114 research outputs found
Evolving Objects in Temporal Information Systems
This paper presents a semantic foundation of temporal conceptual models used to design temporal information systems. We consider a modelling language able to express both timestamping and evolution constraints. We conduct a deeper investigation of evolution constraints, eventually devising a model-theoretic semantics for a full-fledged model with both timestamping and evolution constraints. The proposed formalization is meant both to clarify the meaning of the various temporal constructors that appeared in the literature and to give a rigorous definition, in the context of temporal information systems, to notions like satisfiability, subsumption and logical implication. Furthermore, we show how to express temporal constraints using a subset of first-order temporal logic, i.e. DLRUS, the description logic DLR extended with the temporal operators Since and Until. We show how DLRUS is able to capture the various modelling constraints in a succinct way and to perform automated reasoning on temporal conceptual models
Description Logic for Scene Understanding at the Example of Urban Road Intersections
Understanding a natural scene on the basis of external sensors is a task yet to be solved by computer algorithms. The present thesis investigates the suitability of a particular family of explicit, formal representation and reasoning formalisms for this task, which are subsumed under the term Description Logic
A general cognitive framework for context-aware systems: extensions and applications for high level information fusion approaches
Mención Internacional en el título de doctorContext-aware systems aims at the development of computational systems that process data acquired from different datasources and adapt their behaviour in order to provide the 'right' information, at the 'right' time, in the 'right' place, in the 'right' way to the 'right' person (Fischer, 2012). Traditionally computational research has tried to answer these needs by means of low-level algorithms. In the last years the combination of numeric and symbolic approaches has offered the opportunity to create systems to deal with these issues. However, although the performance of algorithms and the quality of the data directly provided by computers and devices has quickly improved, symbolic models used to represent the resulting knowledge have not yet been adapted to smart environments. This lack of representation does not allow to take advantage of the semantic quality of the information provided by new sensors.
This dissertation proposes a set of extensions and applications focused on a cognitive framework for the implementation of context-aware systems based on a general model inspired by the Information Fusion paradigm. This model is stepped in several abstraction levels from low-level raw data to high level scene interpretation whose structure is determined by a set of ontologies. Each ontology level provides a skeleton that includes general concepts and relations to describe entities and their connections. This structure has been designed to promote extensibility and modularity, and might be refined to apply this model in specific domains. This framework combines a priori context knowledge represented with ontologies with real data coming from sensors to support logic-based high-level interpretation of the current situation and to automatically generate feedback recommendations to adjust data acquisition procedures.
This work advocates for the introduction of general purpose cognitive layers in order to obtain a closer representation to the human cognition, generate additional knowledge and improve the high-level interpretation. Extensibility and adaptability of the basic ontology levels is demonstrated with the introduction of these traverse semantic layers which are able to be present and represent information at several granularity levels of knowledge using a common formalism.
Context-based system must be able to reason about uncertainty. However the reasoning associated to ontologies has been limited to classical description logic mechanisms. This research also tackle the problem of reasoning under uncertainty circumstances through a logic-based paradigm for abductive reasoning: the Belief-Argumentation System.
