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2011 | Book

State-of-the-Art in Group Recommendation and New Approaches for Automatic Identification of Groups Read chapter Read first chapter

Information Retrieval and Mining in Distributed Environments

Editors: Alessandro Soro, Eloisa Vargiu, Giuliano Armano, Gavino Paddeu

Publisher: Springer Berlin Heidelberg

Book Series : Studies in Computational Intelligence

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

At DART'09, held in conjunction with the 2009 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2009) and Intelligent Agent Technology (IAT 2009) in Milan (Italy), practitioners and researchers working on pervasive and intelligent access to web services and distributed information retrieval met to compare their work ad insights in such fascinating topics. Extended and revised versions of their papers, together with selected and invited original contributions, are collected in this book. Topics covered are those that emerged at DART'09 as the most intriguing and challenging: (i) community oriented tools and techniques as infrastructure of the Web 2.0; (ii) agent technology applied to virtual world scenarios; (iii) context aware information retrieval; (iv) content based information retrieval; and (v) industrial applications of information retrieval. Every chapter, before discussing in depth the specific topic, presents a comprehensive review of related work and state of the art, in the hope of this volume to be of use in the years to come, to both researchers and students.

Table of Contents

Frontmatter
State-of-the-Art in Group Recommendation and New Approaches for Automatic Identification of Groups
Abstract
Recommender systems are important tools that provide information items to users, by adapting to their characteristics and preferences. Usually items are recommended to individuals, but there are contexts in which people operate in groups. To support the recommendation process in social activities, group recommender systems were developed. Since different types of groups exist, group recommendation should adapt to them, managing heterogeneity of groups. This chapter will present a survey of the state-of-the-art in group recommendation, focusing on the type of group each system aims to. A new approach for group recommendation is also presented, able to adapt to technological constraints (e.g., bandwidth limitations), by automatically identifying groups of users with similar interests.
Ludovico Boratto, Salvatore Carta
Reputation-Based Trust Diffusion in Complex Socio-Economic Networks
Abstract
Trust and reputation form the foundation of most human interactions, they are ubiquitous in everyday life. Over the past years, attempts have been made to model trust relations computationally, either to assist users or for modeling purposes in multi-agent systems. As a fundamentally social phenomenon, trust forms, operates on and changes social networks, an aspect not investigated in detail so far. In this chapter, we aim to investigate how the nature of social networks, such as their quality of being highly clustered, impacts the spread and thus the availability of data to agents. Furthermore, we will propose an extension to state-of-the art trust frameworks that leverages the capabilities of information spreading in complex networks by decoupling the provisioning process of reputation information from non-neighboring recommenders.
Sascha Hauke, Martin Pyka, Markus Borschbach, Dominik Heider
From Unstructured Web Knowledge to Plan Descriptions
Abstract
Automated Planning (AP) is an AI field whose goal is to automatically generate sequence of actions that solve problems. One of the main difficulties in its extensive use in real-world application lies in the fact that it requires the careful and error-prone process of defining a declarative domain model. This is usually performed by planning experts who should know about both the domain in hand, and the planning techniques (including sometimes the inners of these techniques or the tools that implement them). In order planning to be widely used, this process should be performed by non-planning experts. On the other hand, in many domains there are plenty of electronic documents (including the Web) that describe processes or plans in a semi-structured way. These descriptions mix natural language and certain templates for that specific domain. One such examples is the www.WikiHow.com web site that includes plans in many domains, all plans described through a set of common templates. In this work, we present a suite of tools that automatically extract knowledge from those unstructured descriptions of plans to be used for diverse planning applications.
Andrea Addis, Daniel Borrajo
Semantic Desktop: A Common Gate on Local and Distributed Indexed Resources
Abstract
The social Web is characterized by the communication between loose community members and the sharing of resources that have to be managed by adapted indexing and querying systems. In this chapter we present a solution helping users that want to belong to some community to organize in a common way the resources retrieved from the community and their own resources. We only consider communities structured as a network of peers without any centralized support. The implemented system front-end is built as a web application similar to a traditional desktop operating system. It gives interfaces to several tools dealing with resources stored in the memory. A specific one, independent of any other applications is in charge of all the operations concerning the indexing, the publication of resources in the peer to peer network and the retrieving of resources in the local memory and from the network. Our solution is based on semantic indexing using concepts of domain ontologies automatically downloaded from the network. We show the way we have solved the main issues occurring in this research context.
