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SOCoP Body-of-Knowledge (BoK) Project

Of relevance here, the Geographic Information Science and Technology (GIS&T) Body of Knowledge (BoK) divides the GIScience discipline into knowledge areas and further divides it into training units to provide a guide for their ontology effort for GIScience. The University Consortium of GIScience (UCGIS) is currently working to enhance the SOCoP_BoK to be SOCoP_BoK2. Because the area of geospatial semantics pre-dates the original effort to create the SOCoP_BoK, as part of educational aspect of the NSF SOCoP INTEROP project,Gary Berg-Cross has sketched out where material on geospatial semantics should be included in the SOCoP_BoK, as follows.

Starting a Conversation on Improved Semantics and Ontology for the SOCoP_BoK and SOCoP_BoK2 Efforts as Part of the NSF SOCoP INTEROP Educational Component by Gary Berg-Cross

The GIS&T Body of Knowledge (BoK) is available to order as a book at http://www.ucgis.org/priorities/education/modelcurriculaproject.asp or also available at http://www.aag.org/bok/. The SOCoP_BoK was developed by the University Consortium of GIScience (UCGIS) and outlines knowledge areas relevant to Geographic Information Science (GIScience). The following is a first attempt to add the new area of geospatial semantics to the existing SOCoP_BoK.

The following are some very preliminary ideas noted on possible areas to add more semantics and ontology topics and or the skills noted. Refining the language of inserted material or the organization has not yet been done. The intent is for the following to be a useful start going forward and provide something that can be added to and edited.

Inserts are noted by the orange text color with some annotation (italics on the wiki).

  • CF1-1 Metaphysics and ontology

<Given its title this contains the most reference to ontology in listed skills and would have several ontological inserts. A few are noted here although some of this material may be placed elsewhere. But this is a generally "philosophical" area on geo-semantics. Much of the material we want to cover is practical and technical, such as some nuts and bolts for an area of Geocomputing represented by Semantic Technology / Semantic Web effort as well data modeling and Big Data areas. This material seems like a better fit in sections like DM and GC than CF.>

    • Define common theories on what is real, such as realism, idealism, relativism, and experiential realism (CF1-1-1)
    • Identify the ontological assumptions underlying the work of colleagues (CF1-1-3)
    • Identify the ontological frameworks underlying important work.
    • Identify Frank's Frame (Epistemological layering)- 5 Tiers / Perspectives
    • Compare and contrast the ability of different theories to explain various situations (CF1-1-5)
    • Compare and contrast the ability of different foundational ontologies to explain various geo-phenomena and situations
    • Identify the role of real world and axiomatic semantics in geo-ontologies
    • Identify several levels of ontology from top level to domain to application.
  • CF3-1 Title Space Definition Part of super-concept CF3 Domains of geographic information Sub-concepts none Example skills
    • Define the four basic dimensions or shapes used to describe spatial objects (i.e., points, lines, regions, volumes) (CF3-1-1)
    • Differentiate between absolute and relative descriptions of location (CF3-1-2)
    • Differentiate between common-sense, Cartesian metric, relational, relativistic, phenomenological, social constructivist, and other theories of the nature of space (CF3-1-3)
    • Discuss the contributions that different perspectives on the nature of space bring to an understanding of geographic phenomenon (CF3-1-4)
  • CF5-1 Title: Categories (strategy here is to related this material to ideas of Type and Class used in Knowledge Representation as well as the use of taxonomies as ontology backbones.

