23,351 research outputs found
Survey over Existing Query and Transformation Languages
A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability
of many current Semantic Web approaches to cope with data available in such diverging
representation formalisms as XML, RDF, or Topic Maps. A common query language is the first
step to allow transparent access to data in any of these formats. To further the understanding
of the requirements and approaches proposed for query languages in the conventional as well
as the Semantic Web, this report surveys a large number of query languages for accessing
XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from
all these areas. From the detailed survey of these query languages, a common classification
scheme is derived that is useful for understanding and differentiating languages within and
among all three areas
Comparative Analysis of Five XML Query Languages
XML is becoming the most relevant new standard for data representation and
exchange on the WWW. Novel languages for extracting and restructuring the XML
content have been proposed, some in the tradition of database query languages
(i.e. SQL, OQL), others more closely inspired by XML. No standard for XML query
language has yet been decided, but the discussion is ongoing within the World
Wide Web Consortium and within many academic institutions and Internet-related
major companies. We present a comparison of five, representative query
languages for XML, highlighting their common features and differences.Comment: TeX v3.1415, 17 pages, 6 figures, to be published in ACM Sigmod
Record, March 200
Semantic keyword search for expert witness discovery
In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach
Semantic keyword search for expert witness discovery
In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach
The complexity of acyclic conjunctive queries revisited
In this paper, we consider first-order logic over unary functions and study
the complexity of the evaluation problem for conjunctive queries described by
such kind of formulas. A natural notion of query acyclicity for this language
is introduced and we study the complexity of a large number of variants or
generalizations of acyclic query problems in that context (Boolean or not
Boolean, with or without inequalities, comparisons, etc...). Our main results
show that all those problems are \textit{fixed-parameter linear} i.e. they can
be evaluated in time where is the
size of the query , the database size, is
the size of the output and is some function whose value depends on the
specific variant of the query problem (in some cases, is the identity
function). Our results have two kinds of consequences. First, they can be
easily translated in the relational (i.e., classical) setting. Previously known
bounds for some query problems are improved and new tractable cases are then
exhibited. Among others, as an immediate corollary, we improve a result of
\~\cite{PapadimitriouY-99} by showing that any (relational) acyclic conjunctive
query with inequalities can be evaluated in time
. A second consequence of our method is
that it provides a very natural descriptive approach to the complexity of
well-known algorithmic problems. A number of examples (such as acyclic subgraph
problems, multidimensional matching, etc...) are considered for which new
insights of their complexity are given.Comment: 30 page
Exponential improvement in precision for simulating sparse Hamiltonians
We provide a quantum algorithm for simulating the dynamics of sparse
Hamiltonians with complexity sublogarithmic in the inverse error, an
exponential improvement over previous methods. Specifically, we show that a
-sparse Hamiltonian acting on qubits can be simulated for time
with precision using queries and
additional 2-qubit gates, where . Unlike previous
approaches based on product formulas, the query complexity is independent of
the number of qubits acted on, and for time-varying Hamiltonians, the gate
complexity is logarithmic in the norm of the derivative of the Hamiltonian. Our
algorithm is based on a significantly improved simulation of the continuous-
and fractional-query models using discrete quantum queries, showing that the
former models are not much more powerful than the discrete model even for very
small error. We also simplify the analysis of this conversion, avoiding the
need for a complex fault correction procedure. Our simplification relies on a
new form of "oblivious amplitude amplification" that can be applied even though
the reflection about the input state is unavailable. Finally, we prove new
lower bounds showing that our algorithms are optimal as a function of the
error.Comment: v1: 27 pages; Subsumes and improves upon results in arXiv:1308.5424.
v2: 28 pages, minor change
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