Claude Moulin, Cristian Lai
An Agent-Oriented Architecture for Researcher Profiling and Association Using Semantic Web Technologies
Abstract
Collaboration within the international scientific community has steadily increased over the years especially in the presence of complex interdisciplinary problems being investigated. At the same time the amount of research artifacts produced by the research community has grown exponentially making it difficult for individual researchers to filter and search through such information. In the presence of a vast amount of research information the problem of identifying potential project partners or collaborators with specific profiles can become extremely difficult. This paper presents a semantic multi-agent architecture (called SemoRA) aimed at tackling such a problem. The architecture combines agent and Semantic Web technologies in order to develop a framework capable of efficiently acquiring researcher information, making sense of it and giving meaning to it. The architecture ultimately enables the retrieval and matching of scored profiles aimed at enhancing collaborations among researchers – collaborations that can transcend both institutional and national boundaries.
Sadaf Adnan, Amal Tahir, Amna Basharat, Sergio de Cesare
Integrating Peer-to-Peer and Multi-agent Technologies for the Realization of Content Sharing Applications
Abstract
The combination of peer-to-peer networking and multi-agent systems seems be a perfect solution for the realization of applications that broaden on the Internet. In fact, while peer-to-peer networking infrastructures and protocols provide the suitable discovery and communication services necessary for developing effective and reliable applications, multi-agent systems allow to realize autonomous, social, reactive and proactive peers that make the development of intelligent and flexible application possible. This paper presents how JADE, one of the most known software framework for the development of multi-agent systems, has been extended to take advantage of the JXTA networking infrastructure and protocols, and describes a system, called RAIS, that has been realized thanks to such extended version of the JADE software framework and that provides a set of advanced services for content sharing and retrieval.
Agostino Poggi, Michele Tomaiuolo
Intelligent Advisor Agents in Distributed Environments
Abstract
The chapter presents a Distributed Expert System based on a multi-agent-architecture. The system is composed of a community of intelligent conversational agents playing the role of specialized advisors for the government of a virtual town, inspired to the SimCity game. The agents are capable to handle strategic decision under uncertainty conditions. They interact in natural language with their owners, obtain information on the current status of the town and give suggestions about the best strategies to apply in order to govern the town.
Agnese Augello, Giovanni Pilato, Salvatore Gaglio
Agent-Based Search and Retrieval in Virtual World Environments
Abstract
Virtual world and other 3D Web content has been treated as a separate domain from the traditional 2D Web, but is increasingly being integrated with broader web content. However, search engines do not have the ability to crawl virtual world content directly, making it difficult for users to find relevant content. We present an intelligent agent crawler designed to collect user-generated content in the Second Life and related virtual worlds. The agents navigate autonomously through the world to discover regions, parcels of land within regions, user-created objects, and other users. The agents also interact with objects through movement or ‘touch’ to trigger scripts that present dynamic content in the form of note cards, chat text, landmark links, and web URLs. The collection service includes a focused HTML crawler for collecting linked web content. The experiments we performed are the first which focus on the content of a large virtual world. Our results show that virtual worlds can be effectively crawled using autonomous agent crawlers that emulate normal user behavior. Additionally, we find that the collection of interactive content enhances our ability to identify dynamic, immersive environments within the world.
Joshua Eno, Susan Gauch, Craig W. Thompson
Contextual Data Management and Retrieval: A Self-organized Approach
Abstract
Pervasive computing devices are able to generate enormous amounts of distributed data, from which knowledge about situations and facts occurring in the world should be inferred for the use of pervasive services. However accessing and managing effectively such a huge amount of distributed information is challenging for services. In this paper after having outlined these challenges, we propose a self-organized agent-based approach to autonomously organize distributed contextual data items into sorts of knowledge networks. Knowledge networks are conceived as an alive self-organized layer in charge of managing data, that can facilitate services in extracting useful information out of a large amount of distributed items. In particular, we present the W4 Data Model we used to represent data and the self-organized approach to build Knowledge Networks. Some experimental results are reported to support our arguments and proposal, and related research work are extensively discussed.
Gabriella Castelli, Franco Zambonelli
A Relational Approach to Sensor Network Data Mining
Abstract
