Non-Uniform Information Access in Collaborative Information Retrieval

2018 ◽  
Vol 51 (3) ◽  
pp. 163-164
Author(s):  
Nyi Nyi Htun
Keyword(s):  
Information Retrieval ◽  
Information Access ◽  
Collaborative Information Retrieval

scholarly journals Towards Supporting Community Information Seeking and Use

2007 ◽  
Vol 3 (1) ◽  
Author(s):  
Nkechi Nnadi ◽  
Michael Gurstein
Keyword(s):  
Information Retrieval ◽  
Information Seeking ◽  
Design Guidelines ◽  
Community Context ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Community Information ◽  
Nature Of Information ◽  
Single User ◽  
Collaborative Information Retrieval

In this paper we explore issues surrounding the design of systems that will effectively support community information seeking and use. We discuss the need for information retrieval systems to move away for the single user paradigm to one that recognizes the collaborative nature of information seeking and use. We also examine the collaborative information retrieval literature and derive implications for community informatics. The paper also explores the unique challenges of designing systems to support information seeking and use in a community context, and attempts to provide design guidelines that will enable researchers and practitioners to develop such systems.

Technologies for Information Access and Knowledge Management

2011 ◽  
pp. 3680-3685
Author(s):  
Thomas Mandl
Keyword(s):  
Information Retrieval ◽  
Search Engines ◽  
Information Access ◽  
Partial Match ◽  
Retrieval Models ◽  
Automatic Indexing ◽  
Patent Retrieval ◽  
Information Retrieval Systems ◽  
Information Work ◽  
The 1960S

In the 1960s, automatic indexing methods for texts were developed. They had already implemented the “bag-ofwords” approach, which still prevails. Although automatic indexing is widely used today, many information providers and even Internet services still rely on human information work. In the 1970s, research shifted its interest to partial-match retrieval models and proved their superiority over Boolean retrieval models. Vector-space and later probabilistic retrieval models were developed. However, it took until the 1990s for partial-match models to succeed in the market. The Internet played a great role in this success. All Web search engines were based on partial-match models and provided ranked lists as results rather than unordered sets of documents. Consumers got used to this kind of search systems, and all big search engines included partial-match functionality. However, there are many niches in which Boolean methods still dominate, for example, patent retrieval. The basis for information retrieval systems may be pictures, graphics, videos, music objects, structured documents, or combinations thereof. This article is mainly concerned with information retrieval for text documents.

Uncertain Confidence Network-Based Collaborative Information Retrieval Relevance Feedback Algorithm

2020 ◽  
pp. 20-39
Author(s):  
Fatiha Naouar ◽  
Lobna Hlaoua ◽  
Mohamed Nazih Omri
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
Information Source ◽  
Strong Association ◽  
Relevant Information ◽  
User Query ◽  
Proposed Model ◽  
Evidence Source ◽  
Feedback Algorithm ◽  
Collaborative Information Retrieval

Collaborative retrieval allows increasing the amount of relevant information found and sharing history with others. The collaborative retrieval can reduce the retrieval time performed by the users of the same profile. This chapter proposes a new relevance feedback algorithm to collaborative information retrieval based on a confidence network, which performs propagation relevance between annotations terms. The main contribution in this work is the extraction of relevant terms to reformulate the initial user query considering the annotations as an information source. The proposed model introduces the concept of necessity that allows determining the terms that have strong association relationships estimated to the measure of a confidence. Since the user is overwhelmed by a variety of contradictory annotations, another contribution consists of determining the relevant annotations for a given evidence source. The experimental study gives very encouraging results.

DemonD

2010 ◽  
pp. 652-668
Author(s):  
Charles Delalonde ◽  
Eddie Soulier
Keyword(s):  
Knowledge Management ◽  
Information Retrieval ◽  
Community Participation ◽  
Search Engine ◽  
Management Practices ◽  
Actor Network Theory ◽  
Social Search ◽  
Retrieval Systems ◽  
Knowledge Management Practices ◽  
Collaborative Information Retrieval

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.

Service-Oriented Architectures for Context-Aware Information Retrieval and Access

2010 ◽  
pp. 1549-1560
Author(s):  
Lu Yan
Keyword(s):  
Information Retrieval ◽  
Information Exchange ◽  
Exchange Process ◽  
Information Access ◽  
Mobile Service ◽  
Context Aware ◽  
Service Oriented Architectures ◽  
Information Retrieval Systems ◽  
To Come ◽  
Process Context

Humans are quite successful at conveying ideas to each other and retrieving information from interactions appropriately. This is due to many factors: the richness of the language they share, the common understanding of how the world works, and an implicit understanding of everyday situations (Dey & Abowd, 1999). When humans talk with humans, they are able to use implicit situational information (i.e., context) to enhance the information exchange process. Context (Cool & Spink, 2002) plays a vital part in adaptive and personalized information retrieval and access. Unfortunately, computer communications lacks this ability to provide auxiliary context in addition to the substantial content of information. As computers are becoming more and more ubiquitous and mobile, there is a need and possibility to provide information “personalized, any time, and anywhere” (ITU, 2006). In these scenarios, large amounts of information circulate in order to create smart and proactive environments that will significantly enhance both the work and leisure experiences of people. Context-awareness plays an important role to enable personalized information retrieval and access according to the current situation with minimal human intervention. Although context-aware information retrieval systems have been researched for a decade (Korkea-aho, 2000), the rise of mobile and ubiquitous computing put new challenges to issue, and therefore we are motivated to come up with new solutions to achieve non-intrusive, personalized information access on the mobile service platforms and heterogeneous wireless environments.

Mastering Web Mining and Information Retrieval in the Digital Age

2017 ◽  
pp. 1-28 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Information Retrieval ◽  
Organizational Performance ◽  
User Interfaces ◽  
Web Mining ◽  
Digital Age ◽  
Mass Storage ◽  
Web Data ◽  
Time Saving ◽  
Storage Devices ◽  
Collaborative Information Retrieval

This chapter aims to master web mining and Information Retrieval (IR) in the digital age, thus describing the overviews of web mining and web usage mining; the significance of web mining in the digital age; the overview of IR; the concept of Collaborative Information Retrieval (CIR); the evaluation of IR systems; and the significance of IR in the digital age. Web mining can contribute to the increase in profits by selling more products and by minimizing costs. Web mining is the application of data mining techniques to discover the interesting patterns from web data in order to better serve the needs of web-based multifaceted applications. Mining web data can improve the personalization, create the selling opportunities, and lead to more profitable relationships with customers in global business. Web mining techniques can be applied with the effective analysis of the clearly understood business needs and requirements. Web mining builds the detailed customer profiles based on the transactional data. Web mining is used to create the personalized search engines which can recognize the individuals' search queries by analyzing and profiling the web user's search behavior. IR is the process of obtaining relevant information from a collection of informational resources. IR has considerably changed with the expansion of the Internet and the advent of modern and inexpensive graphical user interfaces and mass storage devices. The effective IR system, including an active indexing system, not only decreases the chances that information will be misfiled but also expedites the retrieval of information. Regarding IR utilization, the resulting time-saving benefit increases office efficiency and productivity while decreasing stress and anxiety. Most IR systems provide the advanced searching capabilities that allow users to create the sophisticated queries. The chapter argues that applying web mining and IR has the potential to enhance organizational performance and reach strategic goals in the digital age.

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