11,128 research outputs found
Operationalization of a graphical knowledge representation language
International audienceMOISE is a knowledge engineering methodology which includes a knowledge specification stage which separates static knowledge from dynamic knowledge. This stage integrates a graphical knowledge specification language (KRL) that combines a static specification language (semantic networks) and a dynamic specification language (task language). The modelling language KRL is the source language describing knowledge which becomes available for consultation. Some additional tools transform source graphical knowledge descriptions into different target languages: textual descriptions (word), hypertextual descriptions (html) and executable descriptions (SPIRAL). The paper deals with the latter tool. It presents the KRL itself (knowledge-level) and sketches the design model (symbol-level) that corresponds to its executable form and that is implemented in the SPIRAL object-oriented language
Semi-automatic distribution pattern modeling of web service compositions using semantics
Enterprise systems are frequently built by combining a
number of discrete Web services together, a process termed
composition. There are a number of architectural configurations or distribution patterns, which express how a composed system is to be deployed. Previously, we presented a Model Driven Architecture using UML 2.0, which took existing service interfaces as its input and generated an executable Web service composition, guided by a distribution pattern model. In this paper, we propose using Web service semantic descriptions in addition to Web service interfaces, to assist in the semi-automatic generation of the distribution pattern model. Web services described using semantic languages, such as OWL-S, can be automatically assessed for compatibility and their input and output messages can be mapped to each other
TumorML: Concept and requirements of an in silico cancer modelling markup language
This paper describes the initial groundwork carried out as part of the European Commission funded Transatlantic Tumor Model Repositories project, to develop a new markup language for computational cancer modelling, TumorML. In this paper we describe the motivations for such a language, arguing that current state-of-the-art biomodelling languages are not suited to the cancer modelling domain. We go on to describe the work that needs to be done to develop TumorML, the conceptual design, and a description of what existing markup languages will be used to compose the language specification
An architecture for autonomic web service process planning
Web service composition is a technology that has received
considerable attention in the last number of years. Languages and tools to aid in the process of creating composite web services have been received specific attention. Web service composition is the process of linking single web services together in order to accomplish more complex tasks. One area of web service composition that has not received as much attention is the area of dynamic error handling and re-planning, enabling autonomic composition. Given a repository of service descriptions and a task to complete, it is possible for AI planners to automatically create a plan that will achieve this goal. If however a service in the plan is unavailable or erroneous the plan will fail. Motivated by this problem, this paper suggests autonomous re-planning as a means to overcome dynamic problems. Our solution involves automatically recovering from faults and creating a context-dependent alternate plan
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Teaching robots parametrized executable plans through spoken interaction
While operating in domestic environments, robots will necessarily
face difficulties not envisioned by their developers at programming
time. Moreover, the tasks to be performed by a robot will often
have to be specialized and/or adapted to the needs of specific users
and specific environments. Hence, learning how to operate by interacting
with the user seems a key enabling feature to support the
introduction of robots in everyday environments.
In this paper we contribute a novel approach for learning, through
the interaction with the user, task descriptions that are defined as a
combination of primitive actions. The proposed approach makes
a significant step forward by making task descriptions parametric
with respect to domain specific semantic categories. Moreover, by
mapping the task representation into a task representation language,
we are able to express complex execution paradigms and to revise
the learned tasks in a high-level fashion. The approach is evaluated
in multiple practical applications with a service robot
Learning Semantic Correspondences in Technical Documentation
We consider the problem of translating high-level textual descriptions to
formal representations in technical documentation as part of an effort to model
the meaning of such documentation. We focus specifically on the problem of
learning translational correspondences between text descriptions and grounded
representations in the target documentation, such as formal representation of
functions or code templates. Our approach exploits the parallel nature of such
documentation, or the tight coupling between high-level text and the low-level
representations we aim to learn. Data is collected by mining technical
documents for such parallel text-representation pairs, which we use to train a
simple semantic parsing model. We report new baseline results on sixteen novel
datasets, including the standard library documentation for nine popular
programming languages across seven natural languages, and a small collection of
Unix utility manuals.Comment: accepted to ACL-201
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