Catálogo de publicaciones - libros

Skill and knowledge of master workmen and artists are important information for digital libraries. Usually disciples acquire the skill and knowledge by conversation with masters and watching the master’s works. Therefor they can be conveyed to limited number of disciples and they are sometimes lost when masters and artists pass away. A digital library for skill and knowledge plays an important role to preserve and convey them to large number of people. Since skill and knowledge inherent in masters and artists, first we need to externalize and represent them in an appropriate form. Interview to masters and artists are effective way to record their skill and knowledge. It can record various kinds of information such as emotional behavior, procedure of creative activity as well as verbal information in conversation. Furthermore interview enables us to obtain the information from masters and artists without heavy mental load. This characteristics is effective not only for gathering information of the skill and knowledge of masters and artists but also for externalizing knowledge of human beings in many fields.

Search performance can be greatly improved by describing data using Natural Language Processing (NLP) tools to create new metadata for digital libraries. In this paper, a methodology is presented to use a specific domain knowledge to improve user request. This domain knowledge is based on concepts, extracted from the document itself, used as “semantic metadata tags” in order to annotate XML documents. We present the process followed to define and to add new XML semantic metadata into the digital library of scientific theses. Using these new metadata, an ontology is also built to complete the annotation process. Effective retrieval information is obtained by using an intelligent system based on our XML semantic metadata and a domain ontology.

Advanced personalization techniques are required to cope with novel challenges posed by attribute-rich digital libraries. At the heart of our deeply personalized alerting system is one extensible preference model that serves all purposes in conjunction with our search technology Preference XPath and XML-based semantic annotations of digital library objects. In this paper we focus on the impact of automatic query expansion by ontologies. First results indicate that use of ontologies improves the quality of the result set and generates further results of higher quality.

In this paper, we present a new classification method, called Associative Naïve Bayes (ANB) , to associate MEDLINE citations with Gene Ontology (GO) terms. We define the concept of class-support to find frequent itemsets and the concept of class-all-confidence to find interesting itemsets. Empirical test results on three MEDLINE datasets show that ANB is superior to naïve Bayesian classifier. The results also show that ANB outperforms the state of the art Large Bayes classifier.

We report evaluation results for a collaborative querying environment. Our results show that compared with traditional information retrieval systems, collaborative querying can lead to faster information seeking when users perform unspecified tasks.

A person reading a book needs to gain insights based on the text. In most books, stories, themes, and references are organized structurally and purposefully. In previous work, we presented the design of an e-Book user interface that reveals the multi-structural information to support reading for comprehension[1]. In this paper, we describe techniques for discovering and representing the narrative structure of e-Books, and describe the user interface components for revealing this narrative structure to readers. We chose e-Bible as our corpus and named our user interface "iSee", meaning that "I see what I read".

Electronic document repositories continue to expand rapidly; public collections, for instance the Google index, contain up to 8 billion individual items. Private electronic archives, maintained by companies, governments and other bodies grow at similar rates. While search techniques have scaled to manage these vast collections, most interfaces between search engines and searchers, usually based on a ranked list, are increasingly insufficient. This paper explains how Information Foraging Theory was applied to create visualisations of query resultsets which, when embedded in an application that contained tools to manipulate the visualisation, helped alleviate the deficiencies of the ranked list.

We describe mod_oai, an Apache 2.0 module that implements the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH). OAI-PMH is the de facto standard for metadata exchange in digital libraries and allows repositories to expose their contents in a structured, application-neutral format with semantics optimized for accurate incremental harvesting. mod_oai differs from other OAI-PMH implementations in that it optimizes harvesting web content by building OAI-PMH capability into the Apache server.

Our research focuses on providing easy access to interdisciplinary information in the natural resource management domain [3] so users can more readily benefit from previous scientific findings, assessments, and decisions. Because of the widespread use of specialized terminology, our work focuses on extending a traditional thesaurus model [1] to properly represent and exploit the broad range of terms in a digital library designed for natural resource management.

Metadata provides a higher-level description of digital library resources and serves as a searchable record for browsing and accessing digital library content. However, manually assigning metadata is a resource-consuming task for which Natural Language Processing (NLP) can provide a solution. This poster coalesces the findings from research and development accomplished across two multi-year digital library metadata generation and evaluation projects and suggests how the lessons learned might benefit digital libraries with the need for high-quality, but efficient metadata assignment for their resources.