DOCSLIB.ORG

Semantically-Enhanced Advertisement Recommender Systems in Social Networks

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Semantically-Enhanced Advertisement Recommender Systems in Social Networks DEPARTAMENTO DE INFORMATICA (COMPUTER SCIENCES DEPARTMENT) Doctoral Programme: INFORMATION TECHNOLOGY, COMMUNICATIONS AND COMPUTATIONAL MATHEMATICS SEMANTICALLY-ENHANCED ADVERTISEMENT RECOMMENDER SYSTEMS IN SOCIAL NETWORKS Doctoral Thesis by Ali Pazahr Under the supervision of Dr. J. Javier Samper Zapater Dr. Francisco García Sánchez Valencia, May 2017 D. José Javier Samper Zapater, Profesor Contratado Doctor del Departamento de Informática de la Universidad de Valencia, y D. Francisco García Sánchez, Profesor Titular de Universidad del Departamento de Informática y Sistemas de la Universidad de Murcia, CERTIFICAN QUE: La presente Tesis Doctoral original de D. Ali Pazahr titulada “SEMANTICALLY-ENHANCED ADVERTISEMENT RECOMMENDER SYSTEMS IN SOCIAL NETWORKS”, ha sido realizada bajo nuestra dirección y supervisión y, a nuestro juicio, reúne los requisitos para su lectura y obtención del grado de Doctor. Y para que así conste a los efectos oportunos, firmamos el presente certificado en Valencia, a 16 de mayo de 2017. Prof. Dr. José Javier Samper Zapater Prof. Dr. Francisco García Sánchez Index of contents INDEX OF CONTENTS Index of contents........................................................................................................................... 5 Index of figures .............................................................................................................................. 9 Index of tables ............................................................................................................................. 11 Acknowledgements ..................................................................................................................... 13 Overview ..................................................................................................................................... 15 SECTION I: Introduction ............................................................................................................... 19 Chapter 1 ..................................................................................................................................... 21 1. Introduction: approach to the problem .............................................................................. 21 1.1 Identification of the problem ......................................................................................... 21 1.2 Motivation ...................................................................................................................... 26 1.3 Research Questions ........................................................................................................ 27 1.4 Objectives ....................................................................................................................... 27 1.5 Methodology of research, work plan ............................................................................. 29 1.6 Summary ........................................................................................................................ 32 SECTION II: Theoretical foundations, technologies and state of the art .................................... 35 Chapter 2 ..................................................................................................................................... 37 2. Definitions (Literature Study) .............................................................................................. 37 2.1 Introduction ................................................................................................................... 37 2.2 Semantic Web ................................................................................................................ 39 2.2.1 Purpose ................................................................................................................ 39 2.2.2 Standards ............................................................................................................. 42 2.2.3 Projects ................................................................................................................ 44 2.2.4 Common Semantic Web technologies and terms ............................................... 45 2.2.5 Other aspect to the Semantic Web ..................................................................... 47 2.2.6 Ontology .............................................................................................................. 48 2.3 Social web....................................................................................................................... 51 2.3.1 The Evolving Social Web ...................................................................................... 53 2.3.2 From the Social Web to Real Life ........................................................................ 55 2.3.3 Social Networks ................................................................................................... 57 2.4 Social Semantic Web ...................................................................................................... 63 2.4.1 Users Semantic Profiles ....................................................................................... 67 2.4.2 Frameworks for Social Networks ........................................................................ 67 2.5 Online Promoting ........................................................................................................... 81 2.5.1 Online advertisement .......................................................................................... 81 5 Index of contents 2.5.2 Competitive benefit over Customary Promoting ................................................ 81 2.5.3 Semantic advertising ........................................................................................... 81 2.5.4 Social Network Advertising ................................................................................. 82 2.6 Recommender Systems .................................................................................................. 82 2.6.1 About recommendation systems ........................................................................ 82 2.6.2 Five Problems of Recommender Systems ........................................................... 83 2.6.3 Role of Social Networks in recommendation systems ........................................ 85 2.6.4 Aim of Recommender Systems ........................................................................... 85 2.6.5 Recommendation Techniques ............................................................................. 89 2.6.6 Role of recommender systems in E-commerce .................................................. 95 2.6.7 Limitations of classical approaches ..................................................................... 