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14 recordsLinked to original sources
USGS2015

A case study of data integration for aquatic resources using semantic web technologies

Use cases, information modeling, and linked data techniques are Semantic Web technologies used to develop a prototype system that integrates scientific observations from four independent USGS and cooperator data systems. The techniques were tested with a use case goal of creating a data set for use in exploring potential relationships among freshwater fish populations and environmental factors. The resulting prototype extracts data from the BioData Retrieval System, the Multistate Aquatic Resource Information System, the National Geochemical Survey, and the National Hydrography Dataset. A prototype user interface allows a scientist to select observations from these data systems and combine them into a single data set in RDF format that includes explicitly defined relationships and data definitions. The project was funded by the USGS Community for Data Integration and undertaken by the Community for Data Integration Semantic Web Working Group in order to demonstrate use of Semantic Web technologies by scientists. This allows scientists to simultaneously explore data that are available in multiple, disparate systems beyond those they traditionally have used.

Open-File Report↗
USGS2022

The global environmental agenda urgently needs a semantic web of knowledge

Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure—i.e., public data and model repositories—is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making.

Environmental Evidence↗
USGS2011

A program for the conversion of The National Map data from proprietary format to resource description framework (RDF)

To expand data functionality and capabilities for users of The National Map of the U.S. Geological Survey, data sets for six watersheds and three urban areas were converted from the Best Practices vector data model formats to Semantic Web data formats. This report describes and documents the conver-sion process. The report begins with an introduction to basic Semantic Web standards and the background of The National Map. Data were converted from a proprietary format to Geog-raphy Markup Language to capture the geometric footprint of topographic data features. Configuration files were designed to eliminate redundancy and make the conversion more efficient. A SPARQL endpoint was established for data validation and queries. The report concludes by describing the results of the conversion.

Open-File Report↗
USGS2012

Design and development of linked data from the National Map

The development of linked data on the World-Wide Web provides the opportunity for the U.S. Geological Survey (USGS) to supply its extensive volumes of geospatial data, information, and knowledge in a machine interpretable form and reach users and applications that heretofore have been unavailable. To pilot a process to take advantage of this opportunity, the USGS is developing an ontology for The National Map and converting selected data from nine research test areas to a Semantic Web format to support machine processing and linked data access. In a case study, the USGS has developed initial methods for legacy vector and raster formatted geometry, attributes, and spatial relationships to be accessed in a linked data environment maintaining the capability to generate graphic or image output from semantic queries. The description of an initial USGS approach to developing ontology, linked data, and initial query capability from The National Map databases is presented.

Semantic Web↗
USGS2018

A linked GeoData map for enabling information access

Overview The Geospatial Semantic Web (GSW) is an emerging technology that uses the Internet for more effective knowledge engineering and information extraction. Among the aims of the GSW are to structure the semantic specifications of data to reduce ambiguity and to link those data more efficiently. The data are stored as triples, the basic data unit in graph databases, which are similar to the vector data model of geographic information systems (GIS); that is, a node-edge-node model that forms a graph of semantically related information. The GSW is supported by emerging technologies such as linked geospatial data, described below, that enable it to store and manage geographical data that require new cartographic methods for visualization. This report describes a map that can interact with linked geospatial data using a simulation of a data query approach called the browsable graph to find information that is semantically related to a subject of interest, visualized using the Data Driven Documents (D3) library. Such a semantically enabled map functions as a map knowledge base (MKB) (Varanka and Usery, 2017). A MKB differs from a database in an important way. The central element of a triple, alternatively called the edge or property, is composed of a logic formalization that structures the relation between the first and third parts, the nodes or objects. Node-edge-node represents the graphic form of the triple, and the subject-property-object terms represent the data structure. Object classes connect to build a federated graph, similar to a network in visual form. Because the triple property is a logical statement (a predicate), the data graph represents logical propositions or assertions accepted to be true about the subject matter. These logical formalizations can be manipulated to calculate new triples, representing inferred logical assertions, from the existing data. To demonstrate a MKB system, a technical proof-of-concept is developed that uses geographically attributed Resource Description Framework (RDF) serializations of linked data for mapping. The proof-of-concept focuses on accessing triple data from visual elements of a geographic map as the interface to the MKB. The map interface is embedded with other essential functions such as SPARQL Protocol and RDF Query Language (SPARQL) data query endpoint services and reasoning capabilities of Apache Marmotta (Apache Software Foundation, 2017). An RDF database of the Geographic Names Information System (GNIS), which contains official names of domestic feature in the United States, was linked to a county data layer from The National Map of the U.S. Geological Survey. The county data are part of a broader Government Units theme offered to the public as Esri shapefiles. The shapefile used to draw the map itself was converted to a geographic-oriented JavaScript Object Notation (JSON) (GeoJSON) format and linked through various properties with a linked geodata version of the GNIS database called “GNIS–LD” (Butler and others, 2016; B. Regalia and others, University of California-Santa Barbara, written commun., 2017). The GNIS–LD files originated in Terse RDF Triple Language (Turtle) format but were converted to a JSON format specialized in linked data, “JSON–LD” (Beckett and Berners-Lee, 2011; Sorny and others, 2014). The GNIS–LD database is composed of roughly three predominant triple data graphs: Features, Names, and History. The graphs include a set of namespace prefixes used by each of the attributes. Predefining the prefixes made the conversion to the JSON–LD format simple to complete because Turtle and JSON–LD are variant specifications of the basic RDF concept. To convert a shapefile into GeoJSON format to capture the geospatial coordinate geometry objects, an online converter, Mapshaper, was used (Bloch, 2013). To convert the Turtle files, a custom converter written in Java reconstructs the files by parsing each grouping of attributes belonging to one subject and pasting the data into a new file that follows the syntax of JSON–LD. Additionally, the Features file contained its own set of geometries, which was exported into a separate JSON–LD file along with its elevation value to form a fourth file, named “features-geo.json.” Extracted data from external files can be represented in HyperText Markup Language (HTML) path objects. The goal was to import multiple JSON–LD files using this approach.

