Ontology acquisition and exchange of evolutionary product-brokering agents

Agent-based electronic commerce (e-commerce) has been booming with the development of the Internet and agent technologies. However, little effort has been devoted to exploring the learning and evolving capabilities of software agents. This paper addresses issues of evolving software agents in e-commerce applications. An agent structure with evolution features is proposed with a focus on internal hierarchical knowledge. We argue that knowledge base of agents should be the cornerstone for their evolution capabilities, and agents can enhance their knowledge bases by exchanging knowledge with other agents. In this paper, product ontology is chosen as an instance of knowledge base. We propose a new approach to facilitate ontology exchange among e-commerce agents. The ontology exchange model and its formalities are elaborated. Product-brokering agents have been designed and implemented, which accomplish the ontology exchange process from request to integration

Finding the right answer: an information retrieval approach supporting knowledge sharing

Knowledge Management can be defined as the effective strategies to get the right piece of knowledge to the right person in the right time. Having the main purpose of providing users with information items of their interest, recommender systems seem to be quite valuable for organizational knowledge management environments. Here we present KARe (Knowledgeable Agent for Recommendations), a multiagent recommender system that supports users sharing knowledge in a peer-to-peer environment. Central to this work is the assumption that social interaction is essential for the creation and dissemination of new knowledge. Supporting social interaction, KARe allows users to share knowledge through questions and answers. This paper describes KARe�s agent-oriented architecture and presents its recommendation algorithm

Internet search techniques: using word count, links and directory structure as internet search tools

A thesis submitted for the degree of Doctor of Philosophy ofthe University of LutonAs the Web grows in size it becomes increasingly important that ways are developed to maximise the efficiency of the search process and index its contents with minimal human intervention. An evaluation is undertaken of current popular search engines which use a centralised index approach. Using a number of search terms and metrics that measure similarity between sets of results, it was found that there is very little commonality between the outcome of the same search performed using different search engines. A semi-automated system for searching the web is presented, the Internet Search Agent (ISA), this employs a method for indexing based upon the idea of "fingerprint types". These fingerprint types are based upon the text and links contained in the web pages being indexed. Three examples of fingerprint type are developed, the first concentrating upon the textual content of the indexed files, the other two augment this with the use of links to and from these files. By looking at the results returned as a search progresses in terms of numbers and measures of content of results for effort expended, comparisons can be made between the three fingerprint types. The ISA model allows the searcher to be presented with results in context and potentially allows for distributed searching to be implemented

An Intelligent Context Aware Recommender System for Real-Estate

Finding products and items in large online space that meet user needs is difficult. Time spent searching before finding a relevant item can be a significant time sink for users. As with other economic branches, growing Internet usage also changed user behavior in the real-estate market. Advancements in virtual reality offer virtual tours and interactive map and floor plans which make an online rental websites very popular among users. With the abundance of information, recommender systems become more important than ever to give the user relevant property suggestions and reduce search time. A sophisticated recommender in this domain can help reduce the need of a real-estate agent. Session-based user behavior and lack of user profiles leads to the use of traditional recommendation methods. In this research, we propose an approach for real-estate recommendation based on Gated Orthogonal Recurrent Unit (GORU) and Weighted Cosine Similarity. GORU captures the user search context and weighted cosine similarity improves the rank of pertinent property. We have used the data of an online public real estate web portal (AARZ.PK). The data represents the original behavior of the user on an online portal. We have used Recall, User coverage and Mean Reciprocal Rank (MRR) metrics for the evaluation of our system against other state-of-the-art techniques. The proposed solution outperforms various baselines and state-of-the-art RNN based solutions

Integrating Case-Based Reasoning in Job Matching System for Pre-selection Process of Recruitment

The progress of Internet and World Wide Web technology brings the movement of traditional recruitment process to web based recruitment. Applying job matching approach automatically will bring benefit to both job seekers and employers. For the employer, the costs of manually preselecting potential candidates have risen and employers are searching for means to automate the preselecting of candidates. A few techniques could be applied in order to implement job matching process such as using fuzzy matching, semantic, rule-base reasoning and case–based reasoning (CBR). This study aims to demonstrate that CBR could be integrated in job matching to recommend the best candidate suitable with the job requirement using similarity measurement. As a result, a prototype called Intelligent Agent Dot Com (IADC) using CBR engine for matching purposes has been developed, validated and evaluated in this study. The finding through validation and evaluation phase indicates that IADC is reliable to assist employer in the pre-selection process during recruitment. In fact, the pre-selection of candidates has become easier than the manual process

SICS MarketSpace: an agent-based market infrastructure

We present a simple and uniform communication framework for an agent-based market infrastructure, the goal of which is to enable automation of markets with self-interested participants distributed over the Internet

Appariement des adresses avec application à la recherche des informations de service géo-localisées

This thesis reports a term-weighted dissimilarity algorithm and its application to address matching in Location Based Services (LBS). It is composed of two parts: address matching and web application of location based services. We start with a brief introduction of location based services, the background of address matching and the main objectives and accomplishments of this study. In Part 1 we discuss various string similarity measures, e.g., the Levenshtein Distance, the Damerau-Levenshtein Distance, the Longest Common Subsequence, the Searching Minimum Errors and Hamming Distance as well as vector space model. Upon evaluating their strength and weakness, we introduce a term-weighted dissimilarity for effective address matching. This is a combination of edit distance similarity and Term Frequency-Inverse Document Frequency weighting. We implement this algorithm into a software and show its effectiveness via a real application for address matching and correction based on Canada Post's address standard. In Part 2 we are concerned with a mobile application of address extracting algorithms for location based services. We build an intelligent agent for online LBS via wireless Internet accessing. Such an agent, based on efficient and accurate address identification, can analyze the content of certain Web pages (ex. Yellow Pages) to search desired LBS information. We propose an ontology-based conceptual information retrieval approach combined with tree structure matching and the nearest neighbour methods to perform address extraction from texts and documents. A prototype, RouteInfo Mobile LBS for automobile drivers, is developed and tested successfully for LBS searching, mapping and route finding. Future application to target marketing through a combination with customer behavioural and transaction data will also be considered

Self-organizing peer-to-peer social networks

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 The Authors.Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules.National Natural Science of Foundation of Chin