114,787 research outputs found
Escaping the Trap of too Precise Topic Queries
At the very center of digital mathematics libraries lie controlled
vocabularies which qualify the {\it topic} of the documents. These topics are
used when submitting a document to a digital mathematics library and to perform
searches in a library. The latter are refined by the use of these topics as
they allow a precise classification of the mathematics area this document
addresses. However, there is a major risk that users employ too precise topics
to specify their queries: they may be employing a topic that is only "close-by"
but missing to match the right resource. We call this the {\it topic trap}.
Indeed, since 2009, this issue has appeared frequently on the i2geo.net
platform. Other mathematics portals experience the same phenomenon. An approach
to solve this issue is to introduce tolerance in the way queries are understood
by the user. In particular, the approach of including fuzzy matches but this
introduces noise which may prevent the user of understanding the function of
the search engine.
In this paper, we propose a way to escape the topic trap by employing the
navigation between related topics and the count of search results for each
topic. This supports the user in that search for close-by topics is a click
away from a previous search. This approach was realized with the i2geo search
engine and is described in detail where the relation of being {\it related} is
computed by employing textual analysis of the definitions of the concepts
fetched from the Wikipedia encyclopedia.Comment: 12 pages, Conference on Intelligent Computer Mathematics 2013 Bath,
U
Intelligent information processing in a digital library using semantic web
With the explosive growth of information, it is
becoming increasingly difficult to retrieve the relevant
documents with current search engine only. The
information is treated as an ordinary database that
manages the contents and positions. To the individual
user, there is a great deal of useless information in
addition to the substantial amount of useful information.
This begets new challenges to docent community
and motivates researchers to look for intelligent
information retrieval approach and ontologies that
search and/or filter information automatically based on
some higher level of understanding are required. We
study improving the efficiency of search methods and
classify the search patrons into several models based on
the profiles of agent based on ontology.
We have proposed a method to efficiently search for
the target information on a Digital Library network with
multiple independent information sources. This paper
outlines the development of an expert prototype system
based in an ontology for retrieval information of the
Digital Library University of Seville. The results of this
study demonstrate that by improving representation by
incorporating more metadata from within the
information and the ontology into the retrieval process,
the effectiveness of the information retrieval is enhanced.
We used Jcolibri and Prótége for developing the
ontology and creation the expert system respectively
An Analysis of Using Expert Systems and Intelligent Agents for the Virtual Library Project at the Naval Surface Warfare Center-Carderock Division
The Virtual Library Project1 at the Naval Surface Warfare Center/Carderock Division (NSWC/CD) is being developed to facilitate the incorporation and use of library documents via the Internet. These documents typically relate to the design and manufacture of ships for the U.S. Navy Fleet. As such, the libraries will store documents that contain not only text but also images, graphs and design configurations. Because of the dynamic nature of digital documents, particularly those related to design, rapid and effective cataloging of these documents becomes challenging. We conducted a research study to analyze the use of expert systems and intelligent agents to support the function of cataloging digital documents. This chapter provides an overview of past research in the use of expert systems and intelligent agents for cataloging digital documents and discusses our recommendations based on NSWC/CD’s requirements
Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques
Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to
be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning
methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories.
We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that
proposes a new form of interaction between users and digital libraries, where the latter are adapted to users
and their surroundings
Graph-of-Entity: A Model for Combined Data Representation and Retrieval
Managing large volumes of digital documents along with the information they contain, or are associated with, can be challenging. As systems become more intelligent, it increasingly makes sense to power retrieval through all available data, where every lead makes it easier to reach relevant documents or entities. Modern search is heavily powered by structured knowledge, but users still query using keywords or, at the very best, telegraphic natural language. As search becomes increasingly dependent on the integration of text and knowledge, novel approaches for a unified representation of combined data present the opportunity to unlock new ranking strategies. We tackle entity-oriented search using graph-based approaches for representation and retrieval. In particular, we propose the graph-of-entity, a novel approach for indexing combined data, where terms, entities and their relations are jointly represented. We compare the graph-of-entity with the graph-of-word, a text-only model, verifying that, overall, it does not yet achieve a better performance, despite obtaining a higher precision. Our assessment was based on a small subset of the INEX 2009 Wikipedia Collection, created from a sample of 10 topics and respectively judged documents. The offline evaluation we do here is complementary to its counterpart from TREC 2017 OpenSearch track, where, during our participation, we had assessed graph-of-entity in an online setting, through team-draft interleaving
A Survey on Retrieval of Mathematical Knowledge
We present a short survey of the literature on indexing and retrieval of
mathematical knowledge, with pointers to 72 papers and tentative taxonomies of
both retrieval problems and recurring techniques.Comment: CICM 2015, 20 page
Computer-based library or computer-based learning?
Traditionally, libraries have played the role of repository of published information resources and, more recently,
gateway to online subscription databases. The library online catalog and digital library interface serve an
intermediary function to help users locate information resources available through the library. With competition from Web search engines and Web portals of various kinds available for free, the library has to step up to play a more active role as guide and coach to help users make use of information resources for learning or to accomplish particular tasks. It is no longer sufficient for computer-based library systems to provide just search and access functions. They must provide the functionality and environment to support learning and become computer-based learning systems. This paper examines the kind of learning support that can be incorporated in library online catalogs and digital libraries, including 1) enhanced support for information browsing and synthesis through linking by shared meta-data, references and concepts; 2) visualization of related information; 3) adoption of Library 2.0 and social technologies; 4) adoption of Library 3.0 technologies including intelligent processing and text mining
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
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