Skills

    • Explain the human tendency to simplify the world using categories (CF5-1-1)
    • Explain use of Is-a relations and taxonomies to express categories such as topographic topics.
    • Reconcile differing common-sense and official definitions of common geospatial categories of entities, attributes, space, and time (CF5-1-10)
    • How are common geospatial categories of entities, attributes, space, and time represented in OWL?
    • Identify specific examples of categories of entities (i.e., common nouns), properties (i.e., adjectives), space (i.e., regions), and time (i.e., eras) (CF5-1-2)
    • Give examples of how these categories of entities (i.e., common nouns), properties (i.e., adjectives), space (i.e., regions), and time (i.e., eras) are represented in ontologies and used as part of the Semantic Web and related work.
    • Explain the role of categories in common-sense conceptual models, everyday language, and analytical procedures (CF5-1-3)
    • Recognize the essential role of conceptual analysis is designing quality ontologies.
    • Recognize and manage the potential problems associated with the use of categories (e.g., the ecological fallacy) (CF5-1-4)
    • Recognize the limitations of semantic models of geo-reality and its representation.
    • Recognize the role of semiotics in understanding semantic models
    • Construct taxonomies and dictionaries (also known as formal ontologies) to communicate systems of categories (CF5-1-5)
    • Compare and contrast the use of geo-taxonomies as ontology backbones
    • Explain steps used to develop assemble ontologies from vocabularies and taxonomies
    • Understand the various formal languages and their degree of formality that exist for many within GIScience
    • Describe the contributions of category theory to understanding the internal structure of categories (CF5-1-6)
    • Recognize the synergy between category theory and ontologies.
    • Document the personal, social, and or institutional meaning of categories used in GIS applications (CF5-1-7)
    • Compare and contrast ontologies of personal, social, and/or institutional concepts relate to GIScience
    • Create or use GIS data structures to represent categories, including attribute columns, layers themes, shapes, legends, etc. (CF5-1-8)
    • Map simple GIS data structures to represent categories, including attribute columns, layers themes, shapes, legends to one or more ontologies with improved semantics
    • Use categorical information in analysis, cartography, and other GIS processes, avoiding common interpretation mistakes (CF5-1-9)
    • Use an ontology to support analysis, cartography, and other GIS processes, and avoid common interpretation errors
  • CF5-4 Title Topological relationships
    • Define various terms used to describe topological relationships, such as disjoint, overlap, within, and intersect (CF5-4-1)
    • Understand how these have been formalized in Foundation Ontologies
    • Describe geographic phenomena in terms of their topological relationships (in space and time to other phenomena (CF5-4-2)
    • Use an ontology to formalize geographic phenomena in terms of their topological relationships (in space and time to other phenomena
    • List the possible topological relationships between entities in space (e.g., 9-intersection) and time (CF5-4-3)
    • Use methods that analyze topological relationships (CF5-4-4)
    • Describe an ontology of a spatial object contains geometry, and attributive data as well as topology
    • Recognize the contributions of Topology (the branch of mathematics) to the study of geographic relationships (CF5-4-5)
    • Recognize the contributions of Mereotopology to the field of GIScience (also in CF5 section below)
  • CF5-2 Mereology: structural relationships
    • Understand the ontological and cognitive aspects of the part-whole relations
    • Discuss how mereology the formal theory of parts and wholes, as it is used for ontology construction
    • Discuss how mereotopology is used for spatial relations such connected objects that meet or overlap spatially
    • Describe how various mereological partonomies (proper, permanent, temporary etc,) that provide a partial ordering over some domain of objects related by parthood
    • Understand that regions of some dimensionality are structured as parts of heterogeneous and homogeneous wholes.
  • AM Analytical Methods
  • AM2-2 Structured Query Language (SQL) and attribute queries

We would have a smaller, but comparable section on SPARQL and GeoSPARQL. Below are some possible skill areas:

    • Recognize key GeoSPARQL Terms
    • Discuss how to link an ontology to GeoSPARQL
    • Understand how to convert W3C Geo Data
    • Discuss how to query RDF(s) Data
    • Describe the role of geometries in GeoSPARQL
    • Describe the role of Topological Relationships in GeoSPARQL
  • AM10 Data mining Definition Algorithms have been developed to scan and search through extremely large data sets in order to find patterns within the data. These data mining and knowledge discovery techniques have been expanded to the spatial case. Legal and ethical concerns associated with such practices are considered in Knowledge Areas GS GIS and T and Society and OI Organizational and Institutional Aspects. Part of super-concept <Since semantics may help search and analytics we should have some items in here.>
  • AM Analytical Methods Sub-concepts
    • AM10-1 Problems of large spatial databases
    • Describe emerging geographical analysis techniques in geocomputation derived from artificial intelligence e.g., expert systems, artificial neural networks, genetic algorithms, and software agents (AM10-1-1)
    • Describe emerging geographical analysis techniques used to support the Semantic Web as a sub-field of geocomputation
    • Describe difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity (AM10-1-2)
    • AM10-3 Knowledge discovery
    • Describe how improved semantics can help with knowledge discovery and data mining
  • Geocomputation GC
  • GC2 Computational aspects, neurocomputing & Semantic Technology
  • GC2-2 Title Computational intelligence Definition Part of super-concept GC2 Computational aspects and neurocomputing

