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Improving Recall and Precision of a Personalized Semantic Search Engine for E-learning

Improving Recall and Precision of a Personalized Semantic Se...

引用

International Conference on the Digital Society

作者： Olfa Nasraoui Leyla Zhuhadar Knowledge Discovery and Web Mining Laboratory Department of Computer Engineering and Computer Science University of Louisville KY USA

ISBN: (纸本)9781424458059;9780769539539

The main objective of this paper is to propose and evaluate an architecture that provides, manages, and collects data that permit high levels of adaptability and relevance to the user profiles. In addition, we implement this architecture on a platform called HyperManyMedia. To achieve this objective, an approach for personalized search is implemented that takes advantage of the semantic Web standards (RDF and OWL) to represent the content and the user profiles. The framework consists of the following phases: (1) building the semantic E-learning domain using the known college and course information as concept and sub-concept, (2) generating the semantic user profiles as ontologies, (3) clustering the documents to discover more refined sub-concepts, (4) re-ranking the user's search results based on his/her profile, and (5) providing the user with semantic recommendations. The implementation of the ontologies models is separate from the design and implementation of the information retrieval system, thus providing a modular framework that is easy to adapt and port to other platforms. Finally, the experimental results show that the user context can be effectively used for improving the precision and recall in E-learning search, particularly by re-ranking the search results based on the user profiles.

关键词： Search engines Electronic learning Ontologies Semantic Web Information retrieval Educational institutions computer architecture Resource description framework OWL Taxonomy

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Self-optimizing through CBR learning

Self-optimizing through CBR learning

引用

Congress on Evolutionary Computation

作者： Ivo Pereira Ana Madureira Computer Science Department GECAD-Knowledge Engineering and Decision Support Research Group Polytechnic of Porto Porto Portugal Computer Science Department Institute of Engineering GECAD-Knowledge Engineering and Decision Support Research Group Polytechnic of Porto Porto Portugal

In this paper, we foresee the use of Multi-Agent Systems for supporting dynamic and distributed scheduling in Manufacturing Systems. We also envisage the use of Autonomic properties in order to reduce the complexity of managing systems and human interference. By combining Multi-Agent Systems, Autonomic Computing, and Nature Inspired Techniques we propose an approach for the resolution of dynamic scheduling problem, with Case-based Reasoning Learning capabilities. The objective is to permit a system to be able to automatically adopt/select a Meta-heuristic and respective parameterization considering scheduling characteristics. From the comparison of the obtained results with previous results, we conclude about the benefits of its use.

关键词： Job shop scheduling Dynamic scheduling Equations Mathematical model Tuning Complexity theory

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SLA validation of service value chains

SLA validation of service value chains

引用

9th International Conference on Grid and Cloud Computing, GCC 2010

作者： Ul Haq, Irfan Schikuta, Erich Brandic, Ivona Paschke, Adrian Boley, Harold Department of Knowledge and Business Engineering University of Vienna Austria Information Systems Institute Vienna University of Technology Austria Institute of Computer Science Freie University Berlin Germany Institute of Information Technology National Research Council Canada

ISBN: (纸本)9780769543130

Business-to-Business (B2B) workflow/service interoperation across Virtual Organisations (VOs) brings about novel business scenarios. In these scenarios, parts of workflows (or services) corresponding to different partners can be aggregated in a producer-consumer manner, leading to hierarchical structures of added value interpreted as service value chains. Service Level Agreements (SLAs), which are contracts between service providers and service consumers, guarantee the expected quality of service (QoS) to different stakeholders at various levels of a service value chain. The essential requirements for such an SLA-based choreography of services include agile component-based infrastructure to support the corresponding choreography of their SLAs;proactive validation of SLAs to prevent violations;reactive validation of SLAs for penalty enforcement and breach management;and business enabling requirements such as trust, privacy, security and automation. In this paper we highlight the significance of these issues and propose their solutions. We then proceed to weave these solution components together into a comprehensive SLA validation framework that blends together two major systems: a rule based distributed system for SLA validation and a service monitoring system for the prevention of SLA violations. © 2010 IEEE.

关键词： Quality of service

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Augmented Ontology-Based Information Retrieval System with External Open Source Resources

Augmented Ontology-Based Information Retrieval System with E...