95 2.7 Related works ................................................................................................................. 96 2.8 Summary ...................................................................................................................... 105 SECTION III: Proposals and contributions.................................................................................. 107 Chapter 3 ................................................................................................................................... 109 3. Methodology ..................................................................................................................... 109 3.1 Introduction ................................................................................................................. 109 3.2 The Framework ............................................................................................................ 110 3.2.1 Preliminary User Activity ................................................................................... 113 3.2.2 Semantic Recommendation Engine .................................................................. 113 3.3 Case study .................................................................................................................... 118 3.4 Client application ......................................................................................................... 120 3.5 A Test Case ................................................................................................................... 124 3.6 Dataset ......................................................................................................................... 125 3.7 Programming Language ............................................................................................... 127 3.8 Efficiency ...................................................................................................................... 127 3.9 The Security of the Framework .................................................................................... 128 3.9.1 On the level of user entry .................................................................................. 129 3.9.2 On the level of user activity............................................................................... 129 3.10 Summary .................................................................................................................. 132 SECTION IV: Evaluation.............................................................................................................. 135 Chapter 4 ................................................................................................................................... 137 4. Evaluation .........................................................................................................................
Load more

Recommended publications
  • Data Warehouse: an Integrated Decision Support Database Whose Content Is Derived from the Various Operational Databases
    1 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in DATABASE MANAGEMENT Topic Objective: At the end of this topic student will be able to: Understand the Contrasting basic concepts Understand the Database Server and Database Specified Understand the USER Clause Definition/Overview: Data: Stored representations of objects and events that have meaning and importance in the users environment. Information: Data that have been processed in such a way that they can increase the knowledge of the person who uses it. Metadata: Data that describes the properties or characteristics of end-user data and the context of that data. Database application: An application program (or set of related programs) that is used to perform a series of database activities (create, read, update, and delete) on behalf of database users. WWW.BSSVE.IN Data warehouse: An integrated decision support database whose content is derived from the various operational databases. Constraint: A rule that cannot be violated by database users. Database: An organized collection of logically related data. Entity: A person, place, object, event, or concept in the user environment about which the organization wishes to maintain data. Database management system: A software system that is used to create, maintain, and provide controlled access to user databases. www.bsscommunitycollege.in www.bssnewgeneration.in www.bsslifeskillscollege.in 2 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in Data dependence; data independence: With data dependence, data descriptions are included with the application programs that use the data, while with data independence the data descriptions are separated from the application programs. Data warehouse; data mining: A data warehouse is an integrated decision support database, while data mining (described in the topic introduction) is the process of extracting useful information from databases.
    [Show full text]
  • Bi-Directional Transformation Between Normalized Systems Elements and Domain Ontologies in OWL
    Bi-directional Transformation between Normalized Systems Elements and Domain Ontologies in OWL Marek Suchanek´ 1 a, Herwig Mannaert2, Peter Uhnak´ 3 b and Robert Pergl1 c 1Faculty of Information Technology, Czech Technical University in Prague, Thakurova´ 9, Prague, Czech Republic 2Normalized Systems Institute, University of Antwerp, Prinsstraat 13, Antwerp, Belgium 3NSX bvba, Wetenschapspark Universiteit Antwerpen, Galileilaan 15, 2845 Niel, Belgium Keywords: Ontology, Normalized Systems, Transformation, Model-driven Development, Ontology Engineering, Software Modelling. Abstract: Knowledge representation in OWL ontologies gained a lot of popularity with the development of Big Data, Artificial Intelligence, Semantic Web, and Linked Open Data. OWL ontologies are very versatile, and there are many tools for analysis, design, documentation, and mapping. They can capture concepts and categories, their properties and relations. Normalized Systems (NS) provide a way of code generation from a model of so-called NS Elements resulting in an information system with proven evolvability. The model used in NS contains domain-specific knowledge that can be represented in an OWL ontology. This work clarifies the potential advantages of having OWL representation of the NS model, discusses the design of a bi-directional transformation between NS models and domain ontologies in OWL, and describes its implementation. It shows how the resulting ontology enables further work on the analytical level and leverages the system design. Moreover, due to the fact that NS metamodel is metacircular, the transformation can generate ontology of NS metamodel itself. It is expected that the results of this work will help with the design of larger real-world applications as well as the metamodel and that the transformation tool will be further extended with additional features which we proposed.
