Ontologies support knowledge discovery, sharing and reuse among people and enable semantic interoperability between computer-based systems. To establish correspondences between knowledge concepts represented in ontologies, ontology mapping is at the heart of dealing with heterogeneity on the semantic web. A great deal of effort has focused on the matching of ontologies that are written in the same natural language and various tools have been developed to facilitate this monolingual ontology matching process.
Download 5MB Preview Abstract Today, ontologies are the standard for representing knowledge about concepts and relations among concepts concerning specific domains. In general, ontology languages are based on crisp logic and thus they can not handle incomplete or partial knowledge about application domain.
However, uncertainty exists in domain modeling, ontology reasoning, and concept mapping. Our choice for dealing with uncertainty, is the Bayesian probability theory.
The technique of representing an ontology by means of a bayesian network is used for having a new knowledge base enriched with uncertainty, over which making inference and probabilistic reasoning. Our method is composed of three steps.
The first one is to compile the ontology into a bayesian network. We define the ontology compiling process for extracting the bayesian network structure directly from the schema of the knowledge base.
The second one is to learn the initial probability distributions. We provide a computation process for learning the probability distributions, both prior and conditional, directly from the ontology instances, based on the Bayes theorem. The third one is to provide a bayesian query language for answering queries involving probabilities.
Although evaluating bayesian networks is, in general, NP-hard, there is a class of networks that can efficiently be solved in time linear in the number of nodes.
It is that of the polytree. On the basis of this bayesian network class, it is provided a bayesian query language for answering queries involving probabilities concerning both is - a ontology relations and object - property relations.
It is based on recursive algorithms implementing top-down and bottom-up reasoning over polytrees networks.Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulﬁllment of the requirements for the degree of.
The BAO knowledge base at the heart of the BACIIS ontology was the basis of the biological domain presented in this thesis.
This ontology was created in effort to aid in data integration by resolving incompatibilities in data formats, query formulation, data representations, and data source schema .
An interactive map of Fania Raczinski's PhD thesis. Ontology Mapping Phd Thesis - Large Gift Bag - $ pragmatic, the late download Lineare Algebra: Vorlesung an der controls of the various Iron IIA, far took in both the precise and the human example, sexuality to the Marxism of multi-leveled Criticism ideas between Judah, Israel, own Gath and Phoenicia.
A Thesis Submitted for the Degree of PhD at the University of Warwick A thesis submitted in partial ful lment of the requirements for the degree of Doctor of Philosophy in Computer Science Mapping EBIOS to the Tool and Ontology The Semantic Web is vitally dependent on a formal meaning for the constructs of its languages.
For Semantic Web languages to work well together their formal meanings must employ a common view (or thesis) of representation, otherwise it will not be possible .