Example skills

    • Describe computational intelligence methods that may apply to GIS and T (GC2-2-1)
    • Describe a hypothesis space that includes searches for optimality of solutions within that space (GC2-2-2)
    • Exemplify the potential for machine learning to expand performance of specialized geospatial analysis functions (GC2-2-3)
    • Identify artificial intelligence tools that may be useful for GIS and T
    • Identify semantic algorithms for data fusion and situation awareness
  • New Sub-Section for GeoComputation: Semantic Technologies and Use of OWL (not its representation)
    • Use RDF schemas/ontologies to describe data structures such as used for topographic & GIS data
    • Use RDF schemas/ontologies to enabling mappings to be effected between data sets
    • Use of Semantic Technology and Ontologies for sensor networks
    • Recognize the use of ontologies as web-based resources
    • Recognize that represented knowledge in OWL can support the processing of queries using spatial concepts.
    • Discuss the use of OWL ontologies to bridge the semantic gap that exists between domain and database, and also to bridge between geo-domains.
    • Semantic web services architectures for Geocomputation (sensor networks etc.)
    • Semantic data integration in large-scale heterogeneous geo-networks (Big data)
    • Many other items possible such as the use of modules and ODPs
  • GD Geospatial Data
  • GD12 Metadata, standards
  • GD12-2-1 Definition Differentiate between a controlled vocabulary, a taxonomy, and various levels that distinguish formal ontology.
    • Discuss how enhanced semantics improves metadata.
  • GD12-2 Content standards
  • GD12-2-2 Definition Describe a domain ontology or vocabulary - i.e., land use classification systems, surveyor codes, data dictionaries, place names, or benthic habitat classification system
    • Contrast and compare a domain ontology or vocabulary for topics such as land use classification systems, surveyor codes, data dictionaries, place names, or benthic habitat classification system
  • GD12-5-7 Definition Describe the characteristics of the Web Ontology Language (OWL) Demonstrates GD12-5 Transport protocols
    • 1. Discuss the value of OWL semantics for transitivity, inverse, irreflexive, symmetry properties etc.
    • 2. Discuss typical elements in a quality ontology
    • 3. Compare and contrast top-level/foundational ontologies
    • 4. Describe the characteristics of a Description Logic
    • 5. Discuss representing classes and properties in the OWL language
    • 6. Explain the role of the Turtle language in simplifying representation
    • 7. Compare and contrast the limitations of OWL with OWL2
  • DM Data Modeling (add KR and much of OWL as a Representation here)
    • 1. Recognize how an ontology is crafted (ontological engineering) from conceptualizations
    • 2. Understand how a good semantic model differs from an ERD
    • 3. Understanding the semantic spectrum
    • 4. Describe how popular forms of knowledge representation differ from typical data representation
    • 5. Discuss how geospatial data are represented as RDF
    • 6. Recognize why RDF is a syntactic graph formalism
    • 7. Recognize the use of unique names: URIs & IRIs
    • 8. Discuss how semantics are available as part of the RDFS vocabulary
    • 9. Recognize what Geo Linked Data is
    • 10. Discuss the role of semantics in Linked Open Data
  • GIS&T and Society GS
  • GS7-1 Title Epistemological critiques Definition Part of super-concept GS7 Critical GIS Sub-concepts none Example skills
    • Discuss the implications of interoperability on ontology
    • Recognize that interoperability can be assigned to three distinct layers: technical, semantic, and institutional.
  • GS
  • GS7-1-5 Definition
    • Discuss the implications of interoperability in leveraging requirements on ontology
    • Demonstrates GS7-1 Epistemological critiques
    • Discuss how improved semantics may allow better interoperability.



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