    [Show full text]
  • A Survey and Classification of Controlled Natural Languages
    A Survey and Classification of Controlled Natural Languages ∗ Tobias Kuhn ETH Zurich and University of Zurich What is here called controlled natural language (CNL) has traditionally been given many different names. Especially during the last four decades, a wide variety of such languages have been designed. They are applied to improve communication among humans, to improve translation, or to provide natural and intuitive representations for formal notations. Despite the apparent differences, it seems sensible to put all these languages under the same umbrella. To bring order to the variety of languages, a general classification scheme is presented here. A comprehensive survey of existing English-based CNLs is given, listing and describing 100 languages from 1930 until today. Classification of these languages reveals that they form a single scattered cloud filling the conceptual space between natural languages such as English on the one end and formal languages such as propositional logic on the other. The goal of this article is to provide a common terminology and a common model for CNL, to contribute to the understanding of their general nature, to provide a starting point for researchers interested in the area, and to help developers to make design decisions. 1. Introduction Controlled, processable, simplified, technical, structured,andbasic are just a few examples of attributes given to constructed languages of the type to be discussed here. We will call them controlled natural languages (CNL) or simply controlled languages.Basic English, Caterpillar Fundamental English, SBVR Structured English, and Attempto Controlled English are some examples; many more will be presented herein. This article investigates the nature of such languages, provides a general classification scheme, and explores existing approaches.
    [Show full text]
  • A Conceptual Framework for Constructing Distributed Object Libraries Using Gellish
    A Conceptual Framework for Constructing Distributed Object Libraries using Gellish Master's Thesis in Computer Science Michael Rudi Henrichs [email protected] Parallel and Distributed Systems group Faculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology June 1, 2009 Student Michael Rudi Henrichs Studentnumber: 9327103 Oranjelaan 8 2264 CW Leidschendam [email protected] MSc Presentation June 2, 2009 at 14:00 Lipkenszaal (LB 01.150), Faculty EWI, Mekelweg 4, Delft Committee Chair: Prof. Dr. Ir. H.J. Sips [email protected] Member: Dr. Ir. D.H.J. Epema [email protected] Member: Ir. N.W. Roest [email protected] Supervisor: Dr. K. van der Meer [email protected] Idoro B.V. Zonnebloem 52 2317 LM Leiden The Netherlands Parallel and Distributed Systems group Department of Software Technology Faculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology Mekelweg 4 2826 CD Delft The Netherlands www.ewi.tudelft.nl Sponsors: This master's thesis was typeset with MiKTEX 2.7, edited on TEXnicCenter 1 beta 7.50. Illustrations and diagrams were created using Microsoft Visio 2003 and Corel Paint Shop Pro 12.0. All running on an Acer Aspire 6930. Copyright c 2009 by Michael Henrichs, Idoro B.V. Cover photo and design by Michael Henrichs c 2009 http:nnphoto.lemantle.com All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without the prior permission of the author.
