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Ontologies for Crisis Management: A Review of State of the Art in Ontology Design and Usability
- 1. Ontologies for Crisis Management: A Review of State of the Art in Ontology Design and Usability Shuangyan Liu Aston University, Birmingham, UK s.liu10@aston.ac.uk WITH THE FINANCIAL SUPPORT OF THE PREVENTION, PREPAREDNESS, AND CONSEQUENCE MANAGEMENT OF TERRORISM AND OTHER SECURITY-RELATED RISKS PROGRAMME. EUROPEAN COMMISSION - DIRECTORATE-GENERAL HOME AFFAIRS
- 2. Outline • What is Semantic Technologies? • What is an Ontology? • Why is Semantic Technologies related? • Existing Ontologies for Disaster Management • Conclusion and Future Work 2
- 3. What is Semantic Technologies? 3 Semantic Web Technology Stack (Steve Bratt, 2006) Assigning unambiguous name for something Basic data model OWL is an ontology language with rich expressiveness Still experimental
- 4. A Flooding Scenario 4 Charity DB Worcestershire County Council DB Table Vulnerable_residents Table disadvantaged_groups How does a program to discover the common meanings for the databases it encounters?
- 5. What is an Ontology? Tim Berners-Lee: “ An ontology is a document or file that formally defines the relations among terms.” OWL – a formal ontology language, and it provides standard labels for describing terms. o Classes (owl:class, owl:unionOf etc.) o Properties (owl:ObjectProperty, owl:DatatypeProperty, rdfs:domain, rdfs:range etc.) o Property restriction (owl:allValuesFrom, owl:cardinality etc.) o Relations (owl:equivalentClass, rdfs:subClassOf, owl:equivalentProperty etc.) o Characteristics of properties (e.g. owl:SymmetricProperty) o Datatypes (e.g. rdfs:Literal) o ... and more A domain ontology provides a shared understanding of the domain. Querying and reasoning using an ontology can help reveal implicit concepts and relationships that may not readily apparent. 5
- 6. Why is Semantic Technologies/Ontologies related? • To promote semantic interoperability • To provide a specified reference or a common language that can be used to specify disaster- related things • To enable integration of crisis information systems and data • To add semantics to web services descriptions which enables automatic service discovery 6
- 7. A Survey of Ontologies for Disaster Management • Research Motivation • Research Methodology – Research Questions – Data Collection Method – Data Analysis Method • Results – Coverage of Ontologies – Design of Ontologies – Use Cases of Ontologies 7
- 8. Research Motivation • The semantic interoperability challenge (Fan & Zlatanova, 2011; W3C EIIF 2009) • Prerequisite - to establish shared vocabularies • Lack of a common vocabulary in disaster management • No overview of the information • Aim – identify the areas of concepts represented in crisis information management systems, and – existing ontologies that cover these concepts. 8
- 9. Research Questions • What subject areas do the concepts used in disaster management belong to? • What are the existing ontologies that cover these subject areas? • How are the existing ontologies for disaster management designed and used? 9
- 10. Methodology 10 Data Collection Method search & select analyse search & identify
- 11. Methodology Data Analysis Sub-questions – QI-1: What subject areas describe the range of concepts involved in crisis and disaster management? – QII-1: What ontologies exist that cover each subject area? – QII-2: Does an individual ontology include concepts in one subject area or in multiple subject areas? – QII-3: Is the ontology represented formally? If yes, what language is used to describe the ontology? – QII-4: Is the ontology publicly accessible e.g. downloadable from a website? – QIII-1: What is the purpose of the ontology e.g. the type of crisis management system it is aimed for? – QIII-2: How many concepts or terms are defined in the ontology? – QIII-3: What categories of concepts are defined in the ontology e.g. classes, object properties and/or data properties? – QIII-4: What is the approach or principle used to design the ontology? – QIII-5: Is there a use case that demonstrates the functionalities of the ontology? 11 Data Analysis Method
- 12. Results 12 Subject Areas in Disaster Management
- 13. Results Subject Area Number of Ontologies Identified Ontology Name Representation Language Downloadable Documentation Resources 3 SOKNOS OWL-DL No Minimal (academic nature) MOAC RDF Yes Online specification SIADEX Not known No Minimal (academic nature) Processes 2 ISyCri OWL-DL No Minimal (private wiki and in French) WB-OS XML Available upon request Academic nature People 2 FOAF RDF Yes Online specification BIO RDF Yes Online specification Organisations 3 ERO2M N/A No Academic nature IntelLEO RDF Yes Online specification Organisation Ontology RDF Yes Online specification 13 Existing Ontologies
- 14. Results Subject Area Number of Ontologies Identified Ontology Name Representation Language Downloadable Documentation Damage 1 HXL RDF Yes Online specification Disasters 4 EM-DAT N/A Online query Classification of disasters available UNEP-DTIE N/A Online query Online documentation Canadian Disaster Database N/A Online query Classification of disasters available Australian Government Attorney-General’s Department Disasters Database N/A Online query Online documentation Infrastructure 3 PSCAD N/A No Minimal (academic nature) EPANET N/A No Minimal (academic nature) OTN OWL Yes Specification available Geography 1 GeoNames RDF Yes Online documentation 14 Existing Ontologies (Cont.)
