Ontology-based data integration

About: Ontology-based data integration is a research topic. Over the lifetime, 11065 publications have been published within this topic receiving 216888 citations.

Ontology-Based Meta-Mining of Knowledge Discovery Workflows

Abstract: This chapter describes a principled approach to meta-learning that has three distinctive features. First, whereas most previous work on meta-learning focused exclusively on the learning task, our approach applies meta-learning to the full knowledge discovery process and is thus more aptly referred to as meta-mining. Second, traditional meta-learning regards learning algorithms as black boxes and essentially correlates properties of their input (data) with the performance of their output (learned model). We propose to tear open the black box and analyse algorithms in terms of their core components, their underlying assumptions, the cost functions and optimization strategies they use, and the models and decision boundaries they generate. Third, to ground meta-mining on a declarative representation of the data mining (dm) process and its components, we built a DM ontology and knowledge base using the Web Ontology Language (owl).

Extracting Modules from Ontologies: A Logic-Based Approach

Abstract: The ability to extract meaningful fragments from an ontology is essential for ontology reuse. We propose a definition of a module that guarantees to completely capture the meaning of a given set of terms, i.e., to include all axioms relevant to the meaning of these terms. We show that the problem of determining whether a subset of an ontology is a module for a given vocabulary is undecidable even for OWL DL. Given these negative results, we propose sufficient conditions for a for a fragment of an ontology to be a module. We propose an algorithm for computing modules based on those conditions and present our experimental results on a set of real-world ontologies of varying size and complexity.

Network control and management using semantic reasoners in a network environment

Abstract: An example method for facilitating network control and management using semantic reasoners in a network environment is provided and includes generating a fully populated semantics model of the network from network data according to a base network ontology of the network, mapping the fully populated semantics model to a network knowledge base, feeding contents of the network knowledge base to a semantic reasoner, and controlling and managing the network using the semantic reasoner. In specific embodiments, generating the model includes receiving the network data from the network, parsing the network data, loading the parsed network data into in-memory data structures, accessing a manifest specifying binding between a network data definition format and ontology components of the base network ontology, identifying ontology components associated with the network data based on the manifest, and populating the identified ontology components with individuals and properties from the corresponding data structures.