1 / 15

The Web-DL Environment for Building Digital Libraries from the Web

The Web-DL Environment for Building Digital Libraries from the Web. P. Calado 1 , M. Gonçalves 2 , E. Fox 2 , B. Ribeiro-Neto 1 , A. Laender 1 , A. da Silva 3 , D. Reis 1 , P. Roberto 1 , M. Vieira 1 , J. Lage 1. 1 Federal University of Minas Gerais 2 Virginia Tech

trista
Download Presentation

The Web-DL Environment for Building Digital Libraries from the Web

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Web-DL Environment for Building Digital Libraries from the Web P. Calado1, M. Gonçalves2, E. Fox2, B. Ribeiro-Neto1, A. Laender1, A. da Silva3, D. Reis1, P. Roberto1, M. Vieira1, J. Lage1 1Federal University of Minas Gerais 2Virginia Tech 3Federal University of Amazonas Project I3DL: Integrating Network Representations and Inference Models to Generate Tailored and Interoperable Digital Libraries from Specifications

  2. Introduction • The Web • Largely unstructured • No assumptions about users • Huge volume of information • Digital Libraries • Information is explicitly organized, described, and managed • Users have broad interests but are more specialized • Controlled environment

  3. Moving From the Web to a Digital Library High Databases Knowledge of users/tasks Digital Libraries Web Low High Structure of data

  4. The Web-DL Environment • Combines data extraction and DL tools to: • Collect data from the Web • Normalize the data • Make the data publicly available • Thus providing: • Services and organization of a DL • Access to the breadth of Web contents

  5. Context • Federated digital library systems • Autonomous and heterogeneous • Different systems/protocols • Challenges: interoperability, data fusion, etc. • Networked Library of Theses and Dissertations (NDLTD) • 176 members (and growing) • Continuous flow of new data (ETDs) • Not all support the same protocols/standards

  6. Web-DL Architecture • From the Web to a DL in 3 steps: • Crawl Web sites to collect pages • Parse the pages to extract the data, mapping it to a standard format • Make the data available through a standard protocol (to a DL service provider)

  7. Web-DL Architecture

  8. Generates agents for collecting Web pages Provides a visual metaphor for navigation examples Able to collect both static and dynamic pages Collecting Web pages: the ASByE tool

  9. Provides a visual paradigm to specify data examples Generates wrappers based on context Implemented as a Web service Extracting the data: the DEByE tool

  10. Web data is far from standardized Missing mandatory information Information only implicitly present Data in wrong format Data in wrong encoding A solution: data conversion modules Normalizing Web data

  11. Configurable from specifications Semantic networks allow a unified representation of complex digital objects and relationships Extensible API Providing DL services:the MARIAN system

  12. An example Web DL • ETDs were collected from NDLTD member sites • 21 different institutions • 9595 ETDs • Work required: • 2—3 examples/field, 9 min./site • Major problems found: • Sites offline • The hidden Web problem • Data normalization

  13. An example Web DL

  14. Conclusions • Moving from the Web to DL is not trivial • Site availability, hidden Web • Data extraction • Data normalization • A general solution depends on solutions for these tasks and on integrating these solutions • Web-DL provides and environment for such integration

  15. Future Work • Data integration • Data quality estimation • Automatic example generation

More Related