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.

Learning Expressive Ontologies

Abstract: The automatic extraction of ontologies from text and lexical resources has become more and more mature. Nowadays, the results of state-of-the-art ontology learning methods are already good enough for many practical applications. However, most of them aim at generating rather inexpressive ontologies, i.e. bare taxonomies and relationships, whereas many reasoning-based applications in domains such as bioinformatics or medicine rely on much more complex axiomatizations. Those are extremely expensive if built by purely manual efforts, and methods for the automatic or semi-automatic construction of expressive ontologies could help to overcome the knowledge acquisition bottleneck. At the same time, a tight integration with ontology evaluation and debugging approaches is required to reduce the amount of manual post-processing which becomes harder the more complex learned ontologies are. Particularly, the treatment of logical inconsistencies, mostly neglected by existing ontology learning frameworks, becomes a great challenge as soon as we start to learn huge and expressive axiomatizations. In this chapter we present several approaches for the automatic generation of expressive ontologies along with a detailed discussion of the key problems and challenges in learning complex OWL ontologies. We also suggest ways to handle different types of inconsistencies in learned ontologies, and conclude with a visionary outlook to future ontology learning and engineering environments.

Role-Based Access-Control Using Reference Ontology in Clouds

Abstract: In cloud computing, security is an important issue due to the increasing scale of users. Current approaches to access control on clouds do not scale well to multi-tenancy requirements because they are mostly based on individual user IDs at different granularity levels. However, the number of users can be enormous and causes significant overhead in managing security. RBAC (Role-Based Access Control) is attractive because the number of roles is significantly less, and users can be classified according to their roles. This paper proposes a RBAC model using a role ontology for Multi-Tenancy Architecture (MTA) in clouds. The ontology is used to build up the role hierarchy for a specific domain. Ontology transformation operations algorithms are provided to compare the similarity of different ontology. The proposed framework can ease the design of security system in cloud and reduce the complexity of system design and implementation.

Knowledge-driven multimedia information extraction and ontology evolution: bridging the semantic gap

Abstract: Bootstrapping Ontology Evolution with Multimedia Information Extraction.- Semantic Representation of Multimedia Content.- Semantics Extraction from Images.- Ontology Based Information Extraction from Text.- Logical Formalization of Multimedia Interpretation.- Ontology Population and Enrichment: State of the Art.- Ontology and Instance Matching.- A Survey of Semantic Image and Video Annotation Tools.

The use of foundational ontologies in ontology development: an empirical assessment

Abstract: There is an assumption that ontology developers will use a top-down approach by using a foundational ontology, because it purportedly speeds up ontology development and improves quality and interoperability of the domain ontology. Informal assessment of these assumptions reveals ambiguous results that are not only open to different interpretations but also such that foundational ontology usage is not foreseen in most methodologies. Therefore, we investigated these assumptions in a controlled experiment. After a lecture about DOLCE, BFO, and partwhole relations, one-third chose to start domain ontology development with an OWLized foundational ontology. On average, those who commenced with a foundational ontology added more new classes and class axioms, and significantly less object properties than those who started from scratch. No ontology contained errors regarding part-of vs. is-a. The comprehensive results show that the 'cost' incurred spending time getting acquainted with a foundational ontology compared to starting from scratch was more than made up for in size, understandability, and interoperability already within the limited time frame of the experiment.