In this chapter a relational framework able to model and analyse the data observed by nodes involved in a sensor network is presented. In particular, we propose a powerful and expressive description language able to represent the spatio-temporal relations appearing in sensor network data along with the environmental information. Furthermore, a general purpose system able to elicit hidden frequent temporal correlations between sensor nodes is presented. The framework has been extended in order to take into account interval-based temporal data by introducing some operators based on a temporal interval logic. A preliminary abstraction step with the aim of segmenting and labelling the real-valued time series into similar subsequences is performed exploiting a kernel density estimation approach. The prposed framework has been evaluated on real world data collected from a wireless sensor network.
Floriana Esposito, Teresa M. A. Basile, Nicola Di Mauro, Stefano Ferilli
Content-Based Retrieval of Distributed Multimedia Conversational Data
Abstract
In this chapter we define the notion of multimedia conversational system and provide a classification schema for characterizing the type of content which can be produced, stored and retrieved with these systems. This classification schema will be used in reviewing the capabilities of a number of existing multimedia conversational systems and to assess requirements for advanced indexing and retrieval of conversational content. To meet these requirements, we present new types of indexing techniques and provide an evaluation of their effectiveness on real case studies.
Vincenzo Pallotta
Multimodal Aggregation and Recommendation Technologies Applied to Informative Content Distribution and Retrieval
Abstract
In the modern age, cross-media production is an innovative technique used by the media industry to ensure a positive return on investments while optimising productivity and market coverage. So that, technologies for the seamless fusion of heterogeneous data streams are increasingly considered important, and thus research efforts have started to explore this area. After having aggregated heterogeneous sources, what has to be addressed as the next problem is efficiency in retrieving and using the produced content. Tools for personalised and context-oriented multimedia retrieval are indispensable to access desired content from the aggregated data in a quicker and more useful way. This chapter describes the problems connected with this scenario and proposes an innovative technological framework to solve them, in the area of informative content (news) distribution and retrieval. Extensive experiments prove the effectiveness of this approach in a real-world business context.
Alberto Messina, Maurizio Montagnuolo
Using a Network of Scalable Ontologies for Intelligent Indexing and Retrieval of Visual Content
Abstract
There are still major challenges in the area of automatic indexing and retrieval of digital data. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. Research has been ongoing for several years in the field of ontological engineering with the aim of using ontologies to add knowledge to information. In this chapter we describe the architecture of a system designed to semi-automatically and intelligently index huge repositories of special effects video clips. The indexing is based on the semantic content of the video clips and uses a network of scalable ontologies to represent the semantic content to further enable intelligent retrieval.
Atta Badii, Chattun Lallah, Meng Zhu, Michael Crouch
Integrating Sense Discrimination in a Semantic Information Retrieval System
Abstract
This paper proposes an Information Retrieval (IR) system that integrates sense discrimination to overcome the problem of word ambiguity. Word ambiguity is a key problem for systems that have access to textual information. Semantic Vectors are able to divide the usages of a word into different meanings, by discriminating among word meanings on the ground of information available in unannotated corpora. This paper has a twofold goal: the former is to evaluate the effectiveness of an IR system based on Semantic Vectors, the latter is to describe how they have been integrated in a semantic IR framework to build semantic spaces of words and documents. To achieve the first goal, we performed an in vivo evaluation in an IR scenario and we compared the method based on sense discrimination to a method based on Word Sense Disambiguation (WSD). Contrarily to sense discrimination, which aims to discriminate among different meanings not necessarily known a priori, WSD is the task of selecting a sense for a word from a set of predefined possibilities. To accomplish the second goal, we integrated Semantic Vectors in a semantic search engine called SENSE (SEmantic N-levels Search Engine).
Pierpaolo Basile, Annalina Caputo, Giovanni Semeraro
Information Processing in Smart Grids and Consumption Dynamics
Abstract
This work suggests an effective approach for information management in smart power grids based on the introduction of a suitable theory of digital energy. It shows a possible way to effectively manage energy dynamics in real life systems in real time. Power grids hold real time information flows already, but the control systems currently adopted use other information sources. We discuss the use of the information and semantic technologies in order to balance the loads in storage-less electric energy domain, and the changes brought by Future Internet and its entities.
Mikhail Simonov, Riccardo Zich, Marco Mussetta
Backmatter
Metadata
Title
Information Retrieval and Mining in Distributed Environments
Editors
Alessandro Soro
Eloisa Vargiu
Giuliano Armano
Gavino Paddeu
Copyright Year
2011
Publisher
Springer Berlin Heidelberg
Electronic ISBN
978-3-642-16089-9
Print ISBN
978-3-642-16088-2
DOI
https://doi.org/10.1007/978-3-642-16089-9

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