The Semantic Web for Earth and Environmental Terminology (SWEET) is a NASA-funded prototype to explore the technological challenges in making the semantic Web a reality for earth system science data and information. SWEET includes an extensive collection of ontologies written in the Ontology Web Language (OWL). These ontologies encompass both faceted (decomposed) concepts and integrative (unifying) concepts. Taken together, these enable a representation of the two science processes of reductionism and synthesis. SWEET stores ontology elements optionally in a DBMS (database management system), with a two-way translator between the XML and DBMS representations. SWEET also includes wrappers for large external online databases (gazetteers, earthquakes, etc.) to enable their contents be virtual components of the ontologies ( http://sweet.jpl.nasa.gov ).

ABSTRACT In the recent decade, knowledge graph has been a key technique under quick development in artificial intelligence. Due to its great potential for tackling big data and solving complex scientific questions in the geosciences, it has attracted the attention of both computer scientists and geoscientists. In this paper, we review concepts and technologies relevant to the knowledge graph, the workflow of geoscience knowledge graph construction, and state-of-the-art examples from several geoscience disciplines. There are two general strategies for constructing geoscience knowledge graphs: top-down and bottom-up. The detailed technologies include geoscience domain knowledge modeling, data collection, knowledge extraction, knowledge cleaning and fusion, knowledge storage, and knowledge service and discovery. A few recent studies have shown that knowledge graph is a useful tool for improving our understanding of the evolution of the Earth and can assist in data-intensive geoscience studies. At the end of the paper, we discuss the best practices from the studies reviewed and propose research topics for future work. Both knowledge and rules in existing human-curated databases and text mining from the literature should be leveraged in constructing geoscience knowledge graphs. Moreover, development of a higher level schema for existing ontology models and a comparable training corpus should be considered.

Digital libraries extend traditional library tools, collections, and metaphors in new and sometimes radical directions. Geoscience information, traditionally found in libraries in the form of maps, reports, books, and journal articles, can potentially gain enormous benefits from digital interfaces that allow geospatial information to be effectively searched and displayed in context. However, to many users of such a collection, the concepts within geoscience represent a barrier to use and understanding. The Georgia Basin Digital Library (GBDL) Project examines community-based decision support for sustainable development, using digital geoscience information as a fundamental component. The context and relevance of geoscientific and other information in the GBDL is explained and represented through the use of semistructured representations based on architectural pattern languages, where a web of connected concepts are each contextualized using storytelling with supporting graphics. The GBDL allows multiple coexisting pattern networks, so that multiple perspectives on an idea, a situation, or a region are possible and in fact preferable. The GBDL interface makes use of geographic information systems (GIS) components to show maps, such that the point of engagement for a user might alternatively be a story, a map, a photo, or a text heading. Linking the mapping component directly to the pattern framework allows for community-level contextualization of ideas and stories. For geoscientists, the ideas in the GBDL are relevant both as novel approaches to sharing geoscience information with the public and as approaches that can be applied to specialized scientific discourse in the future. As the Web evolves, geoscience information systems and communication must evolve along with it; the GBDL shows one such evolutionary pathway.

Abstract The ‘Big Data’ paradigm will revolutionize understanding of the natural environment. New technologies are revolutionizing our ability to measure, model, understand and make robust, evidence-based predictions at increasingly spatial and temporal resolutions. Realising this potential will require reengineering of environmental sciences in the observation infrastructure, in data management and processing, and in the culture of environmental sciences. Collectively these will deliver vibrant, integrated research communities. Manipulating such enormous data streams requires a new data infrastructure underpinned by four technologies. Pervasive environmental sensor networks will continuously measure suites of environmental parameters and transmit these wirelessly to scientists, regulators and modellers in real time. Integrated environmental modelling will process data, streamed from sensor networks, using components synthesizing natural systems developed by domain experts, each of which will be linked at runtime to other expert developed components. Semantic interoperability will facilitate cross-disciplinary working, as has already happened within the biosciences so that data items can be exchanged with unambiguous, shared meaning. Cloud computing will revolutionize data processing allowing scalable computing close to observations on an as-needed basis. Leveraging the full potential of these technologies requires a major culture change in the environmental sciences where national and continental scale observatories of sensors networks become basic scientific tools.

Abstract Geological maps can be seen as a type of model and can be implemented in digital systems as geological spatial databases. In this context, geological map fusion can be implemented at different levels: harmonization of the conceptual data model describing the map objects; the use of shared concepts to describe properties in the model to give semantic harmonization; and ensuring geometric consistency. GeoSciML has been developed as an interchange language for geosciences information, derived from a common conceptual data model, along with common vocabularies of concepts to populate the object properties. GeoSciML and the vocabularies were used in the OneGeology-Europe project where a 1:1 million scale geological map of Europe was delivered using disseminated web services from 20 different data providers. The lessons learnt from the OneGeology-Europe project informed the development of the INSPIRE Geology Data Specification. The INSPIRE data specification is used to define what information must be made available through web services under the INSPIRE legislation, so has to be kept simple. The INSPIRE data model can be extended with GeoSciML and will provide a basis for geological map fusion.

ABSTRACT Data science is receiving increased attention in a variety of geoscience disciplines and applications. Many successful data-driven geoscience discoveries have been reported recently, and the number of geoinformatics and data science sessions at many geoscience conferences has begun to increase. Across academia, industry, and government, there is strong interest in knowing more about current progress as well as the potential of data science for geoscience. To address that need, this paper provides a review from the perspective of a data life cycle. The key steps in the data life cycle include concept, collection, preprocessing, analysis, archive, distribution, discovery, and repurpose. Those subjects are intuitive and easy to follow even for geoscientists with very limited experience with cyberinfrastructure, statistics, and machine learning. The review includes two key parts. The first addresses the fundamental concepts and theoretical foundation of data science, and the second summarizes highlights and sharable experience from existing publications centered on each step in the data life cycle. At the end, a vision about the future trends of data science applications in geoscience is provided that includes discussion of open science, smart data, and the science of team science. We hope this review will be useful to data science practitioners in the geoscience community and will lead to more discussions on the best practices and future trends of data science for the geosciences.

Extracting knowledge from the rock record stored in databases is one of the primary goals of the information-oriented geoscientist. This activity requires well-designed organizational structures to facilitate queries, and ultimately cyber-aided geological research. Such structures need to encompass information about geologic objects and the processes that affect or produce the objects. Therefore, our goal is to create a prototype of a computer-based knowledge environment that specifically reflects the reasoning used by a geoscientist, with the recognition that his/her primary interest lies in understanding processes that affect the rock record. In order to start development of such capabilities, we have utilized an organization of attributes and their definitions to construct a database schema for field-based igneous rocks, and show that its conversion into a knowledge base requires the application of both object and process ontologies.

Abstract Integrated environmental modelling (IEM) is a recent phenomenon that offers the opportunity to solve complex environmental problems. Whilst it has made great strides in recent years, there are still challenges to be met before IEM is universally accepted and used. This paper describes the current state of IEM and sets out a roadmap for achieving its full potential. A multidisciplinary, multi-agency approach will be required, the main goals of which are to: (1) raise awareness and build confidence in IEM; (2) ensure availability and accessibility of IEM techniques, tools and standards; (3) establish a minimum set of standards; (4) build the IEM skills base; (5) establish an underpinning research and development (R&D) programme; (6) co-ordinate and promote collaboration; and (7) foster IEM use by government, industry and the public. Once these goals have been achieved, then IEM can be deployed to help resolve currently intractable environmental issues, and the IEM methodology can be transferred to other fields.