Ontology-Based Privacy Protection in Location Commerce

Location commerce extends e-commerce through the provision of location-related activities, but this gives rise to greater concerns about privacy invasion. To encourage the smooth growth of location commerce, it is suggested that control over the sharing of intimate information be given back to the consumer. This study proposes an ontology-based privacy protection (OPP) framework that allows consumers to specify their own privacy preferences and then uses these preferences to determine whether or not a message from a merchant can be delivered to a consumer. We use ontology to structure the knowledge to simplify the framework and allow for the possibility of automation. The system is believed to be context-aware, as the location, time, service type, information type, and other contextual data are taken into consideration. We develop a prototype system for demonstration and experiment, and show that the framework design is feasible and has a reasonable performance

Evaluation Metrics for Computer Science Domain Specific Ontology in Semantic Web Based IRSCSD System

Performance and efficiency of web information retrieval is important for knowledge engineers, novice users and for organizations. In semantic web based system, the concept of ontology is used to search results by contextual meaning of input query instead of keyword matching. Ontology is an approximate specification of a domain whereas ontology evaluation is concerned with the degree or rather the distance between this approximate conceptualization and the real world. For this, ontology based information retrieval system for computer science domain is designed which this research calls as the IRSCSD system. This paper considers the evaluation of prototype ontology developed in computer science domain for IRSCSD system and has four- fold objectives. Firstly, paper highlights the high level design of IRSCSD system (Information retrieval system for computer science domain). Secondly, paper discusses the prototype ontology developed for computer science domain taking one of its core subjects. Thirdly, paper focuses on the need for evaluation of ontology and various approaches, metrics which can be used for evaluation of domain specific ontology in computer science. Lastly, implementation will be shown by considering those approaches on prototype ontology along with the data sets used for evaluation

Pemanfaatan Konsep Ontology Dalam Interaksi Sistem Collaborative Learning

In the present time, learning system goes through a period of a paradigm shift from conventional learning model into an interactive learning system with information technology-assisted. During its development, interactive learning model has been proven to have an impact that is good enough from the culture, worldview, and also the media used in the learning process. Nevertheless, not all of its evolution has a acceptable effect, especially on the ability of students in terms of communicating the level of the forum or group. Furthermore, a high intensity in the use of media technology also had been trigger the gap between students with different backgrounds individually. This research has focused on providing the views or perception of the structure and flow of information on each entity involved in the collaborative learning system. Collaborative learning is one of the solutions in which this model can improve the soft skills of learners to be able to interact in contextual, integrated, and able to work together to create a conducive academic atmosphere. The presence of the concept of ontology is used because it can provide equivalence perception of the structure and flow of information to any entity involved in this collaborative learning system. Ontology can be defined as the concept of interconnected or relationship which then can cooperatively build a structure on a domain and limit the interpretation of the term science. Based on the framework created, there are 5 important sub-domains in the design model of Collaborative Learning ie Trigger, Learning Materials, Learning Scenarios, Learning Group, and Collaborative Learning Goal. Contribution of this research is to produce a framework Collaborative Learning Ontology for system developers as a guide to re-design the e-Learning syste

Rover-II: A Context-Aware Middleware for Pervasive Computing Environment

It is well recognized that context plays a significant role in all human endeavors. All decisions are based on information which has to be interpreted in context. By making information systems context-aware we can have systems that significantly enhance human capabilities to make critical decisions. A major challenge of context-aware systems is to balance usability with generality and extensibility. The relevant context changes depending on the particular application. The model used to represent the context and its relationship to entities must be general enough to allow additions of context categories without redesign while remaining usable across many applications. Also, while efforts are put in by application designers and developers to make applications context-aware, these efforts are customized to specific needs of the target application, and only certain common contexts like location and time are taken into account. Therefore, a general framework is called for that can (i) efficiently maintain, represent and integrate contextual information, (ii) act as an integration platform where different applications can share contexts and (iii) provide relevant services to make efficient use of the contextual information. This dissertation presents: * a generic and effective context model - Rover Context Model (RoCoM) that is structured around four primitives: entities, events, relationships, and activities; and practically usable through the concept of templates, * a flexible, extensible and generic ontology - Rover Context Model Ontology (RoCoMO) supporting the model, that addresses the shortcomings of existing ontologies, * an effective mechanism of modeling the context of a situation, through the concept of relevant context, with the help of situation graph, efficiently handling and making best use of context information, * a context middleware - Rover-II, which serves as a framework for contextual information integration, that could be used not just to store and compile the contextual information, but also integrate relevant services to enhance the context information; and more importantly, enable sharing of context among the applications subscribed to it, * the initial design and implementation of a distributed architecture for Rover-II, following a P2P arrangement inspired from Tapestry, The above concepts are illustrated through M-Urgency, a context-aware public safety system that has been deployed at the University of Maryland Police Department

