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.

A formal knowledge management ontology: Conduct, activities, resources, and influences

Abstract: This article describes a collaboratively engineered general-purpose knowledge management (KM) ontology that can be used by practitioners, researchers, and educators. The ontology is formally characterized in terms of nearly one hundred definitions and axioms that evolved from a Delphi-like process involving a diverse panel of over 30 KM practitioners and researchers. The ontology identifies and relates knowledge manipulation activities that an entity (e.g., an organization) can perform to operate on knowledge resources. It introduces a taxonomy for these resources, which indicates classes of knowledge that may be stored, embedded, and/or represented in an entity. It recognizes factors that influence the conduct of KM both within and across KM episodes. The Delphi panelists judge the ontology favorably overall: its ability to unify KM concepts, its comprehensiveness, and utility. Moreover, various implications of the ontology for the KM field are examined as indicators of its utility for practitioners, educators, and researchers.

OWL: Representing Information Using the Web Ontology Language

Abstract: Owl Representing Information Using The Web Ontology Language Pdf Book Download hosted by Zachary Baker on October 21 2018. It is a file download of Owl Representing Information Using The Web Ontology Language that you could be downloaded this with no cost on wa-cop.org. For your information, we do not host book downloadable Owl Representing Information Using The Web Ontology Language at wa-cop.org, this is only PDF generator result for the preview.

Definitions of tool integration for environments

Abstract: Tool integration is not a property of a single tool, but of its relationships with other elements in the environment, chiefly other tools, a platform, and a process. Tool integration is about the extent to which tools agree. The subject of these agreements may include data format, user-interface conventions, use of common functions, or other aspects of tool construction. A framework that determines how well tools are integrated into an environment and that defines integration independently of the mechanisms and approaches used to support integration is proposed. Process, data, control, and presentation integration properties are described separately so as to identify them as clearly and independently as possible. Emphasis is placed on definitions of integration properties on relationships between tools rather than on the specific integration-support mechanisms. >

SMART: Automated Support for Ontology Merging and Alignment

Abstract: As researchers in the ontology-design field develop the content of a growing number of ontologies, the need for sharing and reusing this body of knowledge becomes increasingly critical. Aligning and merging existing ontologies, which is usually handled manually, often constitutes a large and tedious portion of the sharing process. We have developed SMART, an algorithm that provides a semi-automatic approach to ontology merging and alignment. SMART assists the ontology developer by performing certain tasks automatically and by guiding the developer to other tasks for which his intervention is required. SMART also determines possible inconsistencies in the state of the ontology that may result from the user’s actions, and suggests ways to remedy these inconsistencies. We define the set of basic operations that are performed during merging and alignment of ontologies, and determine the effects that invocation of each of these operations has on the process. SMART is based on an extremely general knowledge model and, therefore, can be applied across various platforms. 1. Merging Versus Alignment In recent years, researchers have developed many ontologies. These different groups of researchers are now beginning to work with one another, so they must bring together these disparate source ontologies. Two approaches are possible: (1) merging the ontologies to create a single coherent ontology, or (2) aligning the ontologies by establishing links between them and allowing the aligned ontologies to reuse information from one another. As an illustration of the possible processes that establish correspondence between different ontologies, we consider the ontologies that natural languages embody. A researcher trying to find common ground between two such languages may perform one of several tasks. He may create a mapping between the two languages to be used in, say, a machine-translation system. Differences in the ontologies underlying the two languages often do not allow simple one-to-one correspondence, so a mapping must account for these differences. Alternatively, Esperanto language (an international language that was constructed from words in different European languages) was created through merging: All the languages and their underlying ontologies were combined to create a single language. Aligning languages (ontologies) is a third task. Consider how we learn a new domain language that has an extensive vocabulary, such as the language of medicine. The new ontology (the vocabulary of the medical domain) needs to be linked in our minds to the knowledge that we already have (our existing ontology of the world). The creation of these links is alignment. We consider merging and alignment in our work that we describe in this paper. For simplicity, throughout the discussion, we assume that only two ontologies are being merged or aligned at any given time. Figure 1 illustrates the difference between ontology merging and alignment. In merging, a single ontology that is a merged version of the original ontologies is created. Often, the original ontologies cover similar or overlapping domains. For example, the Unified Medical Language System (Humphreys and Lindberg 1993; UMLS 1999) is a large merged ontology that reconciles differences in terminology from various machine-readable biomedical information sources. Another example is the project that was merging the top-most

Semantic Integration in Heterogeneous Databases Using Neural Networks

Abstract: One important step in integrating heterogeneous databases is matching equivalent attributes: Determining which fields in two databases refer to the same data. The meaning of information may be embodied within a database model, a conceptual schema, application programs, or data contents. Integration involves extracting semantics, expressing them as metadata, and matching semantically equivalent data elements. We present a procedure using a classifier to categorize attributes according to their field specifications and data values, then train a neural network to recognize similar attributes. In our technique, the knowledge of how to match equivalent data elements is "discovered" from metadata, not "pre-programmed".