Social Information Retrieval Based on Semantic Annotation and Hashing upon the Multiple Ontologies

The explosion of web 2.0 and social networks has created an enormous and rewarding source of information that has motivated researchers in different fields to exploit it. Our work revolves around the issue of access and identification of social information and their use in building a user profile enriched with a social dimension, and operating in a process of personalization and recommendation. We study several approaches of Social IR (Information Retrieval), distinguished by the type of incorporated social information. We also study various social recommendation approaches classified by the type of recommendation. We then present a study of techniques for modeling the social user profile dimension, followed by a critical discussion. Thus, we propose our social recommendation approach integrating an advanced social user profile model. L’explosion du web 2.0 et des réseaux sociaux a crée une source d’information énorme et enrichissante qui a motivé les chercheurs dans différents domaines à l’exploiter. Notre travail s’articule autour de la problématique d’accès et d’identification des informations sociales et leur exploitation dans la construction d’un profil utilisateur enrichi d’une dimension sociale, et son exploitation dans un processus de personnalisation et de recommandation. Nous étudions différentes approches sociales de RI (Recherche d’Information), distinguées par le type d’informations sociales incorporées. Nous étudions également diverses approches de recommandation sociale classées par le type de recommandation. Nous exposons ensuite une étude des techniques de modélisation de la dimension sociale du profil utilisateur, suivie par une discussion critique. Ainsi, nous présentons notre approche de recommandation sociale proposée intégrant un modèle avancé de profil utilisateur social.

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Social Information Retrieval Systems

10.4018/978-1-59904-543-6 ◽

2008 ◽

Cited By ~ 7

Keyword(s):

Information Retrieval ◽

Social Information ◽

Retrieval Systems ◽

Information Retrieval Systems

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The Ethics of Social Information Retrieval

Social Information Retrieval Systems ◽

10.4018/978-1-59904-543-6.ch010 ◽

2011 ◽

pp. 179-188

Author(s):

Brendan Luyt ◽

Chu Keong Lee

Keyword(s):

Information Retrieval ◽

Social Networking ◽

Ethical Issues ◽

Social Information ◽

Democratic Society ◽

Technological Systems ◽

Social Diversity ◽

Technological Design ◽

The Social ◽

Social And Ethical Issues

In this chapter we discuss some of the social and ethical issues associated with social information retrieval. Using the work of Habermas we argue that social networking is likely to exacerbate already disturbing trends towards the fragmentation of society and a corresponding decline reduction in social diversity. Such a situation is not conducive to developing a healthy, democratic society. Following the tradition of critical theorists of technology, we conclude with a call for responsible and aware technological design with more attention paid to the values embedded in new technological systems.

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An automatic learning for re-ranking in social information retrieval

2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA ◽

10.1109/smap49528.2020.9248437 ◽

2020 ◽

Author(s):

Rihab Haddad ◽

Lobna Hlaoua

Keyword(s):

Information Retrieval ◽

Social Information ◽

Automatic Learning

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Fish Ontology framework for taxonomy-based fish recognition

PeerJ ◽

10.7717/peerj.3811 ◽

2017 ◽

Vol 5 ◽

pp. e3811 ◽

Cited By ~ 4

Author(s):

Najib M. Ali ◽

Haris A. Khan ◽

Amy Y-Hui Then ◽

Chong Ving Ching ◽

Manas Gaur ◽

...

Keyword(s):

Information Retrieval ◽

Fish Species ◽

Life Science ◽

Unique Feature ◽

Semantic Annotation ◽

Automated Classification ◽

Current Version ◽

Fish Taxonomy ◽

Fisheries Resources ◽

Indispensable Tool

Life science ontologies play an important role in Semantic Web. Given the diversity in fish species and the associated wealth of information, it is imperative to develop an ontology capable of linking and integrating this information in an automated fashion. As such, we introduce the Fish Ontology (FO), an automated classification architecture of existing fish taxa which provides taxonomic information on unknown fish based on metadata restrictions. It is designed to support knowledge discovery, provide semantic annotation of fish and fisheries resources, data integration, and information retrieval. Automated classification for unknown specimens is a unique feature that currently does not appear to exist in other known ontologies. Examples of automated classification for major groups of fish are demonstrated, showing the inferred information by introducing several restrictions at the species or specimen level. The current version of FO has 1,830 classes, includes widely used fisheries terminology, and models major aspects of fish taxonomy, grouping, and character. With more than 30,000 known fish species globally, the FO will be an indispensable tool for fish scientists and other interested users.

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