- 15. Results Subject Area Number of Ontologies Identified Ontology Name Representation Language Downloadable Documentation Hydrology 1 Ordnance Survey Hydrology Ontology OWL Yes Online documentation Meteorology 1 NNEW weather ontology OWL Yes Online documentation Topography 4 USGS CEGIS OWL Yes Not available Ordnance Survey Buildings and Places Ontology OWL Yes Online documentation E-response Building Pathology Ontology OWL Yes Not available E-response Building Internal Layout Ontology OWL Yes Not available Other 1 AktiveSA (multi- domain) OWL Yes Not available 15 Existing Ontologies (Cont.)
- 16. Existing Ontologies 16 Implications 4, 15% 4, 15% 3, 11% 3, 11% 3, 12% 2, 8% 2, 8% 1, 4% 1, 4% 1, 4% 1, 4% 1, 4% Number of Existing Ontologies Disasters Topography Resources Organisations Infrastructure Processes People Damage Hydrology Meteorology Geography Other
- 18. Results 18 Purpose of Ontologies Purposes General Disaster Archive Humanitarian Response & Relief Infrastructure System Simulators Decision Support Response Coordination Resource Management Disaster Management Guidance Website Situation Awareness
- 19. Results 19 Coverage of Ontologies • 65% lightweight • average size: 119 concepts • 35% large – large number of instances • 65% contains four types of concepts
- 20. Results • Taxonomy • Specialisation • Hierarchical structure • Properties • Relationships • Upper level ontology • Reverse-engineering approach • Scenario-based approach 20 Design Principles
- 21. Results • Some important subject areas are not fully addressed e.g. damage, people, processes • No formally represented ontology for describing disaster events • Lack of links between subject areas 21 Gaps
- 22. Results 22 Use Cases of Ontologies
- 23. Conclusion • As a result of the review, we identified a set of critical subject areas that cover the information concepts dealt with in crisis management and the currently existing ontologies that represent these subject areas. • All of the identified 11 subject areas are covered by existing ontologies and 65% of the existing ontologies are semantically interoperable. • This review provides an overall picture of the subject areas and how they are represented and used in crisis management systems. • It provides a basis for identifying the missing vocabularies and for constructing a new framework of ontologies for emergency and disaster management. 23
- 24. Where Are We Now? • Proposed a semantic framework of emergency and disaster management – http://www.disaster20.eu/smerst-2013/wp- content/uploads/2013/05/shuangyan_presentation_SMERST2013. pdf • Developed an ontology (Cihai) for emergency and disaster response information interoperability • Developed a use case of using Cihai to structure earthquake data from GDACS website (presentation on ) – http://youtu.be/ZzrxYn_s2A0 24
- 25. Future Work • Publish the Cihai ontology online (presence, feedback, improvement) • Develop use cases (feedback, improvement, applications) • Collaboration (W3C Emergency Information Community Group) 25
- 26. The End Thank you! Disaster 2.0 Project Semantic Technologies for Disaster Management Shuangyan Liu s.liu10@aston.ac.uk 26
Notas del editor
- This is part of my research for the Disaster 2.0 projectBackground on the project, we want to explore how SW tech are currently and can be potentially used for disaster management.
