56 research outputs found
Introducing fuzzy quantification in OWL 2 ontologies
In this paper, we briefly report our latest achievements in fuzzy granulation of OWL 2 ontologies. More precisely, we extend a previously presented method in order to address a new class of sentences with fuzzy quantifier
Dealing with uncertain entities in ontology alignment using rough sets
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision
The TOG Conclusions Background
Information overload, (non-)interoperability of software tool
A finder and representation system for knowledge carriers based on granular computing
In one of his publications Aristotle states âAll human beings by their nature desire to knowâ [Kraut 1991]. This desire is initiated the day we are born and accompanies us for the rest of our life. While at a young age our parents serve as one of the principle sources for knowledge, this changes over the course of time. Technological advances and particularly the introduction of the Internet, have given us new possibilities to share and access knowledge from almost anywhere at any given time. Being able to access and share large collections of written down knowledge is only one part of the equation. Just as important is the internalization of it, which in many cases can prove to be difficult to accomplish. Hence, being able to request assistance from someone who holds the necessary knowledge is of great importance, as it can positively stimulate the internalization procedure. However, digitalization does not only provide a larger pool of knowledge sources to choose from but also more people that can be potentially activated, in a bid to receive personalized assistance with a given problem statement or question. While this is beneficial, it imposes the issue that it is hard to keep track of who knows what. For this task so-called Expert Finder Systems have been introduced, which are designed to identify and suggest the most suited candidates to provide assistance. Throughout this Ph.D. thesis a novel type of Expert Finder System will be introduced that is capable of capturing the knowledge users within a community hold, from explicit and implicit data sources. This is accomplished with the use of granular computing, natural language processing and a set of metrics that have been introduced to measure and compare the suitability of candidates. Furthermore, are the knowledge requirements of a problem statement or question being assessed, in order to ensure that only the most suited candidates are being recommended to provide assistance
Automatic domain ontology extraction for context-sensitive opinion mining
Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizationsâ business strategy development and individual consumersâ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline
HIERARCHICAL-GRANULARITY HOLONIC MODELLING
This thesis aims to introduce an agent-based system engineering approach,
named Hierarchical-Granularity Holonic Modelling, to support intelligent
information processing at multiple granularity levels. The focus is especially
on complex hierarchical systems.
Nowadays, due to ever growing complexity of information systems and
processes, there is an increasing need of a simple self-modular computational
model able to manage data and perform information granulation at different
resolutions (i.e., both spatial and temporal). The current literature lacks to
provide such a methodology. To cite a relevant example, the object-oriented
paradigm is suitable for describing a system at a given representation level;
notwithstanding, further design effort is needed if a more synthetical of more
analytical view of the same system is required.
In the literature, the agent paradigm represents a viable solution in complex
systems modelling; in particular, Multi-Agent Systems have been applied with
success in a countless variety of distributed intelligence settings. Current
agent-oriented implementations however suffer from an apparent dichotomy
between agents as intelligent entities and agents\u2019 structures as superimposed
hierarchies of roles within a given organization. The agents\u2019 architectures are
often rigid and require intense re-engineering when the underpinning ontology
is updated to cast new design criteria.
The latest stage in the evolution of modelling frameworks is represented by
Holonic Systems, based on the notion of \u2018holon\u2019 and \u2018holarchy\u2019 (i.e.,
hierarchy of holons). A holon, just like an agent, is an intelligent entity able to
interact with the environment and to take decisions to solve a specific
problem. Contrarily to agent, holon has the noteworthy property of playing the
role of a whole and a part at the same time. This reflects at the organizational
level: holarchy functions first as autonomous wholes in supra-ordination to
their parts, secondly as dependent parts in sub-ordination to controls on higher
levels, and thirdly in coordination with their local environment.
These ideas were originally devised by Arthur Koestler in 1967. Since then,
Holonic Systems have gained more and more credit in various fields such as
Biology, Ecology, Theory of Emergence and Intelligent Manufacturing.
Notwithstanding, with respect to these disciplines, fewer works on Holonic
Systems can be found in the general framework of Artificial and
Computational Intelligence. Moreover, the distance between theoretic models
and actual implementation is still wide open.