The main contribution of this dissertation is the adaptation of the general architecture and the theoretical proposals to several context-aware application areas such as Ambient Intelligence, Social Signal Processing and surveillance systems. The implementation of prototypes and examples for these areas are explained along this dissertation to progressively illustrate the improvements and extensions in the framework. To initially depict the general model, its components and the basic reasoning mechanisms a video-based Ambient Intelligence application is presented. The advantages and features of the framework extensions through traverse cognitive layers are demonstrated in a Social Signal Processing case for the elaboration of automatic market researches. Finally, the functioning of the system under uncertainty circumstances is illustrated with several examples to support decision makers in the detection of potential threats in common harbor scenarios.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: José Manuel Molina López.- Secretario: Ángel Arroyo.- Vocal: Nayat Sánchez P
At the Biological Modeling and Simulation Frontier
We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine
Evidence-based lean conceptual data modelling languages
Multiple logic-based reconstructions of conceptual data modelling languages such as EER, UML Class Diagrams, and ORM exist. They mainly cover various fragments of the languages and none are formalised such that the logic applies simultaneously for all three modelling language families as unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible. We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, we specify minimal logic profiles availing of this extended process, including the ontological commitments embedded in the languages, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL). The profiles characterise the essential logic structure needed to handle the semantics of conceptual models, therewith enabling the development of interoperability tools. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable DL ALNI). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models
Dwelling on ontology - semantic reasoning over topographic maps
The thesis builds upon the hypothesis that the spatial arrangement of topographic
features, such as buildings, roads and other land cover parcels, indicates how land is
used. The aim is to make this kind of high-level semantic information explicit within
topographic data. There is an increasing need to share and use data for a wider range of
purposes, and to make data more definitive, intelligent and accessible. Unfortunately,
we still encounter a gap between low-level data representations and high-level concepts
that typify human qualitative spatial reasoning. The thesis adopts an ontological
approach to bridge this gap and to derive functional information by using standard
reasoning mechanisms offered by logic-based knowledge representation formalisms. It
formulates a framework for the processes involved in interpreting land use information
from topographic maps. Land use is a high-level abstract concept, but it is also an
observable fact intimately tied to geography. By decomposing this relationship, the
thesis correlates a one-to-one mapping between high-level conceptualisations
established from human knowledge and real world entities represented in the data.
Based on a middle-out approach, it develops a conceptual model that incrementally
links different levels of detail, and thereby derives coarser, more meaningful
descriptions from more detailed ones. The thesis verifies its proposed ideas by
implementing an ontology describing the land use ‘residential area’ in the ontology
editor Protégé. By asserting knowledge about high-level concepts such as types of
dwellings, urban blocks and residential districts as well as individuals that link directly
to topographic features stored in the database, the reasoner successfully infers instances
of the defined classes. Despite current technological limitations, ontologies are a
promising way forward in the manner we handle and integrate geographic data,
especially with respect to how humans conceptualise geographic space
Corporate Smart Content Evaluation
Nowadays, a wide range of information sources are available due to the
evolution of web and collection of data. Plenty of these information are
consumable and usable by humans but not understandable and processable by
machines. Some data may be directly accessible in web pages or via data feeds,
but most of the meaningful existing data is hidden within deep web databases
and enterprise information systems. Besides the inability to access a wide
range of data, manual processing by humans is effortful, error-prone and not
contemporary any more. Semantic web technologies deliver capabilities for
machine-readable, exchangeable content and metadata for automatic processing
of content. The enrichment of heterogeneous data with background knowledge
described in ontologies induces re-usability and supports automatic processing
of data. The establishment of “Corporate Smart Content” (CSC) - semantically
enriched data with high information content with sufficient benefits in
economic areas - is the main focus of this study. We describe three actual
research areas in the field of CSC concerning scenarios and datasets
applicable for corporate applications, algorithms and research. Aspect-
oriented Ontology Development advances modular ontology development and
partial reuse of existing ontological knowledge. Complex Entity Recognition
enhances traditional entity recognition techniques to recognize clusters of
related textual information about entities. Semantic Pattern Mining combines
semantic web technologies with pattern learning to mine for complex models by
attaching background knowledge. This study introduces the afore-mentioned
topics by analyzing applicable scenarios with economic and industrial focus,
as well as research emphasis. Furthermore, a collection of existing datasets
for the given areas of interest is presented and evaluated. The target
audience includes researchers and developers of CSC technologies - people
interested in semantic web features, ontology development, automation,
extracting and mining valuable information in corporate environments. The aim
of this study is to provide a comprehensive and broad overview over the three
topics, give assistance for decision making in interesting scenarios and
choosing practical datasets for evaluating custom problem statements. Detailed
descriptions about attributes and metadata of the datasets should serve as
starting point for individual ideas and approaches
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