8,248 research outputs found
Hybrid XML Retrieval: Combining Information Retrieval and a Native XML Database
This paper investigates the impact of three approaches to XML retrieval:
using Zettair, a full-text information retrieval system; using eXist, a native
XML database; and using a hybrid system that takes full article answers from
Zettair and uses eXist to extract elements from those articles. For the
content-only topics, we undertake a preliminary analysis of the INEX 2003
relevance assessments in order to identify the types of highly relevant
document components. Further analysis identifies two complementary sub-cases of
relevance assessments ("General" and "Specific") and two categories of topics
("Broad" and "Narrow"). We develop a novel retrieval module that for a
content-only topic utilises the information from the resulting answer list of a
native XML database and dynamically determines the preferable units of
retrieval, which we call "Coherent Retrieval Elements". The results of our
experiments show that -- when each of the three systems is evaluated against
different retrieval scenarios (such as different cases of relevance
assessments, different topic categories and different choices of evaluation
metrics) -- the XML retrieval systems exhibit varying behaviour and the best
performance can be reached for different values of the retrieval parameters. In
the case of INEX 2003 relevance assessments for the content-only topics, our
newly developed hybrid XML retrieval system is substantially more effective
than either Zettair or eXist, and yields a robust and a very effective XML
retrieval.Comment: Postprint version. The editor version can be accessed through the DO
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection
Many important forms of data are stored digitally in XML format. Errors can
occur in the textual content of the data in the fields of the XML. Fixing these
errors manually is time-consuming and expensive, especially for large amounts
of data. There is increasing interest in the research, development, and use of
automated techniques for assisting with data cleaning. Electronic dictionaries
are an important form of data frequently stored in XML format that frequently
have errors introduced through a mixture of manual typographical entry errors
and optical character recognition errors. In this paper we describe methods for
flagging statistical anomalies as likely errors in electronic dictionaries
stored in XML format. We describe six systems based on different sources of
information. The systems detect errors using various signals in the data
including uncommon characters, text length, character-based language models,
word-based language models, tied-field length ratios, and tied-field
transliteration models. Four of the systems detect errors based on expectations
automatically inferred from content within elements of a single field type. We
call these single-field systems. Two of the systems detect errors based on
correspondence expectations automatically inferred from content within elements
of multiple related field types. We call these tied-field systems. For each
system, we provide an intuitive analysis of the type of error that it is
successful at detecting. Finally, we describe two larger-scale evaluations
using crowdsourcing with Amazon's Mechanical Turk platform and using the
annotations of a domain expert. The evaluations consistently show that the
systems are useful for improving the efficiency with which errors in XML
electronic dictionaries can be detected.Comment: 8 pages, 4 figures, 5 tables; published in Proceedings of the 2016
IEEE Tenth International Conference on Semantic Computing (ICSC), Laguna
Hills, CA, USA, pages 79-86, February 201
Report on the first Twente Data Management Workshop on XML Databases and Information Retrieval
The Database Group of the University of Twente initiated a new series of workshops called Twente Data Management workshops (TDM), starting with one on XML Databases and Information Retrieval which took place on 21 June 2004 at the University of Twente. We have set ourselves two goals for the workshop series: i) To provide a forum to share original ideas as well as research results on data management problems; ii) To bring together researchers from the database community and researchers from related research fields
XML Matchers: approaches and challenges
Schema Matching, i.e. the process of discovering semantic correspondences
between concepts adopted in different data source schemas, has been a key topic
in Database and Artificial Intelligence research areas for many years. In the
past, it was largely investigated especially for classical database models
(e.g., E/R schemas, relational databases, etc.). However, in the latest years,
the widespread adoption of XML in the most disparate application fields pushed
a growing number of researchers to design XML-specific Schema Matching
approaches, called XML Matchers, aiming at finding semantic matchings between
concepts defined in DTDs and XSDs. XML Matchers do not just take well-known
techniques originally designed for other data models and apply them on
DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical
structure of a DTD/XSD) to improve the performance of the Schema Matching
process. The design of XML Matchers is currently a well-established research
area. The main goal of this paper is to provide a detailed description and
classification of XML Matchers. We first describe to what extent the
specificities of DTDs/XSDs impact on the Schema Matching task. Then we
introduce a template, called XML Matcher Template, that describes the main
components of an XML Matcher, their role and behavior. We illustrate how each
of these components has been implemented in some popular XML Matchers. We
consider our XML Matcher Template as the baseline for objectively comparing
approaches that, at first glance, might appear as unrelated. The introduction
of this template can be useful in the design of future XML Matchers. Finally,
we analyze commercial tools implementing XML Matchers and introduce two
challenging issues strictly related to this topic, namely XML source clustering
and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure
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