3,630 research outputs found
Coherent Integration of Databases by Abductive Logic Programming
We introduce an abductive method for a coherent integration of independent
data-sources. The idea is to compute a list of data-facts that should be
inserted to the amalgamated database or retracted from it in order to restore
its consistency. This method is implemented by an abductive solver, called
Asystem, that applies SLDNFA-resolution on a meta-theory that relates
different, possibly contradicting, input databases. We also give a pure
model-theoretic analysis of the possible ways to `recover' consistent data from
an inconsistent database in terms of those models of the database that exhibit
as minimal inconsistent information as reasonably possible. This allows us to
characterize the `recovered databases' in terms of the `preferred' (i.e., most
consistent) models of the theory. The outcome is an abductive-based application
that is sound and complete with respect to a corresponding model-based,
preferential semantics, and -- to the best of our knowledge -- is more
expressive (thus more general) than any other implementation of coherent
integration of databases
Learning Tuple Probabilities
Learning the parameters of complex probabilistic-relational models from
labeled training data is a standard technique in machine learning, which has
been intensively studied in the subfield of Statistical Relational Learning
(SRL), but---so far---this is still an under-investigated topic in the context
of Probabilistic Databases (PDBs). In this paper, we focus on learning the
probability values of base tuples in a PDB from labeled lineage formulas. The
resulting learning problem can be viewed as the inverse problem to confidence
computations in PDBs: given a set of labeled query answers, learn the
probability values of the base tuples, such that the marginal probabilities of
the query answers again yield in the assigned probability labels. We analyze
the learning problem from a theoretical perspective, cast it into an
optimization problem, and provide an algorithm based on stochastic gradient
descent. Finally, we conclude by an experimental evaluation on three real-world
and one synthetic dataset, thus comparing our approach to various techniques
from SRL, reasoning in information extraction, and optimization
State-of-the-art on evolution and reactivity
This report starts by, in Chapter 1, outlining aspects of querying and updating resources on
the Web and on the Semantic Web, including the development of query and update languages
to be carried out within the Rewerse project.
From this outline, it becomes clear that several existing research areas and topics are of
interest for this work in Rewerse. In the remainder of this report we further present state of
the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give
an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs;
in Chapter 4 event-condition-action rules, both in the context of active database systems and
in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks
Use-cases on evolution
This report presents a set of use cases for evolution and reactivity for data in the Web and
Semantic Web. This set is organized around three different case study scenarios, each of them
is related to one of the three different areas of application within Rewerse. Namely, the scenarios
are: âThe Rewerse Information System and Portalâ, closely related to the work of A3
â Personalised Information Systems; âOrganizing Travelsâ, that may be related to the work
of A1 â Events, Time, and Locations; âUpdates and evolution in bioinformatics data sourcesâ
related to the work of A2 â Towards a Bioinformatics Web
From Text to Knowledge with Graphs: modelling, querying and exploiting textual content
This paper highlights the challenges, current trends, and open issues related
to the representation, querying and analytics of content extracted from texts.
The internet contains vast text-based information on various subjects,
including commercial documents, medical records, scientific experiments,
engineering tests, and events that impact urban and natural environments.
Extracting knowledge from this text involves understanding the nuances of
natural language and accurately representing the content without losing
information. This allows knowledge to be accessed, inferred, or discovered. To
achieve this, combining results from various fields, such as linguistics,
natural language processing, knowledge representation, data storage, querying,
and analytics, is necessary. The vision in this paper is that graphs can be a
well-suited text content representation once annotated and the right querying
and analytics techniques are applied. This paper discusses this hypothesis from
the perspective of linguistics, natural language processing, graph models and
databases and artificial intelligence provided by the panellists of the DOING
session in the MADICS Symposium 2022
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