In this thesis, starting from the Koestler\u2019s original idea, we devise a novel
agent-inspired model that merges intelligence with the holonic structure at
multiple hierarchical-granularity levels. This is made possible thanks to a rule-based
knowledge recursive representation, which allows the holonic agent to
carry out both operating and learning tasks in a hierarchy of granularity levels.
The proposed model can be directly used in terms of hardware/software
applications. This endows systems and software engineers with a modular and
scalable approach when dealing with complex hierarchical systems. In order
to support our claims, exemplar experiments of our proposal are shown and
prospective implications are commented
Cognitive Models and Computational Approaches for improving Situation Awareness Systems
2016 - 2017The world of Internet of Things is pervaded by complex environments
with smart services available every time and everywhere. In
such a context, a serious open issue is the capability of information
systems to support adaptive and collaborative decision processes
in perceiving and elaborating huge amounts of data. This requires
the design and realization of novel socio-technical systems based on
the âhuman-in-the-loopâ paradigm. The presence of both humans
and software in such systems demands for adequate levels of Situation
Awareness (SA). To achieve and maintain proper levels of
SA is a daunting task due to the intrinsic technical characteristics
of systems and the limitations of human cognitive mechanisms.
In the scientific literature, such issues hindering the SA formation
process are defined as SA demons.
The objective of this research is to contribute to the resolution
of the SA demons by means of the identification of information
processing paradigms for an original support to the SA and the
definition of new theoretical and practical approaches based on
cognitive models and computational techniques.
The research work starts with an in-depth analysis and some
preliminary verifications of methods, techniques, and systems of
SA. A major outcome of this analysis is that there is only a limited
use of the Granular Computing paradigm (GrC) in the SA
field, despite the fact that SA and GrC share many concepts and
principles. The research work continues with the definition of contributions
and original results for the resolution of significant SA
demons, exploiting some of the approaches identified in the analysis
phase (i.e., ontologies, data mining, and GrC). The first contribution addresses the issues related to the bad perception of data
by users. We propose a semantic approach for the quality-aware
sensor data management which uses a data imputation technique
based on association rule mining. The second contribution proposes
an original ontological approach to situation management,
namely the Adaptive Goal-driven Situation Management. The approach
uses the ontological modeling of goals and situations and
a mechanism that suggests the most relevant goals to the users at
a given moment. Lastly, the adoption of the GrC paradigm allows
the definition of a novel model for representing and reasoning
on situations based on a set theoretical framework. This model
has been instantiated using the rough sets theory. The proposed
approaches and models have been implemented in prototypical systems.
Their capabilities in improving SA in real applications have
been evaluated with typical methodologies used for SA systems. [edited by Author]XXX cicl
Applications of comparators in data processing systems
This paper shows practical examples of compound object comparators and the application of the theory in various fields related to data processing systems. One can also find the necessary theoretical background needed to understand the examples
An Ontology-Based Interpretable Fuzzy Decision Support System for Diabetes Diagnosis
Diabetes is a serious chronic disease. The importance of clinical decision support systems (CDSSs) to diagnose diabetes has led to extensive research efforts to improve the accuracy, applicability, interpretability, and interoperability of these systems. However, this problem continues to require optimization. Fuzzy rule-based systems are suitable for the medical domain, where interpretability is a main concern. The medical domain is data-intensive, and using electronic health record data to build the FRBS knowledge base and fuzzy sets is critical. Multiple variables are frequently required to determine a correct and personalized diagnosis, which usually makes it difficult to arrive at accurate and timely decisions. In this paper, we propose and implement a new semantically interpretable FRBS framework for diabetes diagnosis. The framework uses multiple aspects of knowledge-fuzzy inference, ontology reasoning, and a fuzzy analytical hierarchy process (FAHP) to provide a more intuitive and accurate design. First, we build a two-layered hierarchical and interpretable FRBS; then, we improve this by integrating an ontology reasoning process based on SNOMED CT standard ontology. We incorporate FAHP to determine the relative medical importance of each sub-FRBS. The proposed system offers numerous unique and critical improvements regarding the implementation of an accurate, dynamic, semantically intelligent, and interpretable CDSS. The designed system considers the ontology semantic similarity of diabetes complications and symptoms concepts in the fuzzy rules' evaluation process. The framework was tested using a real data set, and the results indicate how the proposed system helps physicians and patients to accurately diagnose diabetes mellitusThis work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science, ICT and Future Planning)-NRF-2017R1A2B2012337)S
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