    [Show full text]
  • Automated Ontology Creation Using XML Schema Elements
    Automated Ontology Creation using XML Schema Elements Samuel Suhas Singapogu, Paulo C. G. Costa, J. Mark Pullen C4I center, George Mason University Fairfax, VA, USA [ssingapo,pcosta,mpullen]@c4i.gmu.edu Abstract—Ontologies are commonly used to represent formal semantics in a computer system, usually capturing them in the Although ontologies are valuable assets in system model- form of concepts, relationships and axioms. Axioms convey ing, testing and analysis, the process of manually creating an asserted knowledge and support inferring new knowledge ontology for a complex system is inherently tedious and through logical reasoning. For complex systems, the process of error prone. Existing methods of knowledge discovery and creating ontologies manually can be tedious and error-prone. ontology creation usually are based on text mining of a data Many automated methods of knowledge discovery are based on mining domain text corpus, but current state-of-the-art meth- corpus for that domain. XML-based systems capture the ods using this approach fail to consider properly semantic data structure and syntax of all necessary and meaningful ele- embedded in XML schemata in complex systems. This paper ments in a XML schema, which therefore is a useful starting proposes a mapping method for identifying relevant semantic point for semantic analysis. In such systems, XML schemata data in XML schemata, automatically structuring and repre- have been shown to be a valuable resource of semantic data senting it in the form of a draft ontology. Concepts, concept [2]. Command and Control systems in the military context hierarchy and domain relationships from XML schema are support various functions, including commanding of forces mapped to relevant parts of an OWL ontology.
    [Show full text]
  • Data Models for Home Services
    __________________________________________PROCEEDING OF THE 13TH CONFERENCE OF FRUCT ASSOCIATION Data Models for Home Services Vadym Kramar, Markku Korhonen, Yury Sergeev Oulu University of Applied Sciences, School of Engineering Raahe, Finland {vadym.kramar, markku.korhonen, yury.sergeev}@oamk.fi Abstract An ultimate penetration of communication technologies allowing web access has enriched a conception of smart homes with new paradigms of home services. Modern home services range far beyond such notions as Home Automation or Use of Internet. The services expose their ubiquitous nature by being integrated into smart environments, and provisioned through a variety of end-user devices. Computational intelligence require a use of knowledge technologies, and within a given domain, such requirement as a compliance with modern web architecture is essential. This is where Semantic Web technologies excel. A given work presents an overview of important terms, vocabularies, and data models that may be utilised in data and knowledge engineering with respect to home services. Index Terms: Context, Data engineering, Data models, Knowledge engineering, Semantic Web, Smart homes, Ubiquitous computing. I. INTRODUCTION In recent years, a use of Semantic Web technologies to build a giant information space has shown certain benefits. Rapid development of Web 3.0 and a use of its principle in web applications is the best evidence of such benefits. A traditional database design in still and will be widely used in web applications. One of the most important reason for that is a vast number of databases developed over years and used in a variety of applications varying from simple web services to enterprise portals. In accordance to Forrester Research though a growing number of document, or knowledge bases, such as NoSQL is not a hype anymore [1].
    [Show full text]
  • Universidad Carlos III De Madrid Escuela Politécnica Superior
    Universidad Carlos III de Madrid Escuela Politécnica Superior Ingeniería en Informática Proyecto Fin de Carrera DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Autor: Verónica Casado Manzanero Tutor: Anabel Fraga Vázquez Diciembre, 2009 DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Agradecimientos En primer lugar querría dar las gracias a mis padres, por su apoyo, comprensión, generosidad, por darme todo lo que necesito y más, y sobre todo, por enseñarme a ser como soy y servirme de ejemplo para convertirme en mejor persona cada día. También me gustaría recordar a mi hermana por ayudarme siempre en lo he necesitado con esa gran dosis de paciencia que sé que ha de tener. A mis amigas Carol y Raquel, por permanecer siempre a mi lado a pesar de estar semanas sin vernos. A mi novio Marcos, por pasarme esa paciencia y tranquilidad suya que tanto aprecio, por apoyarme en todo momento, por creer en mí y por permanecer a mi lado durante estos seis largos años. Por último, agradecerle a mi tutora Anabel toda la ayuda prestada, tanto en las asignaturas como en este proyecto. Gracias a su inestimable ayuda y entusiasmo he podido completar con éxito el trabajo aquí propuesto. Ha sido un verdadero placer trabajar con ella. Verónica Casado Manzanero 3/254 Universidad Carlos III de Madrid DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Verónica Casado Manzanero 4/254 Universidad Carlos III de Madrid DISEÑO DE UN MUNDO VIRTUAL PARA LA ENSEÑANZA DE ARQUITECTURA SOFTWARE Contenido 1. Introducción y motivación .........................................................................................
