317 research outputs found
A Context-Oriented Extension of F#
Context-Oriented programming languages provide us with primitive constructs
to adapt program behaviour depending on the evolution of their operational
environment, namely the context. In previous work we proposed ML_CoDa, a
context-oriented language with two-components: a declarative constituent for
programming the context and a functional one for computing. This paper
describes the implementation of ML_CoDa as an extension of F#.Comment: In Proceedings FOCLASA 2015, arXiv:1512.0694
Expressiveness of Temporal Query Languages: On the Modelling of Intervals, Interval Relationships and States
Storing and retrieving time-related information are important, or even critical, tasks on many areas of Computer Science (CS) and in particular for Artificial Intelligence (AI). The expressive power of temporal databases/query languages has been studied from different perspectives, but the kind of temporal information they are able to store and retrieve is not always conveniently addressed. Here we assess a number of temporal query languages with respect to the modelling of time intervals, interval relationships and states, which can be thought of as the building blocks to represent and reason about a large and important class of historic information. To survey the facilities and issues which are particular to certain temporal query languages not only gives an idea about how useful they can be in particular contexts, but also gives an interesting insight in how these issues are, in many cases, ultimately inherent to the database paradigm. While in the area of AI declarative languages are usually the preferred choice, other areas of CS heavily rely on the extended relational paradigm. This paper, then, will be concerned with the representation of historic information in two well known temporal query languages: it Templog in the context of temporal deductive databases, and it TSQL2 in the context of temporal relational databases. We hope the results highlighted here will increase cross-fertilisation between different communities. This article can be related to recent publications drawing the attention towards the different approaches followed by the Databases and AI communities when using time-related concepts
05061 Abstracts Collection -- Foundations of Semistructured Data
From 06.02.05 to 11.02.05, the Dagstuhl Seminar
05061 ``Foundations of Semistructured Data\u27\u27 was held
in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Semantically defined Analytics for Industrial Equipment Diagnostics
In this age of digitalization, industries everywhere accumulate massive amount of data such that it has become the lifeblood of the global economy. This data may come from various heterogeneous systems, equipment, components, sensors, systems and applications in many varieties (diversity of sources), velocities (high rate of changes) and volumes (sheer data size).
Despite significant advances in the ability to collect, store, manage and filter data, the real value lies in the analytics. Raw data is meaningless, unless it is properly processed to actionable (business) insights. Those that know how to harness data effectively, have a decisive competitive advantage, through raising performance by making faster and smart decisions, improving short and long-term strategic planning, offering more user-centric products and services and fostering innovation. Two distinct paradigms in practice can be discerned within the field of analytics: semantic-driven (deductive) and data-driven (inductive).
The first emphasizes logic as a way of representing the domain knowledge encoded in rules or ontologies and are often carefully curated and maintained. However, these models are often highly complex, and require intensive knowledge processing capabilities. Data-driven analytics employ machine learning (ML) to directly learn a model from the data with minimal human intervention. However, these models are tuned to trained data and context, making it difficult to adapt.
Industries today that want to create value from data must master these paradigms in combination. However, there is great need in data analytics to seamlessly combine semantic-driven and data-driven processing techniques in an efficient and scalable architecture that allows extracting actionable insights from an extreme variety of data. In this thesis, we address these needs by providing:
âą A unified representation of domain-specific and analytical semantics, in form of ontology models called TechOnto Ontology Stack. It is highly expressive, platform-independent formalism to capture conceptual semantics of industrial systems such as technical system hierarchies, component partonomies etc and its analytical functional semantics.
âą A new ontology language Semantically defined Analytical Language (SAL) on top of the ontology model that extends existing DatalogMTL (a Horn fragment of Metric Temporal Logic) with analytical functions as first class citizens.
âą A method to generate semantic workflows using our SAL language. It helps in authoring, reusing and maintaining complex analytical tasks and workflows in an abstract fashion.
