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Ontologic View of Earth Sciences

Why ontologies?

EarthCube’s Ontology and Semantic Web WorkshopBallston, VA April 30-May 1, 2012

Hassan Babaie1, 2 and Raj Sunderraman2

1Department of Geosciences, Georgia State University2Department of Computer Science, Georgia State University

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• The Earth is a system composed of major, globally interconnected complex components:• Atmosphere • Hydrosphere• Biosphere• Geosphere• Cryosphere • Each has its own sub-components

Earth Systems

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5

Interacting components

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Atmosphere

BiosphereGeosphere

Process

• Earth’s major components interact through processes

Earth scientists study the components and their parts at all scales

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• These unintegrated data actually stand in the way of both discovering new knowledge and raising new questions regarding the unknown

• In other words, the un-utilized facts in these data prevent us from knowing what we do NOT know!

• There are immense volumes of data collected from each Earth system

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Self-similar Research

The self-similarity that characterizes the research of interacting Earth science communities, and that of many geological processes, requires:

• Fractal structuring of resources:• e.g., software, database,

ontology, service, tools

• From groups of individualsto progressively larger communities, on the Earth science network

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What’s the Problem?• Understanding of data in databases requires

effective accessibility, query mechanism, usability, and post-search visualization by scientists

• Integration of the heterogeneous schema and vocabulary of these distributed databases requires significant programming, at high cost

• Knowledge management systems, dependent on these distributed and heterogeneous databases, if they exist, can only be scaled with difficulty and significant cost through constant updates

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Motivation for the RDF Data Model Most of Earth science Knowledge is available in publications Information is distributed and fragmented No means to efficiently browse/search this knowledge• Structured data in relational database (RDB) systems

do not carry semantics (meaning)• Changes in representation would cause the database

schema to change• Making software interoperable and the RDB and other data

types (text, HTML) machine understandable requires conversion of their data type into the Semantic Web RDF data model in the form of triples in ontologies (subject-predicate-object)

subjectpredicate (property) object

inContactWithReservoirRock Caprock

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composition

crystallizeInto

depth

Earth System Science approach• At each scale, Earth’s interacting complex objects are

investigated by Earth scientists at their atomic or sub-component levels.

• Goal: Integrate data and knowledge units (facts) from the subsystem level and apply them to the global scale, i.e., to the whole Earth system

Magma IgneousRock

Ore Mineral

Lake 1000 m

Earthquake 8.1richterMagnitude

Object properties level

Datatype Properties

• Need to map the building blocks of scientific knowledge (facts) into the building blocks of ontologies (RDF triples) in OWL

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containedIn

permeability

porosity

• Translate facts about both spatial objects and spatio-temporal objects (processes)

• Complex and simple objects communicate through processes; some occurring over several orders of magnitude (e.g., Faulting: 10-3-106 m)

infiltrate

Precipitation Aquiferrecharge

Raining

isA

Groundwater

10-12 m2

0.16

contains

Process

subPropertyOf

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hasPartpartOf

thermProp opticProp

physProp chemProp

gasProduct

meltProductmelt

Processes change state of objects

MeltingSolid

Rock

isA

Liquid

Mineral

MeltingConditionMagma

isAcondition

EarthMaterial ChemicalProperty

isA

PhysicalProperty

OpticalPropertyThermalProperty

Gas

Data about specific instances of these interactions, which are stored in domain databases and other kinds of files, can readily be converted into RDF data model (e.g., through RDB-to-RDF wrappers)

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Unintegrated Communities of ResearchHow many levels of ontology do we want to build?

Do we need a system for faster/easier integration of smaller communities, or one to allow wider inter-operability among larger communities, or both?

To individual level

Earth science level

Discipline level

Sub-discipline level

Which technology can achieve the optimum solution to reach the goals of EarthCube?

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Minimum Requirements• A mechanism to globally identify and integrate

data from variably-sized, locally-integrated but globally-distributed nodes of Earth scientists• One solution: Linked Open Data (LOD) Cloud

• Support mapping/integration of globally distributed community databases, text, etc.

• Provide ways to discover/use data stored in these local and Web-distributed databases by both Earth scientists and software agents

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Requirements cont’d• Develop ways to convert Web documents and

paper and digital scientific publications into machine interpretable formats (e.g., RDF)

• Include all aspects of scientific research about

the data (metadata) in ontologies, such as:• provenance, assumptions, quality, error,

precision, accuracy, uncertainty

• Support distribution, discovery, use, and reuse of ontologies) in all fields. Encourage the use of the controlled vocabularies in domain database

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Let data come to usLinked Open Data Cloud• Use the linked data space to connect the RDF data

models of all Earth science communities

• The cloud will incrementally foster public trust through transparency and community involvement

• It will allow community driven, Wikipedia type, RDF data curation, to guarantee maintenance of, and access to, high quality, relevant and trusted information

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• New data published will include multiple RDF links to the geospatial nodes on the LOD Cloud, such as GeoNames and Linked GeoData.

• These links allow additional data to be discovered from the cloud. The current position is used to search all the linked data in a query.

• We can publish our current position, images, and descriptions, say of an outcrop, to the cloud while standing on/by the outcrop

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Thank you!