- Semantic technologies are the tech created in the development of the Semantic Web [2][3].Most of the Web's content today is designed for humans to read, not for computer programs to manipulate meaningfully. Computers can adeptly parse Web pages for layout and routine processing—here a header, there a link to another page—but in general, computers have no reliable way to process the semantics: this is the home page of the Hartman and Strauss Physio Clinic, this link goes to Dr. Hartman's curriculum vitae. The Semantic Web will bring structure to the meaningful content of Web pages, creating an environment where software agents roaming from page to page can readily carry out sophisticated tasks for users. Instead these semantics were encoded into the Web page,extension of the current one, The essential property of the World Wide Web is its universality. The power of a hypertext link is that "anything can link to anything. difference between information produced primarily for human consumption and that produced mainly for machines. the Web has developed most rapidly as a medium of documents for people rather than for data and information that can be processed automatically. The Semantic Web aims to make up for this. The Semantic Web aims to make up for this. The diagram represents the layer cake of SW, which describes the main layers of the SW design and vision. At the bottom we find XML which a language letting one write structured web documents with a user-defined vocabulary. RDF is a basic data model, like ER model for writing simple statements about web resources. RDF Schema provides modelling primitives for organising web objects into hierarchies. RDFS is based on RDF. RDFS can be viewed as a primitive language for writing ontologies. OWL is a more powerful ontology language that expand RDF Schema and allow the representations of more complex relationships between objects.The logical layer is used to enhance the ontology language further and to allow the writing of application-specific declarative knowledge (in declarative sentences or indicative propositions). The proof layer involves the actual deductive process and the representation of proofs in web languages and proof validation. The Trust layer will emerge through the use of digital signatures and other kinds of knowledge (recommendations by trusted agents or on rating and certification agencies and consumer bodies.Other technologies like trust management are still work in progress.!! XML allows users to add arbitrary structure to their documents but says nothing about what the structures mean.
- Let’s have a look at a flooding scenario first, which shows the typical type of problem that semantic technologies are applied to.Flooding is a common disaster in UK. The recent flooding happened across the country last Nov. More than 800 homes have been flooded after storms hit parts of England and Wales. A number of homes in Kempsey, Worcestershire had to be evacuated after the storm. Some people might need extra support in a flood or emergency. Normally, the local authorities maintain the lists of vulnerable people in the area. Imagine the Worcestershire county council and Kempsey police both have a database that contains information about vulnerable people. However, the two databases may use different table identifiers for what is in fact the same concept. A program that wants to combine information across the two databases has to know that these two terms are being used to mean the same thing. Therefore, the program must have a way to discover such common meanings for whatever db it encounters.A solution to this problem is provided by the basic component of the Semantic Web called ontologies.
- A brief introduction to ontology. Originates from philosophy. What we mean is commonly used by the AI and Semantic Web community.OWL2 is a language for describing sets of things. These sets are called ‘classes’. Any statement we make about a class in OWL2 is used to differentiate that class from the set of all things.We use these labels to describe the terms for a domain. OWL have labels for defining classes and properties. It also has labels to define property restriction such as value constraints and cardinality constraints (e.g. a computer has only one motherboard.) It can define relations between classes and properties. And many other labels.(RDF – a basic data model for the semantic web, The expressive power of RDF and RDFS is very limited in some areas. Web Ontology Language (OWL) is an ontology language that provides richer expressiveness than RDF and RDF Schema. It adopts the RDFS meaning of classes and properties (rdfs:Class, rdfs:subClassOf, etc.) and adds language primitives to support the richer expressiveness required. )If go back to the flooding scenario, imagine an ontology is defined for the domain, two classes ‘VulerableResidents’ and ‘DisadvantagedGroup’ can be defined as equivalent classes. A program that wants to retrieve the related data from two different db can know they have the same meaning and thus can combine the data.
- At fundamental level, different groups can have fundamentally different conceptualisations of disasters and disaster management and might use very different terminologies, which prevents the integration of disaster data from different sources.serve as a specified reference to be used by personnel in different organisation, thus constitute common language to be spoken by the different organisationsserve as a standard, facilitateintegration of different crisis information systems at user interfaces and data levelmatch service requests and offers for discovering the most appropriate offer for a given request.
- At fundamental level, different groups can have fundamentally different conceptualisations of disasters and disaster management and might use very different terminologies
- Consequently, we aim to answer the following research questions:…Gaps ?What standards, if any, do the existing ontologies relevant to crisis management conform to?