Open-File Report↗
USGS2011

Topographic mapping data semantics through data conversion and enhancement

This paper presents research on the semantics of topographic data for triples and ontologies to blend the capabilities of the Semantic Web and The National Map of the U.S. Geological Survey. Automated conversion of relational topographic data of several geographic sample areas to the triple data model standard resulted in relatively poor semantic associations. Further research employed vocabularies of feature type and spatial relation terms. A user interface was designed to model the capture of non-standard terms relevant to public users and to map those terms to existing data models of The National Map through the use of ontology. Server access for the study area triple stores was made publicly available, illustrating how the development of linked data may transform institutional policies to open government data resources to the public. This paper presents these data conversion and research techniques that were tested as open linked data concepts leveraged through a user-centered interface and open USGS server access to the public.

Book chapter↗
USGS2014

An ontology design pattern for surface water features

Surface water is a primary concept of human experience but concepts are captured in cultures and languages in many different ways. Still, many commonalities exist due to the physical basis of many of the properties and categories. An abstract ontology of surface water features based only on those physical properties of landscape features has the best potential for serving as a foundational domain ontology for other more context-dependent ontologies. The Surface Water ontology design pattern was developed both for domain knowledge distillation and to serve as a conceptual building-block for more complex or specialized surface water ontologies. A fundamental distinction is made in this ontology between landscape features that act as containers (e.g., stream channels, basins) and the bodies of water (e.g., rivers, lakes) that occupy those containers. Concave (container) landforms semantics are specified in a Dry module and the semantics of contained bodies of water in a Wet module. The pattern is implemented in OWL, but Description Logic axioms and a detailed explanation is provided in this paper. The OWL ontology will be an important contribution to Semantic Web vocabulary for annotating surface water feature datasets. Also provided is a discussion of why there is a need to complement the pattern with other ontologies, especially the previously developed Surface Network pattern. Finally, the practical value of the pattern in semantic querying of surface water datasets is illustrated through an annotated geospatial dataset and sample queries using the classes of the Surface Water pattern.

Conference Paper↗
USGS2019

Topographic mapping evolution: From field and photogrammetric data collection to GIS production and Linked Open Data

Whither the topographic map? Topographic mapping historically has been approached as a map factory operation through the period 1879-1990. During this time, data were field and photogrammetrically collected; cartographically verified and annotated creating a compilation manuscript; further edited, generalized, symbolized, and produced as a graphic output product using lithography, or more recently, through digital means. Adoption of geographic information systems (GIS) as the primary production process for topographic maps, including digital database preparation (1975-2000) and product generation operations (2001-present), has led to faster and more standardized production in a semi-automated process. However, the topographic product has remained the same static graphic. Global Navigation Systems (GNS) began in the post 1990s, led to publicly and commercially produced location-based information traditionally provided by surveyors for topographic maps. Advances in GIS technology, computer processing, memory, and storage devices, along with GNS spawned new location systems and led to ubiquitous, consumer-based cartography through commercial entities on the World Wide Web (Web). This global availability of cartography has provided consumer access and the ability to produce topographic types of map products previously supplied only by traditional National Mapping Agencies (NMAs). Information provided by location-based services made available through connected databases has led to completely new business models based on cartography and geospatial data. A new form of topographic map as an interactive, linked knowledge base is now being created. The appearance of the Semantic Web and Linked Open Data allows the map to become an interactive knowledge base. In this current theory and implementation of topographic mapping, the map is a graphics-based interface to a triplestore knowledge base which includes a topographic feature ontology, semantics and relations, and instance data with geometry and topology available. The topographic map graphic becomes an interactive link to the knowledge base and additional linked data through the Linked Open Data cloud.