Sensory semantic user interfaces (SenSUI)

Rapid evolution of the World Wide Web with its underlying sources of data, knowledge, services and applications continually attempts to support a variety of users, with different backgrounds, requirements and capabilities. In such an environment, it is highly unlikely that a single user interface will prevail and be able to fulfill the requirements of each user adequately. Adaptive user interfaces are able to adapt information and application functionalities to the user context. In contrast, pervasive computing and sensor networks open new opportunities for context aware platforms, one that is able to improve user interface adaptation reacting to environmental and user sensors. Semantic web technologies and ontologies are able to capture sensor data and provide contextual information about the user, their actions, required applications and environment. This paper investigates the viability of an approach where semantic web technologies are used to maximize the efficacy of interface adaptation through the use of available ontology

Context-Aware Information Retrieval for Enhanced Situation Awareness

In the coalition forces, users are increasingly challenged with the issues of information overload and correlation of information from heterogeneous sources. Users might need different pieces of information, ranging from information about a single building, to the resolution strategy of a global conflict. Sometimes, the time, location and past history of information access can also shape the information needs of users. Information systems need to help users pull together data from disparate sources according to their expressed needs (as represented by system queries), as well as less specific criteria. Information consumers have varying roles, tasks/missions, goals and agendas, knowledge and background, and personal preferences. These factors can be used to shape both the execution of user queries and the form in which retrieved information is packaged. However, full automation of this daunting information aggregation and customization task is not possible with existing approaches. In this paper we present an infrastructure for context-aware information retrieval to enhance situation awareness. The infrastructure provides each user with a customized, mission-oriented system that gives access to the right information from heterogeneous sources in the context of a particular task, plan and/or mission. The approach lays on five intertwined fundamental concepts, namely Workflow, Context, Ontology, Profile and Information Aggregation. The exploitation of this knowledge, using appropriate domain ontologies, will make it feasible to provide contextual assistance in various ways to the work performed according to a user’s taskrelevant information requirements. This paper formalizes these concepts and their interrelationships

Modelling data intensive web sites with OntoWeaver

This paper illustrates the OntoWeaver modelling approach, which relies on a set of comprehensive site ontologies to model all aspects of data intensive web sites and thus offers high level support for the design and development of data-intensive web sites. In particular, the OntoWeaver site ontologies comprise two components: a site view ontology and a presentation ontology. The site view ontology provides meta-models to allow for the composition of sophisticated site views, which allow end users to navigate and manipulate the underlying domain databases. The presentation ontology abstracts the look and feel for site views and makes it possible for the visual appearance and layout to be specified at a high level of abstractio

A context ontology for a mobile recommender system of advertisements)

Currently, most recommendation systems do not consider the context in which they are executed, being inappropriate to operate on mobile devices, this can be observed in the field of advertising, where users are overwhelmed by the excessive general information that they receive, causing widespread dissatisfaction with their use. One of the biggest challenges to incorporate contextual information to the software is the design of a formal model for its representation, because traditional methods are inadequate for this purpose, being necessary to use alternative approaches such as those based on ontologies. This work describes the process used in the construction of an ontology to represent the information of the advertisements and the contextual dimensions: location, time and users’ needs, to consider when recommending. Through the application of the NeOn methodology, an expressive and extensible ontological model was obtained that integrates the ontologies: FOAF, OWL-Time and WGS84 Geo Positioning. The proposed ontology is an initial contribution for the creation of a context-aware mobile recommender system of advertisements

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline