Intelligent Association Exploration and Exploitation of Fuzzy Agents in Ambient Intelligent Environments

This paper presents a novel fuzzy-based intelligent architecture that aims to find relevant and important associations between embedded-agent based services that form Ambient Intelligent Environments (AIEs). The embedded agents are used in two ways; first they monitor the inhabitants of the AIE, learning their behaviours in an online, non-intrusive and life-long fashion with the aim of pre-emptively setting the environment to the users preferred state. Secondly, they evaluate the relevance and significance of the associations to various services with the aim of eliminating redundant associations in order to minimize the agent computational latency within the AIE. The embedded agents employ fuzzy-logic due to its robustness to the uncertainties, noise and imprecision encountered in AIEs. We describe unique real world experiments that were conducted in the Essex intelligent Dormitory (iDorm) to evaluate and validate the significance of the proposed architecture and methods

Recommendation technique-based government-to-business personalized e-services

One of the new directions in current e-government development is to provide personalized online services to citizens and businesses. Recommendation techniques can bring a possible solution for this issue. This study proposes a hybrid recommendation approach to provide personalized government to business (G2B) e-services. The approach integrates fuzzy sets-based semantic similarity and traditional item-based collaborative filtering methods to improve recommendation accuracy. A recommender system named Intelligent Business Partner Locator (IBPL) is designed to apply the proposed recommendation approach for supporting government agencies to recommend business partners. ©2009 IEEE

A Dynamic Knowledge Management Framework for the High Value Manufacturing Industry

Dynamic Knowledge Management (KM) is a combination of cultural and technological factors, including the cultural factors of people and their motivations, technological factors of content and infrastructure and, where these both come together, interface factors. In this paper a Dynamic KM framework is described in the context of employees being motivated to create profit for their company through product development in high value manufacturing. It is reported how the framework was discussed during a meeting of the collaborating company’s (BAE Systems) project stakeholders. Participants agreed the framework would have most benefit at the start of the product lifecycle before key decisions were made. The framework has been designed to support organisational learning and to reward employees that improve the position of the company in the market place

On relational learning and discovery in social networks: a survey

The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence onto popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements

Constrained Querying of Multimedia Databases

Copyright 2001 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/10.1117/12.410976This paper investigates the problem of high-level querying of multimedia data by imposing arbitrary domain-specific constraints among multimedia objects. We argue that the current structured query mode, and the query-by-content model, are insufficient for many important applications, and we propose an alternative query framework that unifies and extends the previous two models. The proposed framework is based on the querying-by-concept paradigm, where the query is expressed simply in terms of concepts, regardless of the complexity of the underlying multimedia search engines. The query-by-concept paradigm was previously illustrated by the CAMEL system. The present paper builds upon and extends that work by adding arbitrary constraints and multiple levels of hierarchy in the concept representation model. We consider queries simply as descriptions of virtual data set, and that allows us to use the same unifying concept representation for query specification, as well as for data annotation purposes. We also identify some key issues and challenges presented by the new framework, and we outline possible approaches for overcoming them. In particular, we study the problems of concept representation, extraction, refinement, storage, and matching

A novel approach integrating ranking functions discovery, optimization and infernce to improve retrieval performance

The significant roles play by ranking function in the performance and success of Information Retrieval (IR) systems and search engines cannot be underestimated. Diverse ranking functions are available in IR literature. However, empirical studies show that ranking functions do not perform constantly well across different contexts (queries, collections, users). In this study, a novel three-stage integrated ranking framework is proposed for implementing discovering, optimizing and inference rankings used in IR systems. The first phase, discovery process is based on Genetic Programming (GP) approach which smartly combines structural and contents features in the documents while the second phase, optimization process is based on Genetic Algorithm (GA) which combines document retrieval scores of various well-known ranking functions. In the 3rd phase, Fuzzy inference proves as soft search constraints to be applied on documents. We demonstrate how these two features are combined to bring new tasks and processes within the three concept stages of integrated framework for effective IR

Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology

A formal model for fuzzy ontologies.

