Design Considerations for Spatial Decision Support Ontologies Karen Kemp, The Kohala Center Robert Raskin, Jet Propulsion Laboratory Naicong Li, University of Redlands AAG 2009, Las Vegas, Nevada March 22 – 27, 2009

Design considerations for SDS ontologies About the project: Part of the Spatial Decision Support Knowledge Portal project Initiated by the Redlands Institute, University of Redlands (fall 2007) Collaborative workshops on SDS ontologies held at The Redlands Institute (Feb 2008, May 2008) Establishment of the SDS Consortium (May 2008) Portal development at The Redlands Institute Several consortium internal release so far Plan to open to public in April 2009

Design considerations for SDS ontologies Outline The need for SDS ontologies Intended users Scope and coverage Development process Choice of classification scheme Degree of formalization Treatment of natural language Modular design and Potential interface with other ontologies Advantages of ontology-based Knowledge Portal Conclusion and future work

Design considerations for SDS ontologies Definition of Spatial Decision Support (SDS) Spatial decision support is the computational or informational assistance for making better informed decisions about problems with a geographic or spatial component. This support assists with the development, evaluation and selection of proper policies, plans, scenarios, projects, interventions, or solution strategies. SDS Consortium, based on

Design considerations for SDS ontologies Intended users People facing spatial decision problems - decision makers, practitioners, etc. University researchers and students Usage: Find resources for spatial decision support Learn about spatial decision support

Design considerations for SDS ontologies The need for SDS ontologies To formally represent the body of knowledge in the field of SDS To organize the Information about SDS resources To promote semantic clarity of commonly used terms within a user community, in the area of spatial decision making

Design considerations for SDS ontologies The need for SDS ontologies To facilitate the development of modular, re-usable, interoperable tools/services To facilitate the evaluation of existing spatial decision support systems and tools in terms of their functionality and interoperability  The need for SDS ontologies

Design considerations for SDS ontologies Scope and coverage The body of knowledge is vast Focus: Decision problem types Decision context Decision process (phases and steps) Methods and techniques SDSS functionalities Sets of properties describing SDS resources: Tools and models Data sources, data models Decision process workflow templates Case studies Literature

Design considerations for SDS ontologies Development process Literature review for a set of essential concepts and definitions Review of related web sites for user needs Collaboration among domain experts (SDS Consortium members) on critical issues: What to include Overall structure of ontology set (partition of concepts) Discussion/debate/agreement on definitions of critical concepts (two workshops; online discussions) Review of the ontology implementation Iterative process of addition/review

Design considerations for SDS ontologies Modular design 8 major components 35 ontologies

Design considerations for SDS ontologies

Towards Developing an SDS Solution Portal

Design considerations for SDS ontologies Choice and inclusion of classification schemes E.g. Classification of methods and technique Based on their functions serving spatial decision process steps Based on whether they are for multi-objective or multi-attribute decision making, whether they can handle uncertainty, etc. E.g. Classification of SDS systems and tools Based on their software type (system vs. tool) Based on their the decision process steps they server Based on their modeling application area (e.g. habitat suitability modeling)

Design considerations for SDS ontologies Choice and inclusion of classification schemes  The SDS ontologies can accommodate multiple classification schemes for the same group of concepts.  When a choice needs to be made, often it is driven by the need of the intended user community.

Design considerations for SDS ontologies Degree of formalization Concepts in SDS ontologies are defined by Natural language description Set of formally defined attributes e.g. analysis-unit attribute of an SDS tool Set of formally defined relations to other concepts Parent-child/sibling relations with other concepts Other associative relations among concepts e.g. method A is “implemented by” tools X and Y

Design considerations for SDS ontologies A method used for this step – simple additive weighting Simple additive weighting method Abbreviation: SAW Synonyms: weighted summation; boolean overlay; weighted linear combination method; WLC; scoring method Definition: SAW is a multiattribute procedure based on the concept of a weighted average; it calculates a total score for each alternative by multiplying the importance weight assigned to each attribute by the scaled attribute value and summing the products over all attributes; the alternative with the highest overall score is best; the procedure can beperformed using GIS supporting overlay operations. Source of description: Malczewski 1999, p.199 Decision type Deterministic decision problem Individual decision making Multiattribute decision making Decision Maker Interaction Level Moderate Editor... Attribute score Criterion weight Ordinal ranking is a kind of Multiattribute decision rule used for Alternative ranking IDRISI EMDS... (publications)

Design considerations for SDS ontologies EMDS – a tool implementing simple additive weighting EMDS Name: Ecosystem Management Decision Support Acronym: EMDS Overview: The Ecosystem Management Decision Support (EMDS) system provides decision support for integrated landscape evaluation and planning. The system provides decision support for landscape-level analyses through logic and decision engines integrated with ArcGIS The logic engine evaluates landscape data against a formal logic specification, designed with NetWeaver Developer, to derive logic-based interpretations of ecosystem conditions such as biodiversity and sustainability. The decision engine evaluates NetWeaver outcomes (and data related to additional factors such as feasibility and efficacy of land management actions) against a decision model for prioritizing landscape features with decision models built in Criterium DecisionPlus Platform: Windows XP Cost: Free... used for decision process phase/steps Condition assessment Choice... methods implemented Multiattribute decision rule Uncertainty methods accept data of process types Biophysical process economic process management process Social process... tool maker EMDS Consortium analysis extent User specified analysis unit User specified software required ArcGIS 9.x Crriterion Decision Plus NetWeaver used in case studies Sandy River Basin Anchor Habitat Project...

