50,853 research outputs found
Using Search Engine Technology to Improve Library Catalogs
This chapter outlines how search engine technology can be used in online public access library
catalogs (OPACs) to help improve users’ experiences, to identify users’ intentions, and to indicate
how it can be applied in the library context, along with how sophisticated ranking criteria can be
applied to the online library catalog. A review of the literature and current OPAC developments
form the basis of recommendations on how to improve OPACs. Findings were that the major
shortcomings of current OPACs are that they are not sufficiently user-centered and that their results
presentations lack sophistication. Further, these shortcomings are not addressed in current 2.0
developments. It is argued that OPAC development should be made search-centered before
additional features are applied. While the recommendations on ranking functionality and the use of
user intentions are only conceptual and not yet applied to a library catalogue, practitioners will find
recommendations for developing better OPACs in this chapter. In short, readers will find a
systematic view on how the search engines’ strengths can be applied to improving libraries’ online
catalogs
Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes
In this paper we present an evaluation of techniques that are designed to encourage web searchers to interact more with the results of a web search. Two specific techniques are examined: the presentation of sentences that highly match the searcher's query and the use of implicit evidence. Implicit evidence is evidence captured from the searcher's interaction with the retrieval results and is used to automatically update the display. Our evaluation concentrates on the effectiveness and subject perception of these techniques. The results show, with statistical significance, that the techniques are effective and efficient for information seeking
Keyword Search on RDF Graphs - A Query Graph Assembly Approach
Keyword search provides ordinary users an easy-to-use interface for querying
RDF data. Given the input keywords, in this paper, we study how to assemble a
query graph that is to represent user's query intention accurately and
efficiently. Based on the input keywords, we first obtain the elementary query
graph building blocks, such as entity/class vertices and predicate edges. Then,
we formally define the query graph assembly (QGA) problem. Unfortunately, we
prove theoretically that QGA is a NP-complete problem. In order to solve that,
we design some heuristic lower bounds and propose a bipartite graph
matching-based best-first search algorithm. The algorithm's time complexity is
, where is the number of the keywords and is a
tunable parameter, i.e., the maximum number of candidate entity/class vertices
and predicate edges allowed to match each keyword. Although QGA is intractable,
both and are small in practice. Furthermore, the algorithm's time
complexity does not depend on the RDF graph size, which guarantees the good
scalability of our system in large RDF graphs. Experiments on DBpedia and
Freebase confirm the superiority of our system on both effectiveness and
efficiency
Enhancing Workflow with a Semantic Description of Scientific Intent
Peer reviewedPreprin
Pattern tree-based XOLAP rollup operator for XML complex hierarchies
With the rise of XML as a standard for representing business data, XML data
warehousing appears as a suitable solution for decision-support applications.
In this context, it is necessary to allow OLAP analyses on XML data cubes.
Thus, XQuery extensions are needed. To define a formal framework and allow
much-needed performance optimizations on analytical queries expressed in
XQuery, defining an algebra is desirable. However, XML-OLAP (XOLAP) algebras
from the literature still largely rely on the relational model. Hence, we
propose in this paper a rollup operator based on a pattern tree in order to
handle multidimensional XML data expressed within complex hierarchies
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