    [Show full text]
  • Ontology Languages – a Review
    International Journal of Computer Theory and Engineering, Vol.2, No.6, December, 2010 1793-8201 Ontology Languages – A Review V. Maniraj, Dr.R. Sivakumar 1) Logical Languages Abstract—Ontologies have been becoming a hot research • First order predicate logic topic for the application in artificial intelligence, semantic web, Software Engineering, Library Science and information • Rule based logic Architecture. Ontology is a formal representation of set of concepts within a domain and relationships between those • concepts. It is used to reason about the properties of that Description logic domain and may be used to define the domain. An ontology language is a formal language used to encode the ontologies. A 2) Frame based Languages number of research languages have been designed and released • Similar to relational databases during the past few years by the research community. They are both proprietary and standard based. In this paper a study has 3) Graph based Languages been reported on the different features and issues of these • languages. This paper also addresses the challenges for Semantic network research community in the further development of ontology languages. • Analogy with the web is rationale for the semantic web I. INTRODUCTION Ontology engineering (or ontology building) is a subfield II. BACKGROUND of knowledge engineering that studies the methods and CycL1 in computer science and artificial intelligence is an methodologies for building ontologies. It studies the ontology language used by Doug Lenat’s Cye artificial ontology development process, the ontology life cycle, the intelligence project. Ramanathan V. Guna was instrumental methods and methodologies for building ontologies, and the in the design of the language.
    [Show full text]
  • Transforming Recursive Relationships to OWL2 Components
    (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 10, 2019 Ontology Learning from Relational Databases: Transforming Recursive Relationships to OWL2 Components 1 Mohammed Reda CHBIHI LOUHDI Hicham BEHJA2 Research Laboratory on computer science innovation (LRII) LRI - Laboratory Faculty of Science Aïn Chock Casablanca ENSEM, Hassan II University Hassan II University, Casablanca, Morocco Casablanca, Morocco Abstract—Relational databases (RDB) are widely used as a ontologies provide classes, properties, individuals, and data backend for information systems, and contain interesting values and are stored as Semantic Web documents [3]. structured data (schema and data). In the case of ontology learning, RDB can be used as knowledge source. Multiple There are many approaches that transform a RDB to an approaches exist for building ontologies from RDB. They mainly ontology. Three main techniques are used: (1) Reverse use schema mapping to transform RDB components to Engineering, to convert the relational model to the conceptual ontologies. Most existing approaches do not deal with recursive model (which is considered as semantically richer than the relationships that can encapsulate good semantics. In this paper, relational model) or to retrieve lost information during the two technics are proposed for transforming recursive transformation of the conceptual model to the relational model, relationships to OWL2 components: (1) Transitivity mechanism (2) Schema Mapping, to convert relational model components and (2) Concept Hierarchy. The main objective of this work is to to ontology components, through the use of transformation build richer ontologies with deep taxonomies from RDB. rules and (3) Data Mining to analyze stored data in order to enrich the ontology.
    [Show full text]
  • Ontology Life Cycle: a Survey on the Ontology and Its Development Steps
    International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2015): 78.96 | Impact Factor (2015): 6.391 Ontology Life Cycle: A Survey on the Ontology and its Development Steps Eiman Alsiddig Altayeb Ibrahim1, Mohammed Awad Mohammed Ataelfadiel2 1Sudan University of Science and Technology, College of Computer Science and Information Technology, Khartoum – Sudan 2Computer Science dept. AL-Imam MuhommedIbn Saud Islamic University, AL-Ahsa, Kingdom of Saudi Arabia Abstract: In the last decades, the turnout has become great for the use of ontology in various fields, such as web technologies, database integration, multi agent systems, natural language processing, etc... The main goal of this paper is to answer generic questions about ontologies, such as: What is the ontology? Which are the different Types of ontologies? What is the purpose of the use of ontologies in an application? Which methods can I use to build ontology? During the paper we discussed the definition of Ontology, types of ontologies (Formal and Informal ontology) and its Component.Also, highlighted some of the languages used in the construction of the Ontologies beside the ontology Building Tools. In addition, we listed the agreed steps for building the Ontology. In conclusion, we did not overlook to mention some of the Ontology projects specifically in Holy Quran Domain. Keywords: Ontology Definition, Ontology Types, Ontology Building Tools, Ontology languages, Ontology components, Ontology Engineering 1. Introduction 2. Ontology Definition Historically, ontologies arise out of the branch of philosophy There are many interpretations about what ontology is. In fact, known as metaphysics, which deals with the nature of reality – hot discussions are often done in many meetings on ontology.
