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Combining Ontology Queries with Text Search in Service Discovery
We present a querying mechanism for service discovery which combines ontology queries with text search. The underlying service discovery architecture used is GloServ. GloServ uses the Web Ontology Language (OWL) to classify services in an ontology and map knowledge obtained by the ontology onto a hierarchical peer-to-peer network. Initially, an ontology-based first order predicate logic query is issued in order to route the query to the appropriate server and to obtain exact and related service data. Text search further enhances querying by allowing services to be described not only with ontology attributes, but with plain text so that users can query for them using key words. Currently, querying is limited to either simple attribute-value pair searches, ontology queries or text search. Combining ontology queries with text search enhances current service discovery mechanisms
The Bag Semantics of Ontology-Based Data Access
Ontology-based data access (OBDA) is a popular approach for integrating and
querying multiple data sources by means of a shared ontology. The ontology is
linked to the sources using mappings, which assign views over the data to
ontology predicates. Motivated by the need for OBDA systems supporting
database-style aggregate queries, we propose a bag semantics for OBDA, where
duplicate tuples in the views defined by the mappings are retained, as is the
case in standard databases. We show that bag semantics makes conjunctive query
answering in OBDA coNP-hard in data complexity. To regain tractability, we
consider a rather general class of queries and show its rewritability to a
generalisation of the relational calculus to bags
The combined approach to ontology-based data access
The use of ontologies for accessing data is one of
the most exciting new applications of description
logics in databases and other information systems.
A realistic way of realising sufficiently scalable ontology-
based data access in practice is by reduction
to querying relational databases. In this paper,
we describe the combined approach, which incorporates
the information given by the ontology into
the data and employs query rewriting to eliminate
spurious answers. We illustrate this approach for
ontologies given in the DL-Lite family of description
logics and briefly discuss the results obtained
for the EL family
The Limits of Efficiency for Open- and Closed-World Query Evaluation Under Guarded TGDs
Ontology-mediated querying and querying in the presence of constraints are
two key database problems where tuple-generating dependencies (TGDs) play a
central role. In ontology-mediated querying, TGDs can formalize the ontology
and thus derive additional facts from the given data, while in querying in the
presence of constraints, they restrict the set of admissible databases. In this
work, we study the limits of efficient query evaluation in the context of the
above two problems, focussing on guarded and frontier-guarded TGDs and on UCQs
as the actual queries. We show that a class of ontology-mediated queries (OMQs)
based on guarded TGDs can be evaluated in FPT iff the OMQs in the class are
equivalent to OMQs in which the actual query has bounded treewidth, up to some
reasonable assumptions. For querying in the presence of constraints, we
consider classes of constraint-query specifications (CQSs) that bundle a set of
constraints with an actual query. We show a dichotomy result for CQSs based on
guarded TGDs that parallels the one for OMQs except that, additionally, FPT
coincides with PTime combined complexity. The proof is based on a novel
connection between OMQ and CQS evaluation. Using a direct proof, we also show a
similar dichotomy result, again up to some reasonable assumptions, for CQSs
based on frontier-guarded TGDs with a bounded number of atoms in TGD heads. Our
results on CQSs can be viewed as extensions of Grohe's well-known
characterization of the tractable classes of CQs (without constraints). Like
Grohe's characterization, all the above results assume that the arity of
relation symbols is bounded by a constant. We also study the associated meta
problems, i.e., whether a given OMQ or CQS is equivalent to one in which the
actual query has bounded treewidth
From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data
We compare two distinct approaches for querying data in the context of the
life sciences. The first approach utilizes conventional databases to store the
data and intuitive form-based interfaces to facilitate easy querying of the
data. These interfaces could be seen as implementing a set of "pre-canned"
queries commonly used by the life science researchers that we study. The second
approach is based on semantic Web technologies and is knowledge (model) driven.
It utilizes a large OWL ontology and same datasets as before but associated as
RDF instances of the ontology concepts. An intuitive interface is provided that
allows the formulation of RDF triples-based queries. Both these approaches are
being used in parallel by a team of cell biologists in their daily research
activities, with the objective of gradually replacing the conventional approach
with the knowledge-driven one. This provides us with a valuable opportunity to
compare and qualitatively evaluate the two approaches. We describe several
benefits of the knowledge-driven approach in comparison to the traditional way
of accessing data, and highlight a few limitations as well. We believe that our
analysis not only explicitly highlights the specific benefits and limitations
of semantic Web technologies in our context but also contributes toward
effective ways of translating a question in a researcher's mind into precise
computational queries with the intent of obtaining effective answers from the
data. While researchers often assume the benefits of semantic Web technologies,
we explicitly illustrate these in practice
An infrastructure for building semantic web portals
In this paper, we present our KMi semantic web portal infrastructure, which supports two important tasks of semantic web portals, namely metadata extraction and data querying. Central to our infrastructure are three components: i) an automated metadata extraction tool, ASDI, which supports the extraction of high quality metadata from heterogeneous sources, ii) an ontology-driven question answering tool, AquaLog, which makes use of the domain specific ontology and the semantic metadata extracted by ASDI to answers questions in natural language format, and iii) a semantic search engine, which enhances traditional
text-based searching by making use of the underlying ontologies and the extracted metadata. A semantic web portal application has been built, which illustrates the usage of this infrastructure
XQOWL: An Extension of XQuery for OWL Querying and Reasoning
One of the main aims of the so-called Web of Data is to be able to handle
heterogeneous resources where data can be expressed in either XML or RDF. The
design of programming languages able to handle both XML and RDF data is a key
target in this context. In this paper we present a framework called XQOWL that
makes possible to handle XML and RDF/OWL data with XQuery. XQOWL can be
considered as an extension of the XQuery language that connects XQuery with
SPARQL and OWL reasoners. XQOWL embeds SPARQL queries (via Jena SPARQL engine)
in XQuery and enables to make calls to OWL reasoners (HermiT, Pellet and
FaCT++) from XQuery. It permits to combine queries against XML and RDF/OWL
resources as well as to reason with RDF/OWL data. Therefore input data can be
either XML or RDF/OWL and output data can be formatted in XML (also using
RDF/OWL XML serialization).Comment: In Proceedings PROLE 2014, arXiv:1501.0169
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