense of containment would say a query Q₁ is contained in a query Q₂ provided for every input database D, for every tuple t, t ε Q₂(D) holds in every “situation” in which t ε Q₁(D) is true. The quantitative notion of containment would say Q₁ is contained in Q₂ provided on every input, the certainty associated with any tuple computed by Q₁ is no more than the certainty associated with the same tuple by Q₂ on the given input. We also prove that qualitative and quantitative notions of containment (both absolute and uniform versions) coincide. (5) We establish necessary and sufficient conditions for the qualitative containment of conjunctive queries. (6) We extend the well-known chase technique to develop a test for uniform containment and equivalence of positive DIST programs. (7) Finally, we prove that the complexity of testing containment of conjunctive DIST queries remains the same as in the classical case when number of information sources is regarded as a constant (so, it's NP-complete in the size of the queries). We also show that testing containment of conjunctive queries is co-NP-complete in the number of information sources.     

A methodology to retrieve text documents from multiple databases

This paper presents a methodology for finding the n most similar documents across multiple text databases for any given query and for any positive integer n. This methodology consists of two steps. First, the contents of databases are indicated approximately by database representatives. Databases are ranked using their representatives with respect to the given query. We provide a necessary and sufficient condition to rank the databases optimally. In order to satisfy this condition, we provide three estimation methods. One estimation method is intended for short queries; the other two are for all queries. Second, we provide an algorithm, OptDocRetrv, to retrieve documents from the databases according to their rank and in a particular way. We show that if the databases containing the n most similar documents for a given query are ranked ahead of other databases, our methodology will guarantee the retrieval of the n most similar documents for the query. When the number of databases is large, we propose to organize database representatives into a hierarchy and employ a best-search algorithm to search the hierarchy. It is shown that the effectiveness of the best-search algorithm is the same as that of evaluating the user query against all database representatives.     

A context-aware ubiquitous learning environment for language listening and speaking

This paper reported the results of a study that aimed to construct a sensor and handheld augmented reality (AR)-supported ubiquitous learning (u-learning) environment called the Handheld English Language Learning Organization (HELLO), which is geared towards enhancing students' language learning. The HELLO integrates sensors, AR, ubiquitous computing and information technologies. It is composed of two subsystems: an English learning management system and a u-learning tool. In order to evaluate the effects of the proposed learning environment on the learning performance of students, a case study on English learning was conducted on a school campus. The participants included high school teachers and students. A learning course entitled 'My Campus' was conducted in the class; it included three activities, namely 'Campus Environment', 'Campus Life' and 'Campus Story'. The evaluation results showed that the proposed HELLO and the learning activities could improve the students' English listening and speaking skills.