âą A multi-layer architecture that fuses knowledge- and data-driven analytics into a federated and distributed solution.
To our knowledge, the work in this thesis is one of the first works to introduce and investigate the use of the semantically defined analytics in an ontology-based data access setting for industrial analytical applications. The reason behind focusing our work and evaluation on industrial data is due to (i) the adoption of semantic technology by the industries in general, and (ii) the common need in literature and
in practice to allow domain expertise to drive the data analytics on semantically interoperable sources, while still harnessing the power of analytics to enable real-time data insights. Given the evaluation results of three use-case studies, our approach surpass state-of-the-art approaches for most application scenarios.Im Zeitalter der Digitalisierung sammeln die Industrien ĂŒberall massive Daten-mengen, die zum Lebenselixier der Weltwirtschaft geworden sind. Diese Daten können aus verschiedenen heterogenen Systemen, GerĂ€ten, Komponenten, Sensoren, Systemen und Anwendungen in vielen Varianten (Vielfalt der Quellen), Geschwindigkeiten (hohe Ănderungsrate) und Volumina (reine DatengröĂe) stammen.
Trotz erheblicher Fortschritte in der FĂ€higkeit, Daten zu sammeln, zu speichern, zu verwalten und zu filtern, liegt der eigentliche Wert in der Analytik. Rohdaten sind bedeutungslos, es sei denn, sie werden ordnungsgemÀà zu verwertbaren (GeschĂ€fts-)Erkenntnissen verarbeitet. Wer weiĂ, wie man Daten effektiv nutzt, hat einen entscheidenden Wettbewerbsvorteil, indem er die Leistung steigert, indem er schnellere und intelligentere Entscheidungen trifft, die kurz- und langfristige strategische Planung verbessert, mehr benutzerorientierte Produkte und Dienstleistungen anbietet und Innovationen fördert. In der Praxis lassen sich im Bereich der Analytik zwei unterschiedliche Paradigmen unterscheiden: semantisch (deduktiv) und Daten getrieben (induktiv).
Die erste betont die Logik als eine Möglichkeit, das in Regeln oder Ontologien kodierte DomÀnen-wissen darzustellen, und wird oft sorgfÀltig kuratiert und gepflegt. Diese Modelle sind jedoch oft sehr komplex und erfordern eine intensive Wissensverarbeitung. Datengesteuerte Analysen verwenden maschinelles Lernen (ML), um mit minimalem menschlichen Eingriff direkt ein Modell aus den Daten zu lernen. Diese Modelle sind jedoch auf trainierte Daten und Kontext abgestimmt, was die Anpassung erschwert.
Branchen, die heute Wert aus Daten schaffen wollen, mĂŒssen diese Paradigmen in Kombination meistern. Es besteht jedoch ein groĂer Bedarf in der Daten-analytik, semantisch und datengesteuerte Verarbeitungstechniken nahtlos in einer effizienten und skalierbaren Architektur zu kombinieren, die es ermöglicht, aus einer extremen Datenvielfalt verwertbare Erkenntnisse zu gewinnen. In dieser Arbeit, die wir auf diese BedĂŒrfnisse durch die Bereitstellung:
⹠Eine einheitliche Darstellung der DomÀnen-spezifischen und analytischen Semantik in Form von Ontologie Modellen, genannt TechOnto Ontology Stack. Es ist ein hoch-expressiver, plattformunabhÀngiger Formalismus, die konzeptionelle Semantik industrieller Systeme wie technischer Systemhierarchien, Komponenten-partonomien usw. und deren analytische funktionale Semantik zu erfassen.
âą Eine neue Ontologie-Sprache Semantically defined Analytical Language (SAL) auf Basis des Ontologie-Modells das bestehende DatalogMTL (ein Horn fragment der metrischen temporĂ€ren Logik) um analytische Funktionen als erstklassige BĂŒrger erweitert.
