DemonD: A Social Search Engine Built Upon the Actor-Network Theory

DemonD: A Social Search Engine Built Upon the Actor-Network Theory

Charles Delalonde (EDF R&D, France) and Eddie Soulier (Université de Technologie de Troyes, France)
DOI: 10.4018/978-1-60566-306-7.ch008
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Abstract

This research leverages information retrieval activity in order to build a network of organizational expertise in a distributed R&D laboratory. The authors describe traditional knowledge management practices and review post-cognitivists theories in order to define social creation in collaborative information retrieval activity. The Actor-Network theory accurately describes association processes and includes both human and non-human entities. This chapter compares this theory with the emergence of Social Search services online and Experts’ Retrieval Systems. The chapter authors suggest afterward, a social search engine named DemonD that identifies documents but more specifically users relevant to a query. DemonD relies on transparent profile construction based upon user activity, community participation, and shared documents. Individuals are invited to participate in a dedicated newsgroup and the information exchanged is capitalized. The evaluation of our service both ergonomic and through a simulation provides encouraging data.
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Introduction And Context Description

During the early years of the Personal Computer research, two rather distinctive philosophical approaches competed. Artificial Intelligence believers wished to replace humans by machine whereas Human Intelligence Augmentation project, led by Douglas Engelbart, envisioned computers as a technology to augment human mind and eventually network each other’s (Markoff, 2005).

This debate is still vibrant in the Information Retrieval community where the algorithmic approach is recently challenged by human approaches leveraging individual's social capital to identify pertinent knowledge sources. Our work contributes to this “Social Search movement”, in a corporate environment, and identifies the challenges of a Research and Development laboratory, of 80 persons, in a French telecommunication company. The laboratory observed is distributed, in France, among three cities: Grenoble, Sophia Antipolis and Caen. Its mission is to plan, conceive and support the production of original telecommunication services for businesses. This process involves each distributed team of the laboratory. Ideas are suggested utilizing an email discussion list. Then, marketing teams identify a potential market. Business development teams confirm the financial opportunities of such project. When validated by the steering committee, the service is prototyped and developed. A partner company usually accepts to experiment the service. Business units of the telecommunication company might then decide to market this innovation. In such context, cooperation is a necessity. Teams must be well coordinated to remain creative in order to shorten the time to market of the services. Information Retrieval being a critical task for laboratory members, the company previously attempted two strategies in order to create and share organizational information in a distributed context.

First, they produced an exhaustive knowledge database, trying to externalize and share explicit knowledge. Intranet's folders were also utilized to share content among coworkers. Yet, interviewed employees revealed that the knowledge database was usually obsolete and shared folders not accessible (privileges needed to be granted on each folder) and content was not properly indexed.

Second, the organization, conscious about the shortcomings of a systemic approach of knowledge management, deployed communities of practice (Wenger, 1998). The 'not-so-informal' communities shared a virtual collaborative workplace and face to face member's meetings were scheduled monthly. Yet, this second strategy also turned out to be unsatisfactory. Employees were reluctant to ask/share information with individuals they had never met.

Unlike content, which is perishable and quickly becomes obsolete, experts’ informal networks are rather permanent in R&D context. We assert that the real value of information systems is connecting people to people and encouraging them to share their expertise rather than collecting and storing de-contextualized information. (Hertzum & Pejtersen, 2000) already evidenced that individual looking for information usually explore and contact personal communications prior to using documents or knowledge bases. Following this strategy and in order to identify pertinent individuals, we need to evaluate their relevance on a specific subject along with social indicators. Thus, we leverage transparent user's profile modeling techniques to match a knowledge demand with one or many knowledge offers (Delalonde & Soulier, 2007). Relying on Bruno Latour and Michel Callon Actor Network Theory (named ANT throughout this article) our objective is then to validate a hybrid information retrieval model. This model helps specifying DemonD (Demand&responD) a search engine dedicated to collaborative information retrieval and favoring the emergence of a lightly structured information network.

