Syntactic and semantic metadata integration for science data use

Abstract

This paper proposes a novel metadata solution to allow applications to intelligently use science data in an automated fashion. The solution provides rich syntactic and semantic metadata, where the semantic metadata is linked with an ontology to define the semantic terms. This solution allows applications to exploit the syntactic metadata to read the data and the semantic metadata to infer the content and the meaning of the data. The solution presented in this paper leverages the Earth Science Markup Language for providing the syntactic metadata and adds a semantic metadata component along with links to the appropriate ontology. This new semantic component is orthogonal to the syntactic metadata, so it does not perturb the existing design. An example application was designed and built that integrates this syntactic and semantic metadata via an ontology to perform a data processing operation.

Introduction

Metadata, or data about data, for science data sets can be classified into three general categories: content, syntactic and semantic. Content or “search” metadata is a broad category describing the intrinsic content of the data. Content metadata typically describe the physical parameters or variables measured in a data set, its spatio-temporal coverage, coordinate systems used, information about the data producer, provenance and other keywords. Content metadata typically populate data catalogs and registries. Syntactic metadata describe the structure of the data file in terms of bits, bytes, data type, arrays and structures. This information is often found in README files accompanying science data. For some data formats, this information is embedded in the data file and an accompanying software library is used to create or read the files in these formats. Finally, semantic or “use” metadata provide meaning to the data, relating the content of the data file to some known context. Such semantic information may be found in documentation or publications about a data set. Current semantic web research is aimed at encoding semantic information in ontologies in order to enable more powerful and intelligent automated data search and usage.

To accommodate the rapid growth of Earth Science data, scientific investigations require the ability to use new data sets with minimal effort. With the emergence of Semantic Web (Berners-Lee et al., 2001) concepts, intelligently automating services has become an area of active research. Extending this idea of intelligently automating services to the Earth Science domain requires solving several critical issues related to the varying level of metadata richness associated with different geosciences data sets. This paper proposes a metadata solution by integrating syntactic with semantic metadata to automate data use. This solution assembles orthogonal and yet synergistic information to provide a rich description of the syntax and semantics of scientific data sets. Furthermore, data analysis and other applications are provided a context for the semantic metadata via an ontology. Applications can use this rich set of metadata with the ontology to make automated decisions to achieve data processing goals. This paper describes an example application that uses this metadata information to automate and drive a useful data preprocessing capability.

The concept of integrating syntactic and semantic metadata for data use is not new (Cornillon et al., 2003). However, the solution presented in this paper is the first of its kind in Earth Science: the metadata contains both syntactic and semantic information; the semantic metadata is linked to an ontology to provide context; and an application is designed and built to use this metadata and the ontology to perform data processing.