引用

International Conference on Information Technology: New Generations (ITNG)

作者： Leyla Zhuhadar Olfa Nasraoui Department of Computer Engineering and Computer Science Knowledge Discovery and Web Mining Laboratory University of Louisville Louisville KY USA

As the number of online students has been growing significantly for the last couple of years, generic Web Information Retrieval methods have either maintained an emphasis on serving the general population, or have been lagging in integrating the power of adaptive semantics and personalization via knowledge discovery into real-life working E-learning applications. This paper is devoted to the implementation of an augmented ontology-based information retrieval system with external open-source resources from MIT OpenCourseWare that uses Protégé to design and build the structure of HyperManyMedia ontology as means of retrieving documents in two languages, English and Spanish. In addition, this paper deals with clustering the augmented documents, the outcome of the Clustering Analysis is added to the domain ontology as additional leaves under the “SubSubSubconcept = lecture”. From each cluster, we extract the Top n keywords (descriptive terms), then we modify the ontology accordingly by adding the cluster's terms as semantic terms under the “SubSubSubconcept = lecture” to which these documents belong. This research is implemented and evaluated on a real platform HyperManyMedia at Western Kentucky University.

关键词： Ontologies Information retrieval Indexing Mobile computing computer science Tree data structures Spatial databases Information technology computerized monitoring Delay effects

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Evaluating a Cross-Language Semantically Enriched Search Engine

Evaluating a Cross-Language Semantically Enriched Search Eng...

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International Conference on Information Technology: New Generations (ITNG)

作者： Leyla Zhuhadar Olfa Nasraoui Knowledge Discovery and Web Mining Laboratory Department of Computer Engineering and Computer Science University of Louisville Louisville KY USA

This paper tackles the problem of a user who is capable of reading or using documents written in a specific language, but who is not fluent enough in this specific language to use the right query terms to find the document. The design of Cross-Language Information Retrieval systems started since 1969 by Gerard Salton who enhanced his SMART system to retrieve documents in multiple languages, English and Spanish; however, the translation process is still considered to be a challenging problem. This paper is devoted to the evaluation of a Cross-Language search engine that uses Natural Language Processing techniques as a means of improving the search process of documents provided by two languages, English and Spanish. The research is implemented and evaluated on a real platform HyperManyMedia at Western Kentucky University. The implementation of the Cross-Language search engine follows a synergistic approach between (1) A Thesaurus-based Approach and (2) A Corpus-based Approach. In the case of the Thesaurus-based Approach, we use a simple bilingual listing of terms, phrases, concepts, and subconcepts where the hierarchical structure of the ontology is used to define the relationship between concepts/subconcepts. Also, we use a specific terminology that captures the domain of E-learning; those terms are associated with college name, course name, and lecture name which is presented in two languages. In the case of the Corpus-based Approach, we use the Term Vector Translation approach; the goal is to find statistical information about term usage between the two languages using techniques which map sets of term weights from English to Spanish and vice-versa.

关键词： Search engines Natural languages Information retrieval Natural language processing knowledge engineering Ontologies Speech processing knowledge representation Information technology Web mining

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Personalized Search Based on a User-Centered Recommender Engine

Personalized Search Based on a User-Centered Recommender Eng...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Leyla Zhuhadar Olfa Nasraoui Knowledge Discovery and Web Mining Laboratory Department of Computer Engineering and Computer Science University of Louisville Louisville KY USA

Designing personalized search engines based on a recommender system that takes into consideration the user situated moment in relation to the subject matter and the context that governs user interest has been largely ignored. In this paper, we present a novel approach to integrating user interests into search within a recommender system that is guided by the semantic representation of the user and the content. In addition, our research tackles two problems of creating any recommender system: (a) the initial stage problem (how to provide recommendations to a user if the system hasn't been used yet) and (b) user context (providing the same user with different recommendations based on the context of their recent activity). Also the design of our recommender system is modular. It integrates and accommodates user's preferences by using User Relevance Feedback. Finally, we describe how the personalization aspects can increase the recommendation quality.

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Towards explanation-aware social software: Applying the mining and analysis continuum of explaining

Towards explanation-aware social software: Applying the mini...

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5th International Workshop on Explanation-Aware Computing, ExaCt 2010

作者： Atzmueller, Martin Roth-Berghofer, Thomas University of Kassel Knowledge and Data Engineering Group Wilhelmshöher Allee 73 34121 Kassel Germany GmbH Trippstadter Straße 122 67663 Kaiserslautern Germany Knowledge-Based Systems Group Department of Computer Science University of Kaiserslautern P.O. Box 3049 67653 Kaiserslautern Germany

Data mining methods build patterns or models. When presenting these, all or part of the result needs to be explained to the user in order to be understandable and for increasing the user acceptance of the patterns. In doing that, a variety of dimensions in the Mining and Analysis Continuum of Explaining (MACE) needs to be considered, e.g., from concrete to more abstract explanations. This paper discusses the application of the MACE in the context of social software. We consider applications of the proposed approaches in three social software systems, and show how the data mining results can seamlessly be analysed on the presented continuous dimensions and levels.