Au Yeung Ching Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 97-110).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- The Semantic Web and Ontologies --- p.3Chapter 1.2 --- Motivations --- p.5Chapter 1.2.1 --- Fuzziness of Concepts --- p.6Chapter 1.2.2 --- Typicality of Objects --- p.6Chapter 1.2.3 --- Context and Its Effect on Reasoning --- p.8Chapter 1.3 --- Objectives --- p.9Chapter 1.4 --- Contributions --- p.10Chapter 1.5 --- Structure of the Thesis --- p.11Chapter 2 --- Background Study --- p.13Chapter 2.1 --- The Semantic Web --- p.14Chapter 2.2 --- Ontologies --- p.16Chapter 2.3 --- Description Logics --- p.20Chapter 2.4 --- Fuzzy Set Theory --- p.23Chapter 2.5 --- Concepts and Categorization in Cognitive Psychology --- p.25Chapter 2.5.1 --- Theory of Concepts --- p.26Chapter 2.5.2 --- Goodness of Example versus Degree of Typicality --- p.28Chapter 2.5.3 --- Similarity between Concepts --- p.29Chapter 2.5.4 --- Context and Context Effects --- p.31Chapter 2.6 --- Handling of Uncertainty in Ontologies and Description Logics --- p.33Chapter 2.7 --- Typicality in Models for Knowledge Representation --- p.35Chapter 2.8 --- Semantic Similarity in Ontologies and the Semantic Web --- p.39Chapter 2.9 --- Contextual Reasoning --- p.41Chapter 3 --- A Formal Model of Ontology --- p.44Chapter 3.1 --- Rationale --- p.45Chapter 3.2 --- Concepts --- p.47Chapter 3.3 --- Characteristic Vector and Property Vector --- p.47Chapter 3.4 --- Subsumption of Concepts --- p.49Chapter 3.5 --- Likeliness of an Individual in a Concept --- p.51Chapter 3.6 --- Prototype Vector and Typicality --- p.54Chapter 3.7 --- An Example --- p.59Chapter 3.8 --- Similarity between Concepts --- p.61Chapter 3.9 --- Context and Contextualization of Ontology --- p.65Chapter 3.9.1 --- Formal Definitions --- p.67Chapter 3.9.2 --- Contextualization of an Ontology --- p.69Chapter 3.9.3 --- "Contextualized Subsumption Relations, Likeliness, Typicality and Similarity" --- p.71Chapter 4 --- Discussions and Analysis --- p.73Chapter 4.1 --- Properties of the Formal Model for Fuzzy Ontologies --- p.73Chapter 4.2 --- Likeliness and Typicality --- p.78Chapter 4.3 --- Comparison between the Proposed Model and Related Works --- p.81Chapter 4.3.1 --- Comparison with Traditional Ontology Models --- p.81Chapter 4.3.2 --- Comparison with Fuzzy Ontologies and DLs --- p.82Chapter 4.3.3 --- Comparison with Ontologies modeling Typicality of Objects --- p.83Chapter 4.3.4 --- Comparison with Ontologies modeling Context --- p.84Chapter 4.3.5 --- Limitations of the Proposed Model --- p.85Chapter 4.4 --- "Significance of Modeling Likeliness, Typicality and Context in Ontologies" --- p.86Chapter 4.5 --- Potential Application of the Model --- p.88Chapter 4.5.1 --- Searching in the Semantic Web --- p.88Chapter 4.5.2 --- Benefits of the Formal Model of Ontology --- p.90Chapter 5 --- Conclusions and Future Work --- p.91Chapter 5.1 --- Conclusions --- p.91Chapter 5.2 --- Future Research Directions --- p.93Publications --- p.96Bibliography --- p.9

Dynamic adaptation of user profiles in recommender systems

In a period of time in which the content available through the Internet increases exponentially and is more easily accessible every day, techniques for aiding the selection and extraction of important and personalised information are of vital importance. Recommender Systems (RS) appear as a tool to help the user in a decision making process by evaluating a set of objects or alternatives and aiding the user at choosing which one/s of them suits better his/her interests or preferences. Those preferences need to be accurate enough to produce adequate recommendations and should be updated if the user changes his/her likes or if they are incorrect or incomplete. In this work an adequate model for managing user preferences in a multi-attribute (numerical and categorical) environment is presented to aid at providing recommendations in those kinds of contexts. The evaluation process of the recommender system designed is supported by a new aggregation operator (Unbalanced LOWA) that enables the combination of the information that defines an alternative into a single value, which then is used to rank the whole set of alternatives. After the recommendation has been made, learning processes have been designed to evaluate the user interaction with the system to find out, in a dynamic and unsupervised way, if the user profile in which the recommendation process relies on needs to be updated with new preferences. The work detailed in this document also includes extensive evaluation and testing of all the elements that take part in the recommendation and learning processes