Design considerations for SDS ontologies Degree of formalization E.g. for “decision problems”: Problem types Problem objective Problem domain Institutional context Legal context Social context Cultural context Geographical context Temporal extent Whether spatial computation is required Analysis unit

Design considerations for SDS ontologies Degree of formalization E.g. for “spatial decision systems and tools”: name acronym summary overview toolmaker decision problem types targeted decision process steps served methods and techniques implemented used in case studies analysis extent analysis unit data models used model application areas indicators used supports analysis of interdisciplinary interactions supports multi spatial scale analysis supports social negotiation input data format output data type description of system components software required platform scientific expertise level required technical expertise level required developer support needed development status online download available cost information source

Design considerations for SDS ontologies

Degree of formalization Use of inverse relations, for example, for decision process phases / steps and commonly used methods and techniques  specify the reciprocal relations between a method and a process phase/step. methods and techniques implemented / implemented by tools  specify the reciprocal relations between a tool and a method. used for decision process phases / steps and commonly used tools  specify the reciprocal relations between a tool and a process phase/step

Design considerations for SDS ontologies Degree of formalization  Dictated by the purpose of the ontologies  Set of attributes and relations that are minimal, but sufficient for the automation of information access as well as reasoning (to derive implicit knowledge)

Design considerations for SDS ontologies Treatment of natural language Natural language term for concept Abbreviations or acronyms Synonyms Description Editorial notes (comments, editor, editor’s notes, etc.)

Design considerations for SDS ontologies SDS Knowledge Repository natural language term abbreviation synonyms description

Design considerations for SDS ontologies Modular design 800 concepts, and 200 attributes and relations, partitioned into 35 ontologies, grouped into 8 major components

Design considerations for SDS ontologies ComponentContent IntroductionDefinition of spatial decision support, the related fields of study, the basic concepts related to the notion of decision Decision context The context in which to frame a decision: decision problem types, application domains, institutional, legal, social, cultural, geographic contexts of a decision Spatial decision process Knowledge about spatial decision processes: major phases and sub steps during a structured spatial decision process; typical spatial decision process workflows for different application domains Methods and techniques Methods and techniques used during a spatial decision process TechnologyTechnology available for SDS, including equipment and software, especially the spatial decision support systems and tools. Related information on information systems, general SDSS functionalities People and participation Collaborative dimension of decision making process, decision process participant roles Domain data and knowledge Data, data topics, data sources, data models, domain models ResourcesSDS software systems and tools, domain models, related literature, case studies, related websites, data sources, data models, decision process workflow templates, organizations and people referred to elsewhere in the ontologies

Design considerations for SDS ontologies Based on part of iso owl by Drexel University. Data topics re- implemented as classes instead of instances for future expansion for better topic granularity. Potential interface with other ontologies “Upper level ontologies” potentially replaced by available better defined ontologies

Design considerations for SDS ontologies Advantages of an ontology-based SDS Knowledge Portal Attributes and relations among concepts and formalized to facilitate automatic information retrieval Search query expansion based on multiple natural language references to the same concept Search query expansion based on parent-child relations “Implicit knowledge” extraction based on relations that are inverse, transitive, etc. “Smart” recommendations based on semantic similarity among concepts

Design considerations for SDS ontologies Conclusion and future work SDS ontologies provide a conceptual framework for Formally representing the body of knowledge in the field of SDS Organizing various types SDS resources Formally describing SDS resources for interoperability Future work Improve the definition of concepts, especially in the area of collaborative decision making Include more representative, modular, reusable SDS resources, especially tools and models Consideration for different “dialect” among different user communities (language and dialect tags on rdfs:label property of concepts)

Thank You Design considerations for SDS ontologies Karen Kemp, Robert Raskin, Naicong Li,

Design considerations for SDS ontologies Complexities of spatial decision making Spatial decision making faces various decision complexities such as Spatial nature and temporal development of phenomena and processes; Complex multi-dimensional and heterogeneous data describing decision situations; Large or extremely large data sets that include data in numerical, map, image, text, and other forms; Large number of available alternatives or a need to generate decision alternatives "on the fly" according to the changing situation; Multiple participants with different and often conflicting interests; Multiple categories of knowledge involved, including expert knowledge and layman knowledge.

Design considerations for SDS ontologies Practical definition of “ontology”: A domain specific ontology is constructed on the basis of a controlled vocabulary whose meaning is agreed upon by the members of a particular user community. An ontology is a classification system, contains the conceptual categories in this domain, and for each category, the defining attributes of this category, and the various relations that this category bears to other categories. A category has instances sharing the same defining attributes and relations of this category; the difference in their attribute values make them distinct individuals. Ontology language used in this project: OWL