A faceted lightweight ontology for Earthquake Engineering Research Projects and Experiments

Transcription

1 Eng. Md. Rashedul Hasan Phone: Fax: 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, University of Trento Via Mesiano 77, 38123, Trento, Italy. Md. Rashedul Hasan, Feroz Farazi, Oreste S. Bursi, Md. Shahin Reza Page 1 June 7, 2013

2 Acknowledgement 1. The authors gratefully acknowledge the supports from the European Union through the SERIES * project (Grant number: ). 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 Page 2

3 Outline Introduction Ontology development DERA Methodology Facet Ontology representation RDF OWL Existing ontology/thesaurus WordNet NEES Ontology Integration Experimental Set-up Conclusion Page 3 6/7/2013

4 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 Page 4

5 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 Page 5 Figure: Data Upload Form

6 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. Page 6

7 Motivation DBMS: KB: 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. 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. Page 7

8 Motivation UNITN have already developed some ontologies for representing space domain GeoWordNet space ontology Figure: GeoWordNet (Giunchiglia et al, 2010) Page 8

9 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. Page 9 6/7/2013

10 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. Page 10

11 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. Page 11 6/7/2013

12 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 Experiment Static ocyclic test omonotonic test Dynamic opsd (Pseudo-dynamic) test with substructuring oshaking table test oshaker-based Test ohammer-based test Page 12 6/7/2013

13 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]. 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 Page 13 6/7/2013

14 Keys Resource Ontology Representation RDF #2 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. Page 14

15 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. Page 15

16 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. Page 16

17 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. Page 17

18 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. Page 18

19 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 Page 19

20 Experimental Set-up #2 A segment of the code that is used to publish the ontology in RDF is given below: Page 20

21 Experimental Set-up #3 Figure: Synonym relationship Page 21 Figure: Transitive relationship

22 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. Page 22

23 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 , Trento Italy Page 23 5/24/2013