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Dalia E. Varanka

Publications and source records attributed to Dalia E. Varanka.

At least 19 recordsLinked to original sources
USGS2025

Grammar to graph—An approach for semantic transformation of annotations to triples

Data annotation is the process of labeling data to show the outcome that a related data model should predict. In this study, annotation data were transformed into semantic graph triples, mainly for use with the Resource Description Framework (RDF), a type of entity-relationship-attribute data model for graph databases. The transformation of annotation data to semantic graph triples provides complex linguistic meaning with data handling advantages such as reduced data storage needs, improved logical specification of relations between objects, and reusable classes and properties that support logic and inference. A grammar-based framework in graph form supports user questions and queries. The words defining approximately 334 topographic feature types compiled by the U.S. Geological Survey were tokenized as units of analysis and grouped by part of speech. Their dependency relations were identified for this study using natural language processing libraries. Dependency concepts are used as structured semantic relations among part-of-speech classes. Tokens, units equivalent to words, form instances of classes and were quantified within a tabular output format using PostgreSQL data storage software. Table data were logically aligned as triples following a mapping file and stored with an ontology file using Ontop virtual triplestore software. A grammar ontology schema for the data was synchronized to match queries whose results validated the graph’s structure. The text analysis produced 8 part-of-speech classes of content words for object representations and 4 classes of function words for operational applications. Dependency relations formed 27 ontology properties for topographic subgraph structures. Token occurrences shaped overall ontology salience and formed a lexicon of syntactic terms for subgraph objects and properties. The schema ontology of class and property population shapes formed the lexicon of English terms. SPARQL Protocol and RDF Query Language (SPARQL) was used with the lexicon to conform data to RDF guidelines. This study confirms the hypothesis that although linguistic logic varies from description logic, its approximation applies to ontology design. Property and query use case patterns extracted from the analysis support queries concerning complex topographic relations and patterns normally embedded within text definitions. The method used in this study could be applied to text forms in other domains, such as survey notes.

Scientific Investigations Report↗
USGS2024

Grammar To Graph, an approach for semantic transformation of annotations to triples

Linguistic representation of geographic knowledge is semantically complex and particularly challenging when employing geographic information technology to automate interpreted analysis dealing with unstructured knowledge. This study describes an approach called GrammarToGraph (G2G) that applies dependency grammar rules through natural language processing to transform annotation data into structured geospatial semantic graph triples. This approach offers data handling advantages that include reducing string annotation storage needs, improving the logical specification of relations between objects, and providing reusable classes and properties that support graph queries and logic inference.

Conference Paper↗
USGS2022

A geospatial knowledge graph prototype for national topographic mapping

Knowledge graphs are a form of database representation and handling that show the potential to better meet the challenges of data interoperability, semi-automated information reasoning, and information retrieval. Geospatial knowledge graphs (GKG) have at their core specialized forms of applied ontology that provide coherent spatial context to a domain of information including non-spatial attributes. This paper discusses research toward the development of a prototype GKG based on national topographic databases of geospatial feature instances, attributes, properties, metadata, and annotations. The challenges are to capture and represent geographic semantics inherent in the source data, to align such graph models with standards where possible, to test logical computations, and to visualize the data using a cartographic user interface. Data integration from outside sources was tested through SPARQL and GeoSPARQL queries. Called the MapKB, the approaches applied in this prototype use a number of software components to build a system architecture aligned with those objectives and are composed entirely of free and open-source software. The system and ontology design were validated through reasoning and competency questions. Technical aspects of the prototype software succeeded, but customization was found to be needed for user-based design.

International Archives of the Photogrammetry, Remo↗
USGS2022

Open knowledge network roadmap: Powering the next data revolution

Open access to shared information is essential for the development and evolution of artificial intelligence (AI) and AI-powered solutions needed to address the complex challenges facing the nation and the world. The Open Knowledge Network (OKN), an interconnected network of knowledge graphs, would provide an essential public-data infrastructure for enabling an AI-driven future. It would facilitate the integration of diverse data needed to develop solutions to drive continued strong economic growth, expand opportunities, and address complex problems from climate change to social equity. The OKN Roadmap describes the key characteristics of the OKN and essential considerations in taking the effort forward in an effective and sustainable manner.

Report↗
USGS2021

GeoAI in the US Geological Survey for topographic mapping

Geospatial artificial intelligence (GeoAI) can be defined broadly as the application of artificial intelligence methods and techniques to geospatial data, processes, models, and applications. The application of these methods to topographic data and phenomena is a focus of research in the US Geological Survey (USGS). Specifically, the USGS has researched and developed applications in terrain feature extraction, hydrographic network extraction, and semantic modeling. This article is a documentation of the recent work and current state of research and development. The article helps define the accomplishments and directions of research and applications in fields of GeoAI for topographic mapping within the USGS and more broadly.

Transactions in GIS↗
USGS2021

GIS object data properties

Data properties are characteristics of GIS attribute systems and values whose design and format impacts analytical and computational processing. Geospatial data are expressed at conceptual, logical, and physical levels of database abstraction intended to represent geographical information. The appropriate design of attribute systems and selection of properties should be logically consistent and support appropriate scales of measurement for representation and analysis. Geospatial concepts such as object-field views and dimensional space for relating objects and qualities form data models based on a geographic matrix and feature geometry. Three GIS approaches and their attribute system design are described: tessellations, vectors, and graphs.

