26,953 research outputs found
An extensible web interface for databases and its application to storing biochemical data
This paper presents a generic web-based database interface implemented in
Prolog. We discuss the advantages of the implementation platform and
demonstrate the system's applicability in providing access to integrated
biochemical data. Our system exploits two libraries of SWI-Prolog to create a
schema-transparent interface within a relational setting. As is expected in
declarative programming, the interface was written with minimal programming
effort due to the high level of the language and its suitability to the task.
We highlight two of Prolog's features that are well suited to the task at hand:
term representation of structured documents and relational nature of Prolog
which facilitates transparent integration of relational databases. Although we
developed the system for accessing in-house biochemical and genomic data the
interface is generic and provides a number of extensible features. We describe
some of these features with references to our research databases. Finally we
outline an in-house library that facilitates interaction between Prolog and the
R statistical package. We describe how it has been employed in the present
context to store output from statistical analysis on to the database.Comment: Online proceedings of the Joint Workshop on Implementation of
Constraint Logic Programming Systems and Logic-based Methods in Programming
Environments (CICLOPS-WLPE 2010), Edinburgh, Scotland, U.K., July 15, 201
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Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
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
kLog: A Language for Logical and Relational Learning with Kernels
We introduce kLog, a novel approach to statistical relational learning.
Unlike standard approaches, kLog does not represent a probability distribution
directly. It is rather a language to perform kernel-based learning on
expressive logical and relational representations. kLog allows users to specify
learning problems declaratively. It builds on simple but powerful concepts:
learning from interpretations, entity/relationship data modeling, logic
programming, and deductive databases. Access by the kernel to the rich
representation is mediated by a technique we call graphicalization: the
relational representation is first transformed into a graph --- in particular,
a grounded entity/relationship diagram. Subsequently, a choice of graph kernel
defines the feature space. kLog supports mixed numerical and symbolic data, as
well as background knowledge in the form of Prolog or Datalog programs as in
inductive logic programming systems. The kLog framework can be applied to
tackle the same range of tasks that has made statistical relational learning so
popular, including classification, regression, multitask learning, and
collective classification. We also report about empirical comparisons, showing
that kLog can be either more accurate, or much faster at the same level of
accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at
http://klog.dinfo.unifi.it along with tutorials
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