Cartographic Journal↗
USGS2020

Improving geospatial query performance of an interoperable geographic situation-awareness system (IGSAS) for disaster response

Disaster response operations require fast and coordinated actions based on the real-time disaster situation information. Although Volunteered Geographic Information (VGI) or crowdsourced geospatial data applications have demonstrated to be valuable tools for gathering real-time disaster situation information, they only provide limited utility for disaster response coordination because of the lack of compatibility and interoperability. VGI based on Geospatial Semantic Web (GSW) technologies has the potential to overcome the incompatibility and heterogeneity problems. However, GSW-based VGI often has poor performance due to complex geometric computation. The objective of this research is to explore how to use optimization techniques to improve performance of an interoperable geographic situation-awareness system (IGSAS) based on GSW technologies for disaster response. We conducted experiments to evaluate various client-side optimization techniques for improving performance of an IGSAS prototype for flooding disaster response in New Haven, Connecticut. Our experimental results show that the developed prototype can greatly reduce the runtime costs of geospatial semantic queries through on-the-fly spatial indexing, tile-based rendering, efficient algorithms for spatial join, and caching, especially for those spatial-join geospatial queries that involve a large number of spatial features and heavy geometric computation.

Transactions in GIS↗
USGS2011

An analysis of spatial relation predicates in U.S. Geological Survey feature definitions

The Semantic Web uses a data model called a triple, which consists of a subject -predicate - object structure. When represented as triples, geospatial data require a spatial relation term to serve as the predicate linking two spatial features. This document summarizes the approaches and procedures used during the identification of spatial relationships common between topographic features using terms from topographic data standards. This project identified verb-predicate arguments that could be used in the creation of data triples and ontologies for The National Map of the U.S. Geological Survey and also investigated the possibility of deriving ontology from predefined textual definitions. The primary purpose of this report is to present the data used for subsequent analysis. A summary of terms organized by basic categories is provided. Keywords: semantic technology, geospatial relations, topographic data

Open-File Report↗
USGS2016

Community for Data Integration 2015 annual report

The Community for Data Integration (CDI) continued to experience success in fiscal year 2015. The CDI community members have been sharing, learning, and collaborating through monthly forums, workshops, working groups, and funded projects. In fiscal year 2015, CDI coordinated 10 monthly forums with 16 different speakers from the U.S. Geological Survey and external partners; funded 11 collaborative projects; and hosted an in-person, four-day workshop, which attracted 168 (134 in-person and 34 remote) data practitioners, data providers, and data consumers from across the USGS, academia, industry, and other government agencies. The Citizen Science, Connected Devices, Data Management, Semantic Web, and Tech Stack Working Groups continued to accomplish great things in fiscal year 2015. These working groups were major stakeholders in planning the 2015 CDI Workshop; they continued developing solutions to pressing challenges, and they brought in speakers throughout the year for more focused presentations and discussions. Additionally, a new working group formed during the 2015 CDI Workshop—the Earth-Science Themes Working Group.

Open-File Report↗
USGS2015

Supporting open collaboration in science through explicit and linked semantic description of processes

The Web was originally developed to support collaboration in science. Although scientists benefit from many forms of collaboration on the Web (e.g., blogs, wikis, forums, code sharing, etc.), most collaborative projects are coordinated over email, phone calls, and in-person meetings. Our goal is to develop a collaborative infrastructure for scientists to work on complex science questions that require multi-disciplinary contributions to gather and analyze data, that cannot occur without significant coordination to synthesize findings, and that grow organically to accommodate new contributors as needed as the work evolves over time. Our approach is to develop an organic data science framework based on a task-centered organization of the collaboration, includes principles from social sciences for successful on-line communities, and exposes an open science process. Our approach is implemented as an extension of a semantic wiki platform, and captures formal representations of task decomposition structures, relations between tasks and users, and other properties of tasks, data, and other relevant science objects. All these entities are captured through the semantic wiki user interface, represented as semantic web objects, and exported as linked data.

Conference Paper↗
USGS2012

Introduction to geospatial semantics and technology workshop handbook

The workshop is a tutorial on introductory geospatial semantics with hands-on exercises using standard Web browsers. The workshop is divided into two sections, general semantics on the Web and specific examples of geospatial semantics using data from The National Map of the U.S. Geological Survey and the Open Ontology Repository. The general semantics section includes information and access to publicly available semantic archives. The specific session includes information on geospatial semantics with access to semantically enhanced data for hydrography, transportation, boundaries, and names. The Open Ontology Repository offers open-source ontologies for public use.

Open-File Report↗
USGS2019

Integrating the sociology of space with geospatial semantics relation properties for data graphs

This research posits that socially constructed spatial relations address concepts of interactions instead of intersections, human/tool agents instead of physical processes, and broader ranges of geographical outcomes. The hypothesis is that social space can be represented by using patterns of logic relations between sets of entities. The data corpus of spatial relations was extracted from geographic term definitions. The relations were further analyzed as primitives using Case Grammar Matrix models. These findings are being related to Web Ontology Language (OWL) properties. This approach allows an extensive range of natural language terms to instantiate ontology sub-types, while supporting inferences to study their logical implications.

Conference Paper↗