âą Eine Methode zur Erzeugung semantischer workflows mit unserer SAL-Sprache. Es hilft bei der Erstellung, Wiederverwendung und Wartung komplexer analytischer Aufgaben und workflows auf abstrakte Weise.
⹠Eine mehrschichtige Architektur, die Wissens- und datengesteuerte Analysen zu einer föderierten und verteilten Lösung verschmilzt.
Nach unserem Wissen, die Arbeit in dieser Arbeit ist eines der ersten Werke zur EinfĂŒhrung und Untersuchung der Verwendung der semantisch definierten Analytik in einer Ontologie-basierten Datenzugriff Einstellung fĂŒr industrielle analytische Anwendungen. Der Grund fĂŒr die Fokussierung unserer Arbeit und Evaluierung auf industrielle Daten ist auf (i) die Ăbernahme semantischer Technologien durch die Industrie im Allgemeinen und (ii) den gemeinsamen Bedarf in der Literatur und in der Praxis zurĂŒckzufĂŒhren, der es der Fachkompetenz ermöglicht, die Datenanalyse auf semantisch inter-operablen Quellen voranzutreiben, und nutzen gleichzeitig die LeistungsfĂ€higkeit der Analytik, um Echtzeit-Daten-einblicke zu ermöglichen. Aufgrund der Evaluierungsergebnisse von drei AnwendungsfĂ€llen Ăbertritt unser Ansatz fĂŒr die meisten Anwendungsszenarien Modernste AnsĂ€tze
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 â 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 â 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 â they do not introduce negation if it is not present in the input
program â and only the affected part of a predicateâs 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
Asynchronous Multi-Context Systems
In this work, we present asynchronous multi-context systems (aMCSs), which
provide a framework for loosely coupling different knowledge representation
formalisms that allows for online reasoning in a dynamic environment. Systems
of this kind may interact with the outside world via input and output streams
and may therefore react to a continuous flow of external information. In
contrast to recent proposals, contexts in an aMCS communicate with each other
in an asynchronous way which fits the needs of many application domains and is
beneficial for scalability. The federal semantics of aMCSs renders our
framework an integration approach rather than a knowledge representation
formalism itself. We illustrate the introduced concepts by means of an example
scenario dealing with rescue services. In addition, we compare aMCSs to
reactive multi-context systems and describe how to simulate the latter with our
novel approach.Comment: International Workshop on Reactive Concepts in Knowledge
Representation (ReactKnow 2014), co-located with the 21st European Conference
on Artificial Intelligence (ECAI 2014). Proceedings of the International
Workshop on Reactive Concepts in Knowledge Representation (ReactKnow 2014),
pages 31-37, technical report, ISSN 1430-3701, Leipzig University, 2014.
http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-15056
Context-aware security: Linguistic mechanisms and static analysis
Adaptive systems improve their efficiency by modifying their behaviour to respond to changes in their operational
environment. Also, security must adapt to these changes and policy enforcement becomes dependent on the dynamic contexts.
We study these issues within MLCoDa, (the core of) an adaptive declarative language proposed recently. A main characteristic
of MLCoDa is to have two components: a logical one for handling the context and a functional one for computing. We extend
this language with security policies that are expressed in logical terms. They are of two different kinds: context and application
policies. The first, unknown a priori to an application, protect the context from unwanted changes. The others protect the
applications from malicious actions of the context, can be nested and can be activated and deactivated according to their scope.
An execution step can only occur if all the policies in force hold, under the control of an execution monitor. Beneficial to this is
a type and effect system, which safely approximates the behaviour of an application, and a further static analysis, based on the
computed effect. The last analysis can only be carried on at load time, when the execution context is known, and it enables us to
efficiently enforce the security policies on the code execution, by instrumenting applications. The monitor is thus implemented
within MLCoDa, and it is only activated on those policies that may be infringed, and switched off otherwise
Using semantic web technologies for exploratory OLAP: A survey
Peer ReviewedPostprint (authorâs final draft
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
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