SERIES Concluding Workshop Joint with US-NEES
JRC, Ispra, May 28-30, 2013
A faceted lightweight ontology
for
Earthquake Engineering Research Projects and
Experiments
Department of Civil, Environment and Mechanical Engineering,
Md. Rashedul Hasan, Feroz
Farazi,ofOreste
University
Trento S. Bursi, Md. Shahin Reza
Via Mesiano 77, 38123, Trento, Italy.
Eng. Md. Rashedul Hasan
email: [email protected]
Phone: +39-0461-282571
Fax:
+39-0461-282521
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Acknowledgement
1. The authors gratefully acknowledge the supports from the European
Union through the SERIES* project (Grant number: 227887).
2. The authors gratefully acknowledge the supports from the NEES
3. The authors gratefully acknowledge the supports of the University of
Trento for Research activities.
4. The authors gratefully acknowledge the supports from the ERNCIP.
*SERIES:
Seismic Engineering Research Infrastructures for European
Synergies
*NEES: Network for Earthquake Engineering Simulation
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Outline
•
•
•
•
•
•
•
Introduction
Ontology development
– DERA Methodology
– Facet
Ontology representation
– RDF
– OWL
Existing ontology/Thesaurus
– WordNet
– NEES
Ontology Integration
Experimental Set-up
Conclusion
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Introduction
• Ontology is a means of representing knowledge of a
domain.
• In an ontology, knowledge is represented as a set of
axioms.
• Axioms are the relations between:
– concepts (e.g., Scientist, Researcher)
• Researcher is-a scientist
– entities (e.g., Einstein, Satyen Bose)
• Einstein collaborated-with Satyen Bose
– Concept and Entity
• Einstein is a researcher
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Objective
•
To develop an ontology for information sharing and extraction
between/from researchers and research community in the field of
Earthquake Engineering
Potential Users
 Earthquake Engineering
Research Community
 Laboratory
 Researcher
Figure: Data Upload Form
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Context
• Our domain of interest is earthquake engineering
• So far, Ontologies have been overlooked in this field.
• To the best of our knowledge, except NEES no other
ontology has been developed.
• However, it is mainly a thesaurus encoding broader and
narrower relations.
• It does not capture ontological details for example, is-a and
part-of cannot be distinguished in it.
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Motivation
• DBMS:
– Traditionally, we use Database Management Systems (DBMS) for
managing information.
– DBMSs are powerful tools for managing large amount of data.
– Nevertheless, it lacks among others reasoning capability.
• KB:
– A Knowledge Base (KB) consists of, possibly, a set of ontologies
developed to cover a single domain or multiple domains.
– Information in KB enables reasoning tools to do inference.
– KB provides mechanism for not only managing data but also their
semantics through ontologies.
• To overcome the limitation of DBMS and to exploit the benefit offered
by the state-of-the-art technologies, we can use KB-based systems.
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Motivation
• UNITN have already developed some ontologies for
representing space domain
– GeoWordNet
– space ontology
Figure: GeoWordNet (Giunchiglia et al, 2010)
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Ontology development
DERA Methodology
– DERA is a faceted knowledge organization methodology.
– It allows for building domain specific ontologies.
– However, it can be used to develop ontology for any
domain.
– In DERA, a domain consists of three elementary
components namely entity, relation and attribute.
D=<E, R,A>
– E is a tuple <C, e>, where C represents a set of concepts
and e represents a set of entities.
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Ontology development
DERA Methodology
• Methodology:
– Step 1: Identification of the atomic concepts
– Step 2: Analysis
– Step 3: Synthesis
– Step 4: Standardization
– Step 5: Ordering
• Following the above steps leads to the creation of
a set of facets.
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Ontology development
Facet
•
Facet is a hierarchy of homogeneous terms describing an aspect of
the domain, where each term in the hierarchy denotes a different
concept.
•
A facet is a distinctive property of a set of concepts that can help in
differentiating one group from another.
•
Faceted ontology is an ontology in which concepts are organized into
facets.
•
S. R. Ranganathan [1934] was the first to introduce the faceted
approach in organizing concepts into hierarchies.
•
GeoWordNet is an example of a faceted ontology consists of facets
such as body of water, geological formation and administrative
division.