    [Show full text]
  • An Approach to Formalizing Ontology Driven Semantic Integration: Concepts, Dimensions and Framework Wenlong Gao
    Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2012 An Approach to Formalizing Ontology Driven Semantic Integration: Concepts, Dimensions and Framework Wenlong Gao Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY COLLEGE OF COMMUNICATION AND INFORMATION AN APPROACH TO FORMALIZING ONTOLOGY DRIVEN SEMANTIC INTEGRATION: CONCEPTS, DIMENSIONS AND FRAMEWORK By WENLONG GAO A Dissertation submitted to the School of Library and Information Studies In partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree Awarded: Spring Semester, 2012 Wenlong Gao defended this dissertation on December 9, 2011 The members of the supervisory committee were: Corinne Jörgensen Professor Directing Dissertation Daniel Schwartz University Representative Ian Douglas Committee Member Besiki Stvilia Committee Member The Graduate School has verified and approved the above-named committee members and certifies that the dissertation has been approved in accordance with university requirements. ii For my parents, I could not have done this without you. iii ACKNOWLEDGEMENTS This dissertation could not have been completed without the tremendous support and help of my dissertation committee, friends and family. First, I would like to thank Dr. Corinne Jörgensen, my dissertation chair and mentor. You supported me through this entire process, and encouraged me whenever I encountered challenges. I will always appreciate the trust and confidence you placed in me. It has been an honor and privilege to be your doctoral student. Second, I am thankful for other committee members, Dr. Ian Douglas, Dr. Besiki Stvilia, Dr. Daniel Schwartz, who inspired me not only throughout the dissertation development but also though my personal academic development.
    [Show full text]
  • Ontology-Based Design of Space Systems
    Ontology-Based Design of Space Systems Christian Hennig 1, Alexander Viehl 2, Benedikt Kämpgen 2, and Harald Eisenmann 1 1Airbus Defence and Space, Space Systems, Friedrichshafen, Germany {christian.hennig,harald.eisenmann}@airbus.com 2FZI Research Center for Information Technology, Karlsruhe, Germany {viehl,kaempgen}@fzi.de Abstract. In model-based systems engineering a model specifying the system's design is shared across a variety of disciplines and used to ensure the consisten- cy and quality of the overall design. Existing implementations for describing these system models exhibit a number of shortcomings regarding their approach to data management. In this emerging applications paper, we present the appli- cation of an ontology for space system design that provides increased semantic soundness of the underlying standardized data specification, enables reasoners to identify problems in the system, and allows the application of operational knowledge collected over past projects to the system to be designed. Based on a qualitative evaluation driven by data derived from an actual satellite design pro- ject, a reflection on the applicability of ontologies in the overall model-based systems engineering approach is pursued. Keywords: Space Systems, Systems Engineering, MBSE, ECSS-E-TM-10-23, Conceptual Data Model, OWL, Reasoning. 1 Introduction The industrial setting for producing systems to be deployed in space, such as satel- lites, launch vehicles, or science spacecraft, involves a multitude of engineering disci- plines. Each involved discipline has its own view on the system to be built, along with its own models, based on its own model semantics. For forming a consistent picture of the system, information from all relevant discipline-specific models is integrated towards an interdisciplinary system model, forming the practice of model-based sys- tems engineering (MBSE).
    [Show full text]