Book chapter↗
USGS2020

Creating annotations for web ontology language ontology generated from relational databases

Many approaches that have been proposed that allow users to create a Web Ontology Language (OWL) ontology from a relational database fail to include metadata that are inherent to the database tables. Without metadata, the resulting ontology lacks annotation properties. These properties are key when performing ontology alignment. This paper proposes a method to include relevant metadata through annotation properties to OWL ontologies, which furthers the ability to integrate and use data from multiple unique ontologies. The described method is applied to geospatial data collected from The National Map, a data source hosted by the U. S. Geological Survey. Following that method, an ontology was manually created that used the metadata from The National Map. Because a manual approach is prone to human error, an automated approach to storing and converting metadata into annotation properties is discussed.

Conference Paper↗
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↗
USGS2020

A system design for implementing advanced feature descriptions for a map knowledge base

A prototype system to explore Linked Data that semantically integrates geospatial data in various formats from different publication sources with data from The National Map of the U.S. Geological Survey is presented. The focus is on accessing advanced feature descriptions for data from The National Map with data coreferenced from other sources. The prototype uses Geoserver to access The National Map data, which are converted to Resource Description Framework triples using Karma and stored in the Marmotta triplestore. Marmotta uses a Postgres relational database as a backend for the project and queries to the Marmotta triplestore are converted to structured query language and executed by Postgres. Triples retrieved are linked with same_as relationships to external data sources. The links to these sources provide additional attributes and relationships of the data from The National Map. Visualization of the results is provided using Leaflet and workflows for all parts of the system are defined. A use case for the system is provided to access structures and names information from The National Map for the Washington, D.C., area and link these to Geonames data, with visualization of the graphical and tabular results.

Scientific Investigations Report↗
USGS2019

Alignment of surface water ontologies: A comparison of manual and automated approaches

More data are being collected about the world around us than ever before, but effectively using this information requires different data stores to be integrated in such a way that they can be seamlessly queried and analyzed. Automated alignment algorithms exist to facilitate this data integration challenge. In this paper we examine the utility of two current leading automated alignment systems to integrate four ontologies from the surface water domain. We show that the performance of such systems in this domain lags behind their results on popular benchmarks, and therefore incorporate the alignment task described here into the set of benchmarks used by the alignment community. In addition, we show that, with minor modifications, existing alignment algorithms can be used effectively within a semi-automated alignment system for the surface water domain.

Journal of Geographical Systems↗
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↗
USGS2019

Semantically supported linked data mapping

Semantic technology based on the Resource Description Framework (RDF) modeling environment has introduced new data management capabilities that can lead to innovative cartographic techniques. This report describes research toward more semantically expressive linked geospatial data mapping, topics of research, and an avenue for further international collaboration.

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↗
USGS2018

Book review: Mapping gendered routes and spaces in the early modern world

This book encapsulates and extends many seminal ideas presented at the eighth “Attending to Early Modern Women” conference held at the University of Wisconsin–Milwaukee in June 2012. Merry Wiesner-Hanks has done a masterful job editing these papers within a central theme of the interaction of spatial domains with gender-based phenomena. The fifteen chapters of this book are organized into four sections: “Framework,” discussing theoretical concepts; “Embodied Environments,” focusing on physicality; “Communities and Networks” of social patterns; and “Exchanges” across geographic space. Together, a global society shaped by gender and sexuality and intersected by race and class emerges.

Renaissance Quarterly↗
USGS2018

GNIS-LD: Serving and visualizing the Geographic Names Information System Gazetteer as linked data

In this dataset description paper we introduce the GNIS-LD, an authoritative and public domain Linked Dataset derived from the Geographic Names Information System (GNIS) which was developed by the U.S. Geological Survey (USGS) and the U.S. Board on Geographic Names. GNIS provides data about current, as well as historical, physical, and cultural geographic features in the United States. We describe the dataset, introduce an ontology for geographic feature types, and demonstrate the utility of recent linked geographic data contributions made in conjunction with the development of this resource. Co-reference resolution links to GeoNames.org and DBpedia are provided in the form of owl:sameAs relations. Finally, we point out how the adapted workflow is foundational for complex Digital Line Graph (DLG) data from the USGS National Map and how the GNIS-LD data can be integrated with DLG and other data sources such as sensor observations.

Conference Paper↗
USGS2018

The map as knowledge base

This paper examines the concept and implementation of a map as a knowledge base. A map as a knowledge base means that the visual map is not only the descriptive compilation of data and design principles, but also involves a compilation of semantic propositions and logical predicates that create a body of knowledge organized as a map. The digital product of a map as knowledge base can be interpreted by machines, as well as humans, and can provide access to the knowledge base through interfaces to select features and other information from the map. The design of maps as a knowledge base involves technical approaches and a system architecture to support a knowledge base. This paper clarifies how a map as a knowledge base differs from earlier map theory models by investigating the knowledge-based concepts of implementation through logical modelling, a knowledge repository, user interfaces for information access, and cartographic visualization. The paper ends with proof of concepts for two types of cartographic data query.

International Journal of Cartography↗
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↗