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Ontology development
Faceted Ontology Example
• As part of the ontology development
– We developed 11 facets
– We included 193 concepts
Device
•Shaker
•Instrument Hammer
•Active Structural device
•Passive Structural device
oHydraulic damper
oElectrical damper
oMR damper
oFriction damper
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Experiment
•Static
oCyclic test
oMonotonic test
•Dynamic
oPSD (Pseudo-dynamic)
test with substructuring
oShaking table test
oShaker-Based Test
oHammer-Based test
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Ontology Representation
RDF #1
• A language to represent all kinds of things that
can be identified on the Web [RDF Primer].
• A language with an underlying model designed to
publish data on the Semantic Web [F. Giunchiglia
et al., 2010].
http://www.w3.org/TR/rdf-primer/
A facet-based methodology for the construction of a large-scale geospatial
ontology F Giunchiglia, B Dutta, V Maltese, F Farazi - Journal on Data Semantics, 2012
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Keys
Ontology Representation
RDF #2
 Resource
 Represent a thing or a class or an entity.
 For example, web pages, articles, authors, etc.
 Property
 Metadata of the resources to be described.
 For example, creator, date of creation, publisher, etc.
 Statement
 A piece of information about a resource represented using a property and a value.
 For example, Tim Berners-Lee authored Weaving the Web. In other words,
Weaving the Web has an author (or creator) whose value is Tim Berners-Lee.
 A subject (Weaving the Web)–predicate (creator)–object (Tim Berners-Lee) triple.
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Ontology Representation
OWL
 Web Ontology Language is designed to be used when the
document content is necessary to be processed by applications
instead of making it understandable only by humans [OWL
Overview].
 It can be used to represent ontology
 Vocabulary terms and the relationships between them.
 Concepts and relations between them.
 It provides more facilities than RDF and RDF Schema
 In the representation of semantics.
 In performing reasoning tasks.
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Existing Ontology
•
WordNet (Princeton)
– WordNet is an ontology consists of more than 100 thousand
concepts and 26 different kinds of relations (e.g., hyponym).
– It contains 155,287 words organized in 117,659 synsets for a total
of 206,941 word-sense pairs.
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Existing Thesaurus
NEES
 NEES is a thesaurus containing hierarchy of the terms about
Earthquake engineering.
 It contains around 300 concepts organized into broader term and
narrower term hierarchy.
 We have integrated in our ontology 75 concepts from NEES.
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Ontology Integration
To increase the coverage, we integrated Earthquake research project
faceted ontology with WordNet
• Integration macro-steps are as follows:
•
– Facet concept identification: For each facet, the concept of its root node is
manually mapped with WordNet. We call it the facet concept
– Concept identification:
• For each atomic concept C of the faceted ontology, check if the corresponding
class label is available in WordNet. If the label is available, retrieve all the
candidate concepts for it.
• For each candidate concept check if it is more specific than the facet concept
– Parent identification: In case of unavailability of a concept it tries to identify
parent.
• For each multiword concept label it checks the presence of the header and if
it is found within the given facet it identifies it as a parent.
• In case of failure, ask for manual intervention.
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Experimental Set-up #1
JENA
•
Jena is a Java framework developed by HP Labs.
•
It can deal with RDF, OWL and SPARQL.
•
It supports reasoning over RDF and OWL ontologies.
•
For performing reasoning, we configured the following inference
engine:
– OntModelSpec.OWL_MEM_MICRO_RULE_INF
Sesame and OWLIM: used for storage
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Experimental Set-up #2
A segment of the code that is used to publish the ontology in RDF
is given below:
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Experimental Set-up #3
Figure: Synonym relationship
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Figure: Transitive relationship
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Conclusion
• We have presented the development process of the
ontology as well as User Interface (UI).
• The developed Ontology
– useful for effective sharing and extraction of data;
– can be used by Laboratory, Researchers…
• We
have presented ontology as a tool for
representing knowledge and Jena as a tool for
managing ontology.
• We have described the part of the faceted ontology
we have built.
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SERIES Concluding Workshop - Joint with US-NEES
University of Trento, Trento, Italy
Thank You for Your Attention
Any Question?
Md. Rashedul Hasan
Feroz Farazi
Prof. Oreste S. Bursi
Md. Shahin Reza
University of Trento
Via Mesiano 77
38123, Trento
Italy
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