The remainder of this paper is structured as follows. In section “Actor network theory in information retrieval activity” we present Actor Network Theory and its application in Information Retrieval. In section “Related works on social search” we review related work on Social Search. Section “DemonD a social search engine” describes DemonD's specifications. Section “Evaluation” is shared between a simulation of DemonD and its ergonomic evaluation. Section “Conclusion and future works” finally concludes our work with a discussion of future directions for research in this area.

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Table of Contents
Foreword
Bamshad Mobasher
Acknowledgment
Max Chevalier, Christine Julien, Chantal Soule-Dupuy
Chapter 1
Laurent Candillier, Kris Jack, Françoise Fessant, Frank Meyer
The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative... Sample PDF
State-of-the-Art Recommender Systems
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Chapter 2
Neal Lathia
Recommender systems generate personalized content for each of its users, by relying on an assumption reflected in the interaction between people... Sample PDF
Computing Recommendations with Collaborative Filtering
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Chapter 3
Edwin Simpson, Mark H. Butler
The increasing amount of available information has created a demand for better, more automated methods of finding and organizing different types of... Sample PDF
Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling
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Chapter 4
Adaptive User Profiles  (pages 65-87)
Steve Cayzer, Elke Michlmayr
A major opportunity for collaborative knowledge management is the construction of user models which can be exploited to provide relevant... Sample PDF
Adaptive User Profiles
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Chapter 5
Eugene Santos Jr., Hien Nguyen
In this chapter, we study and present our results on the problem of employing a cognitive user model for Information Retrieval (IR) in which a... Sample PDF
Modeling Users for Adaptive Information Retrieval by Capturing User Intent
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Chapter 6
Mihaela Brut, Florence Sedes, Corinne Zayani
Inside the e-learning platforms, it is important to manage the user competencies profile and to recommend to each user the most suitable documents... Sample PDF
Ontology-Based User Competencies Modeling for E-Learning Recommender Systems
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Chapter 7
Colum Foley, Alan F. Smeaton, Gareth J.F. Jones
Traditionally information retrieval (IR) research has focussed on a single user interaction modality, where a user searches to satisfy an... Sample PDF
Combining Relevance Information in a Synchronous Collaborative Information Retrieval Environment
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Chapter 8
Charles Delalonde, Eddie Soulier
This research leverages information retrieval activity in order to build a network of organizational expertise in a distributed R&D laboratory. The... Sample PDF
DemonD: A Social Search Engine Built Upon the Actor-Network Theory
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Chapter 9
Hager Karoui
In this chapter, the authors propose a case-based reasoning recommender system called COBRAS: a Peer-to-Peer (P2P) bibliographical reference... Sample PDF
COBRAS: Cooperative CBR Bibliographic Recommender System
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Chapter 10
Zehra Cataltepe, Berna Altinel
As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative... Sample PDF
Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity
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Chapter 11
Nima Taghipour, Ahmad Kardan
Information overload is no longer news; the explosive growth of the Internet has made this issue increasingly serious for Web users. Recommender... Sample PDF
Web Content Recommendation Methods Based on Reinforcement Learning
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Chapter 12
Angela Carrillo-Ramos, Manuele Kirsch Pinheiro, Marlène Villanova-Oliver, Jérôme Gensel, Yolande Berbers
The authors of this chapter present a two-fold approach for adapting content information delivered to a group of mobile users. This approach is... Sample PDF
Collaborating Agents for Adaptation to Mobile Users
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Chapter 13
Cristina Gena, Liliana Ardissono
This chapter describes the user-centered design approach we adopted in the development and evaluation of an adaptive Web site. The development of... Sample PDF
A User-Centered Approach to the Retrieval of Information in an Adaptive Web Site
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Chapter 14
Antonella Carbonaro, Rodolfo Ferrini
Active learning is the ability of learners to carry out learning activities in such a way that they will be able to effectively and efficiently... Sample PDF
Personalized Information Retrieval in a Semantic-Based Learning Environment
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Chapter 15
Hanh Huu Hoang, Tho Manh Nguyen, A Min Tjoa
Formulating unambiguous queries in the Semantic Web applications is a challenging task for users. This article presents a new approach in guiding... Sample PDF
A Semantic Web Based Approach for Context-Aware User Query Formulation and Information Retrieval
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About the Contributors