153 research outputs found
Towards Ontology-Based Program Analysis
Program analysis is fundamental for program optimizations, debugging,
and many other tasks. But developing program analyses has been a
challenging and error-prone process for general users. Declarative
program analysis has shown the promise to dramatically improve the
productivity in the development of program analyses. Current
declarative program analysis is however subject to some major
limitations in supporting cooperations among analysis tools, guiding
program optimizations, and often requires much effort for repeated
program preprocessing.
In this work, we advocate the integration of ontology into declarative
program analysis. As a way to standardize the definitions of concepts
in a domain and the representation of the knowledge in the domain,
ontology offers a promising way to address the limitations of current
declarative program analysis. We develop a prototype framework named
PATO for conducting program analysis upon ontology-based program
representation. Experiments on six program analyses confirm the
potential of ontology for complementing existing declarative program
analysis. It supports multiple analyses without separate program
preprocessing, promotes cooperative Liveness analysis between two
compilers, and effectively guides a data placement optimization for
Graphic Processing Units (GPU)
Approximate Assertional Reasoning Over Expressive Ontologies
In this thesis, approximate reasoning methods for scalable assertional reasoning are provided whose computational properties can be established in a well-understood way, namely in terms of soundness and completeness, and whose quality can be analyzed in terms of statistical measurements, namely recall and precision. The basic idea of these approximate reasoning methods is to speed up reasoning by trading off the quality of reasoning results against increased speed
Reason Maintenance - Conceptual Framework
This paper describes the conceptual framework for reason maintenance developed as part of
WP2
A semantic and agent-based approach to support information retrieval, interoperability and multi-lateral viewpoints for heterogeneous environmental databases
PhDData stored in individual autonomous databases often needs to be combined and
interrelated. For example, in the Inland Water (IW) environment monitoring domain,
the spatial and temporal variation of measurements of different water quality indicators
stored in different databases are of interest. Data from multiple data sources is more
complex to combine when there is a lack of metadata in a computation forin and when
the syntax and semantics of the stored data models are heterogeneous. The main types
of information retrieval (IR) requirements are query transparency and data
harmonisation for data interoperability and support for multiple user views. A
combined Semantic Web based and Agent based distributed system framework has
been developed to support the above IR requirements. It has been implemented using
the Jena ontology and JADE agent toolkits. The semantic part supports the
interoperability of autonomous data sources by merging their intensional data, using a
Global-As-View or GAV approach, into a global semantic model, represented in
DAML+OIL and in OWL. This is used to mediate between different local database
views. The agent part provides the semantic services to import, align and parse
semantic metadata instances, to support data mediation and to reason about data
mappings during alignment. The framework has applied to support information
retrieval, interoperability and multi-lateral viewpoints for four European environmental
agency databases.
An extended GAV approach has been developed and applied to handle queries that can
be reformulated over multiple user views of the stored data. This allows users to
retrieve data in a conceptualisation that is better suited to them rather than to have to
understand the entire detailed global view conceptualisation. User viewpoints are
derived from the global ontology or existing viewpoints of it. This has the advantage
that it reduces the number of potential conceptualisations and their associated
mappings to be more computationally manageable. Whereas an ad hoc framework
based upon conventional distributed programming language and a rule framework
could be used to support user views and adaptation to user views, a more formal
framework has the benefit in that it can support reasoning about the consistency,
equivalence, containment and conflict resolution when traversing data models. A
preliminary formulation of the formal model has been undertaken and is based upon
extending a Datalog type algebra with hierarchical, attribute and instance value
operators. These operators can be applied to support compositional mapping and
consistency checking of data views. The multiple viewpoint system was implemented
as a Java-based application consisting of two sub-systems, one for viewpoint
adaptation and management, the other for query processing and query result
adjustment
Constructive Reasoning for Semantic Wikis
One of the main design goals of social software, such as wikis, is to
support and facilitate interaction and collaboration. This dissertation
explores challenges that arise from extending social software with
advanced facilities such as reasoning and semantic annotations and
presents tools in form of a conceptual model, structured tags, a rule
language, and a set of novel forward chaining and reason maintenance
methods for processing such rules that help to overcome the
challenges.
Wikis and semantic wikis were usually developed in an ad-hoc
manner, without much thought about the underlying concepts. A conceptual
model suitable for a semantic wiki that takes advanced features
such as annotations and reasoning into account is proposed. Moreover,
so called structured tags are proposed as a semi-formal knowledge
representation step between informal and formal annotations.
The focus of rule languages for the Semantic Web has been predominantly
on expert users and on the interplay of rule languages
and ontologies. KWRL, the KiWi Rule Language, is proposed as a
rule language for a semantic wiki that is easily understandable for
users as it is aware of the conceptual model of a wiki and as it
is inconsistency-tolerant, and that can be efficiently evaluated as it
builds upon Datalog concepts.
The requirement for fast response times of interactive software
translates in our work to bottom-up evaluation (materialization) of
rules (views) ahead of time – that is when rules or data change, not
when they are queried. Materialized views have to be updated when
data or rules change. While incremental view maintenance was intensively
studied in the past and literature on the subject is abundant,
the existing methods have surprisingly many disadvantages – they
do not provide all information desirable for explanation of derived
information, they require evaluation of possibly substantially larger
Datalog programs with negation, they recompute the whole extension
of a predicate even if only a small part of it is affected by a
change, they require adaptation for handling general rule changes.
A particular contribution of this dissertation consists in a set of
forward chaining and reason maintenance methods with a simple declarative
description that are efficient and derive and maintain information
necessary for reason maintenance and explanation. The reasoning
methods and most of the reason maintenance methods are described
in terms of a set of extended immediate consequence operators the
properties of which are proven in the classical logical programming
framework. In contrast to existing methods, the reason maintenance methods in this dissertation work by evaluating the original Datalog
program – they do not introduce negation if it is not present in the input
program – and only the affected part of a predicate’s extension is
recomputed. Moreover, our methods directly handle changes in both
data and rules; a rule change does not need to be handled as a special
case.
A framework of support graphs, a data structure inspired by justification
graphs of classical reason maintenance, is proposed. Support
graphs enable a unified description and a formal comparison of the
various reasoning and reason maintenance methods and define a notion
of a derivation such that the number of derivations of an atom is
always finite even in the recursive Datalog case.
A practical approach to implementing reasoning, reason maintenance,
and explanation in the KiWi semantic platform is also investigated. It
is shown how an implementation may benefit from using a graph
database instead of or along with a relational database
Ontology-Based Digital Twin Framework for Smart Factories
In modern smart factories we have multiple entities that interact with one another, such as worker-assistance system, robot collaboration and their corresponding software modules. To fa- cilitate seamless cooperation between those subsystems, it is beneficial that they all have access to one coherent environment model. Hence, we propose an ontology-based Digital Twin that al- lows semantic representation of all important parts of such a scenario. It allows uniform access for different application components such as intention recognition and robotic action planning. Furthermore, it provides information tailored to the needs of those different components, e.g., via different zoom levels and affordances
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