关键词： Application programs

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An adaptive multi-heuristic ant colony system for finding optimal elimination orderings in Bayesian networks

An adaptive multi-heuristic ant colony system for finding op...

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International Conference on Electrical engineering Computing science and Automatic Control

作者： Dong, Xuchu Zhang, Yonggang Cai, Dianbo Yu, Haihong Ye, Yuxin Department of Computer Science and Technology Jilin University China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education China Network Monitor Maintenance Centre of Jilin Branch China Telecom Corporation Limited Changchun Jilin China

ISBN: (纸本)9781424473120

To find an optimal elimination ordering for Bayesian networks, a multi-heuristic-based ant colony system named MHC-HS-ACS is proposed. MHC-HS-ACS uses a set of heuristics to guide the ants to search solutions. The heuristic set can evolve with the searching procedure in an adaptive way. MHC-HS-ACS also utilizes a heuristic-based local search to accelerate its convergence. Computational experiments show that MHC-HS-ACS can find very high quality solutions. © 2010 IEEE.

关键词： Bayesian networks

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MMIR: Mining multi-scale intervention rules in sub-complex system

MMIR: Mining multi-scale intervention rules in sub-complex s...

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12th International Asia Pacific Web Conference, APWeb 2010

作者： Jiaoling, Zheng Changjie, Tang Shaojie, Qiao Ning, Yang Yue, Wang Yu, Chen Jun, Zhu Institute of Database and Knowledge Engineering School of Computer Science Sichuan University Chengdu 610065 China Department of Software Engineering Chengdu University of Information Technology Chengdu 610225 China School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China National Centre for Birth Defects Monitoring Chengdu 610041 China

ISBN: (纸本)9780769540122

Intervention analysis is the common method to reveal relationships between objects in human as well as biological society. Data mining research community is just starting to pay attention to intervention analysis. As the traditional association rules are not successful at measuring intervention, this paper tries to mining intervention rules from time series data of subcomplex system. The main contributions of this study include: (1) introduces a new concept of intervention rules. It can quantitatively detect at what scale, how intensive and how long the intervention does make sense;(2) conduct wavelet transform on time series. The decomposition scale can denotes the intervention scale;(3) proposes a new concept named directional correlation to measure intervention intensity;(4) calculates the intervention intensity between time series data with different time delays. The time delay can uncover after how long the intervention takes place;(5) conducts experiments on real datasets. The results show that intervention rules do exist at different decomposition scale of the original data. Moreover, the number of rules discovered by multi-scale analysing methods is always 4 or 5 times more than those found by single scale methods. And the stability of rules discovered by the improved multi-scale mining algorithm MMIR is always 100%, while the stability of rules discovered by the basic multi-scale mining algorithm MMIR fluctuate around 70%. © 2010 IEEE.

关键词： Time series

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Semantically Enriched Recommender Engine: A Novel Collaborative Filtering Approach Using "User-to-User Fast Xor Bit Operation"

Semantically Enriched Recommender Engine: A Novel Collaborat...

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International Conference on Semantic Computing

作者： Leyla Zhuhadar Olfa Nasraoui Robert Wyatt Knowledge Discovery and Web Mining Laboratory Department of Computer Engineering and Computer Science University of Louisville Waterford KY USA Knowledge Discovery and Web Mining Laboratory Department of Computer Engineering and Computer Science University of Louisville Louisville KY USA Office of Distance Learning Western Kentucky University KY USA

In this paper, we focus on Collaborative Filtering to provide recommendations to users that fit their profiles. We employed two methods: (1) K-Nearest Neighbors classifier, and (2) a fast implementation of Collaborative Filtering approach: “user-to-user fast XOR bit operation”. Both techniques serve the same objective, which is modifying the user's ontology profile (semantic profile). Technically, Collaborative Filtering extends the user's ontology profile based on the interests of a community of similar users. Also, we describe the implementation of the recommender system on a real platform, known as Hyper Many Media at Western Kentucky University. Finally, we evaluate the system based on Top-n-Recall and Top-n-Precision. The results show an improvement in Recall and Precision using Collaborative Filtering.

关键词： Filtering Collaboration Semantics Search engines Data mining Educational institutions Boosting

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