- Data Collection Methodssearched databases and forums relevant to disaster management, information systems and semantic web selected nineteen papers to include in our review (for the full list of papers see [5])seeked for the keywords highlighting the concepts presented in the papers, and added to a list of subject areassearched the papers and the Web to identify relevant ontologiesThe number of relevant ontologies collected was 26. DriveQuestion 1: how do u generate the list of 11 subject areas? E.g. how to decide Shelter, which area? By common sense and by referring to dictionaries if not sure where to put. http://tagcrowd.com/
- We have identified 11 subject areas that concepts used in disaster management belong to.The subject areas identified show two groups of concepts involved in crisis information systems: common concepts (people, organisations, resources, disasters, geography, processes, infrastructure, damage) and unusual concepts (topography, hydrology and meteorology).Subclasses of each area as examples to illustrate the concept.
- A total of 26 ontologies identified that over the 11 subject areas.In the following tables, the number of the ontologies identified for each area, their names, the representation languages, theiraccessibility and documentation are illustrated.(here not mention - Point 1: Accessibility and representation - These tables show that among the ontologies designed originally for crisis management, very few (e.g. MOAC, HXL) are formally represented and publicly accessible. Point 2: Missing areas - For the disaster and infrastructure areas, no formally represented ontologies were found. For processes area, no public available ontologies found.)To mention: Point 3: Coverage of concepts- 20 out of 26 ontologies describe concepts in a single subject area.A few represent multiple subject areas. In the table, an ontology is assigned to the subject area where the main purpose of the ontology lies.
- Point out the patternsLower number are areas provides unusual concepts e.g. hydrology, meteorologySome important common conceptual areas are not fully addressed e.g. damage, people, processesOn the top of the list, although the biggest number of ontologies for disasters are found. If you take a look at the form, they are not formally represented, as in database scheme.Other top number of ontologies, they are not public available such as resources ontologies
- Analyse ‘No’ scenarioNot formal: mainly are the common areasNot public: mainly are the common areasNot well documented: mainly the common areasformal and public: half; not formal and public are mainly the common areasOnly few are formal, public and well documented
- General includes people, org, transport system, geography, and so on
- Patterns11 out of the 17 ontologies (i.e. 65% of the countable ones) are lightweight (containing totally less than 300 concepts, average size: 119 concepts).Six ontologies (35% of the countable ones) are relatively large (containing over 500 concepts, average size: 1297 concepts).Some contain large number of instances, e.g. SIADEX, GeoNames.
- Analysis of ontology design conducted for each subject area (totally 11 subject areas)Focusing on principles for structuring concepts, what concepts are represented in each ontology, and what types of crisis information systems they are aimed forTaxonomy (Damage, disaster)Specialisation (Damage)Hierarchical structure (All as Resources)Properties (People, Insfrastructure, Geo, Hydrology, Weather)Relationships (Processes, Geo)Upper level ontology (Organisation, SUMO, domain independent)Reverse-engineering approach (Topography, design from data already existing)Scenario-based approach (E-response Building pathology and layout ontology)The advantage of conforming to an upper level ontology lies in the ability to aligning the model to a set of common and cross-domain notions and thus can reduce the heterogeneity in domain specific ontologies.The reverse-engineering approach refers to the geospatial databases of a national map for constructing the topography ontology. Scenario-based approach: when the system does not exist, you are going to build an ontology for the system.===feedback===Show examples from the identified ontologies, mainly diagramsMissing areas
- Damage – affected population and places, are there other types of things are affected such as affected infrastructure such as affected airport aspect of damage, causesPeople – foaf general features, no specialisation of person type, shall we care about the variety of people involved in disaster response?Processes – related to actors, their resources, the services they provide and their procedures,No publicly available ontology found !! For a response planning system, we may care about who is going to do what type of response task and the procedures to complete the task (in case don’t know how – miseiphone app) However, Do the information need to be recorded during or after the disaster?Disaster – classification of disasters, other properties as start time, boundaryOpen discussion
- Drive Question 2: end-users of the research (Other Work will cover, show detail examples as linked data science) NGO to refer to the ontology to create the databases
- Use Case – Cihai at H4D2:Cihai Ontology Project at the hackathon H4D2Unstructured Earthquake Data from GDACS websiteStructured Data in RDF using Cihai OntologyFuseki SPARQL data repositoryCihai SPARQL endpointMake queries to the SPARQL endpoint
- Seek for