Catálogo de publicaciones - libros

Digital Libraries represent semantically rich collections of digital documents. Ontology-based information retrieval systems capture semantic relations for providing value added information services. Deviating from the regular approach of developing ontologies on the basis of domain knowledge, the present paper puts forward a novel method for developing ontologies from the semantic information available in the titles of digital documents. Such an approach gathers significance due to its simplicity in ontology development process. To examine the same, the study considered the case of Agricultural Electronic Theses and Dissertations (ETDs) present in Vidyanidhi Digital Library. The study resulted in the development of Indian Agricultural Research domain ontology, which was used for developing ontology-based information retrieval system. This paper while describing the methodology followed for developing the ontology presents the technical details of the developed system.

There are huge numbers of documents in digital libraries. How to effectively organize these documents so that humans can easily browse or reference is a challenging task. Existing classification methods and chronological or geographical ordering only provide partial views of the news articles. The relationships among news articles might not be easily grasped. In this paper, we propose a near-duplicate copy detection approach to organizing news archives in digital libraries. Conventional copy detection methods use word-level features which could be time-consuming and not robust to term substitutions. In this paper, we propose a sentence-level statistics-based approach to detect near-duplicate documents, which is language independent, simple but effective. It’s orthogonal to and can be used to complement word-based approaches. Also it’s insensitive to actual page layout of articles. The experimental results showed the high efficiency and good accuracy of the proposed approach in detecting near-duplicates in news archives.

Dictionary is an efficient management of large sets of distinct strings in memory. It has significant influence on Natural Language Process, Information Retrieval and other areas. In this paper, we propose an efficient dictionary mechanism, which is suitable for Double-Byte coding languages. Compared with other five popular dictionary mechanisms, this mechanism performs the best of all. It improves the search performance greatly and reduces the complexity of the construction and maintenance of the dictionary. It can be well applied in large-scale and real-time processing systems. Since Unicode is a typical double-byte code which can represents all kinds of characters in the world, this dictionary will be applicable for multi-language dictionaries.

Digital libraries have untapped potential for supporting language teaching and learning. This paper describes a new scheme for automating topic-specific language learning using a specially built digital library. Three exercises of different types are generated automatically from the library content: one that learners undertake individually, one in which learners collaborate in pairs, and one in which a group of learners compete. The system aims to foster content-based language learning, which greatly increases students’ motivation, fosters long-term recollection, and can be culturally situated in appropriate ways.

This paper is a follow-up to our ICADL 2006 paper, reporting on our progress over the past year in developing a digital library curriculum. It presents and describes the current curriculum framework, which now includes ten modules and 41 sub-modules. It provides an overview of the curriculum development lifecycle, and our progress through that lifecycle. In particular, it reports on our evaluation of the modules that have been drafted. It concludes with a description of two new technologies – Superimposed Information (SI) to help resource presentation in a module and Visual User model Data Mining (VUDM) to help long-term module upgrade by visualizing the user community and its trends.

In this paper, we describe our ongoing project to help alleviate the digital divide problem among high schools in rural areas of Thailand. The idea is to select, organize, index and distribute useful educational Web contents to schools where the Internet connection is not available. These Web contents can be used by teachers and students to enhance the teaching and learning for many class subjects. We have collaborated with a group of teachers from different high schools in order to gather the requirements for designing our software tools. One of the challenging issues is the variation in computer hardwares and network configuration found in different schools. Some shools have PCs connected to the school’s server via the Local Area Network (LAN). While some other schools have low-performance PCs without any network connection. To support both cases, we provide two solutions via two different search engine tools. These tools support content administrators, e.g., teachers, with the features to organize and index the contents. The tools also provide general users with the features to browse and search for needed information. Since the contents and index are locally stored on hard disk or some removable media such as CD-ROM, the Internet connection is not needed.

This study investigates the optimization of the ordering of retrieved media objects from educational multimedia repositories for a continuously-played presentation such that the total presentation lag through a slow network is minimized. We present a computation-efficient NEH-based heuristic algorithm that can obtain a near-optimal solution with minimal computation time. A simulation experiment shows the average gaps between the idle rateof heuristic solutions and randomly generated sequences are around 26.3%. The results indicate the proposed heuristic solution can significantly reduce the presentation lag as compared to a random ordering approach that is commonly applied in conventional multimedia repositories.

We examine the ‘native’ metadata and organizational structures that individuals create for their personal photo digital libraries, by analyzing the behavior of photo collectors as recorded in 37 autoethnographies and ethnographies. The findings confirm several common assumptions about how people organize their photos that have been the basis for features in earlier photo digital libraries-that photos are commonly organized by time, event, and location, and that collection owners create very little metadata manually. We discuss alternate sources of metadata that arise as a consequence of sharing photos, and consider additional features for photo digital libraries that may be useful in supporting searching and browsing of personal collections.

Despite significant growth of the Web in recent years, some portions of the Web remain largely underdeveloped, as shown in a lack of high quality content and functionality. An example is the Arabic Web, in which a lack of well-structured Web directories has limited users’ ability to browse for Arabic resources. In this research, we proposed an approach to building Web directories for the underdeveloped Web and developed a proof-of-concept prototype called Arabic Medical (AMed) Web Directory that supports browsing of over 5,000 Arabic medical Web sites and pages organized in a hierarchical structure. We conducted an experiment involving Arab subjects and found that AMed directory significantly outperformed a benchmark Arabic Web directory in terms of browsing effectiveness and user ratings. This research thus contributes to developing a useful Web directory for organizing information of the Arabic medical domain and to better understanding of supporting browsing on the underdeveloped Web.

Recently, collaborative tagging has become popular in the web2.0 world. Tags can be helpful if used for the recommendation since they reflect characteristic content features of the resources. However, there are few researches which introduce tags into the recommendation. This paper proposes a tag-based recommendation framework for scientific literatures which models the user interests with tags and literature keywords. A hybrid recommendation algorithm is then applied which is similar to the user-user collaborative filtering algorithm except that the user similarity is measured based on the vector model of user keywords other than the rating matrix, and that the rating is not from the user but represented as user-item similarity computed with the dot-product-based similarity instead of the cosine-based similarity. Experiments show that our tag-based algorithm is better than the baseline algorithm and the extension of user model and dot-product-based similarity computation are also helpful.