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Semantic Data Integration in Humanities

Semantic Data Integration in Humanities. Mark Greengrass (University of Sheffield) Oscar Corcho (University of Manchester). A+H e-Science Lecture Edinburgh, June 18th 2007. Contents. Introduction to ontologies Definitions Ontology development Ontologies and the Semantic Web

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Semantic Data Integration in Humanities

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  1. Semantic Data Integration in Humanities Mark Greengrass (University of Sheffield) Oscar Corcho (University of Manchester) A+H e-Science Lecture Edinburgh, June 18th 2007

  2. Contents • Introduction to ontologies • Definitions • Ontology development • Ontologies and the Semantic Web • Ontologies for humanities • TGN, ULAN, HASSET, CULTOS, VICODI • Projects using ontologies for humanities • Historillo • Cultural tour (Residencia de Estudiantes) • Semantic data/information integration approaches • Open issues and discussion Semantic Data Integration for Humanities

  3. Reuse and Sharing Areas: • Software • Knowledge • Communications • Interfaces • --- Reuse means to build new applications assembling components already built Sharing is when different applications use the some resources Advantages: • Less money • Less time • Less resources Semantic Data Integration for Humanities

  4. The Knowledge Sharing Initiative • Building Knowledge Based Systems usually entails constructing new knowledge bases from scratch. • It could instead be done by: • Assembling reusable components. • System developers would then only need to worry aboutcreating the specialized knowledge and reasonersnew to the specific task of their systems. • New systems wouldinteroperate with existing systems. • Declarative knowledge, problem-solving techniques, and reasoning services could all be shared between systems. Neches, R.; Fikes, R.; Finin, T.; Gruber, T.; Patil, R.; Senator, T.; Swartout, W.R. Enabling Technology for Knowledge Sharing. AI Magazine. Winter 1991. 36-56. Semantic Data Integration for Humanities

  5. Reusable Knowledge Components Interaction Problem Representing Knowledge for the purpose of solving some problem is strongly affected by the nature of the problem and the inference strategy to be applied to the problem Ontologies Problem Solving Methods Describe domain knowledge in a generic way and provide agreed understanding of a domain Describe the reasoning process of a KBS in an implementation and domain-independent manner Bylander Chandrasekaran, B. Generic Tasks in knowledge-based reasoning.: the right level of abstraction for knowledge acquisition. In B.R. Gaines and J. H. Boose, EDs Knowledge Acquisition for Knowledge Based systems, 65-77, London: Academic Press 1988. Semantic Data Integration for Humanities

  6. Definitions of Ontologies 1. “An ontology defines the basic terms and relations comprising the vocabulary of a topic area, as well as the rules for combining terms and relations to define extensions to the vocabulary” Neches R, Fikes RE, Finin T, Gruber TR, Senator T, Swartout WR (1991) Enabling technology for knowledge sharing. AI Magazine 12(3):36–56 2. “An ontology is an explicit specification of a conceptualization” Gruber TR (1993a) A translation approach to portable ontology specification. Knowledge Acquisition 5(2):199–220 Studer R, Benjamins VR, Fensel D (1998) Knowledge Engineering: Principles and Methods. IEEE Transactions on Data and Knowledge Engineering 25(1-2):161–197 3. “An ontology is a formal, explicit specification of a shared conceptualization” Guarino N, Giaretta P (1995) Ontologies and Knowledge Bases: Towards a Terminological Clarification. In: Mars N (ed) Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing (KBKS’95). University of Twente, Enschede, The Netherlands. IOS Press, Amsterdam, The Netherlands, pp 25–32 4. “A logical theory which gives on explicit, partial account of a conceptualization” Guarino N (1998) Formal Ontology in Information Systems. In: Guarino N (ed) 1st International Conference on Formal Ontology in Information Systems (FOIS’98). Trento, Italy. IOS Press, Amsterdam, pp 3–15 5. “A set of logical axioms designed to account for the intended meaning of a vocabulary” Semantic Data Integration for Humanities

  7. Example of Domain Ontology. Protégé Semantic Data Integration for Humanities

  8. Example of a domain ontology. WebODE Semantic Data Integration for Humanities

  9. Components of an Ontology Concepts are organized in taxonomies Relations R: C1 x C2 x ... x Cn-1 x Cn Subclass-of: Concept 1 x Concept2 Connected to: Component1 x Component2 Functions F: C1 x C2 x ... x Cn-1 --> Cn Mother-of: Person --> Women Price of a used car: Model x Year x Kilometers --> Price Instances Elements Gruber, T. A translation Approach to portable ontology specifications. Knowledge Acquisition. Vol. 5. 1993. 199-220. Axioms Sentences which are always true Semantic Data Integration for Humanities

  10. Vocabulary • “Class”  “Concept”  “Category”  “Type” • “Instance”  “Individual” • “Entity”  “object”, Class or individual • “Property”  “Slot”  “Relation”  “Relationtype”  “Attribute”  Semantic link type”  “Role” • but be careful about “role” • Means “property” in description logics • Means “role played” in most ontologies • E.g. “doctorrole”, “student role” … Semantic Data Integration for Humanities

  11. Using Frames and First Order Logic for Modeling Ontologies (define-functionPays (?room ?discount) :-> ?finalPrice "Price of the room after applying the discount" :def (and (Room ?room) (Number ?discount) (Number ?finalPrice) (Price ?room ?price)) :lambda-body (- ?price (/ (* ?price ?discount) 100))) (define-classTravel (?travel) "A journey from place to place" :axiom-def (and (Superclass-Of Travel Flight) (Template-Facet-Value Cardinality arrivalDate Travel 1) (Template-Facet-Value Cardinality departureDate Travel 1) (Template-Facet-Value Maximum-Cardinality singleFare Travel 1)) :def (and (arrivalDate ?travel Date) (departureDate ?travel Date) (singleFare ?travel Number) (companyName ?travel String))) (define-relation connects (?edge ?source ?target) "This relation links a source and a target by an edge. The source and destination are considered as spatial points. The relation has the following properties: symmetry and irreflexivity." :def (and (SpatialPoint ?source) (SpatialPoint ?target) (Edge ?edge)) :axiom-def ((=> (connects ?edge ?source ?target) (connects ?edge ?target ?source)) ;symmetry (=> (connects ?edge ?source ?target) (not (or (part-of ?source ?target) ;irreflexivity (part-of ?target ?source)))))) (define-instanceAA7462-Feb-08-2002 (AA7462) :def ((singleFare AA7462-Feb-08-2002 300) (departureDate AA7462-Feb-08-2002 Feb8-2002) (arrivalPlace AA7462-Feb-08-2002 Seattle))) Semantic Data Integration for Humanities

  12. Using Description Logics for Modeling Ontologies (defrelationPays :is (:function (?room ?Discount) (- (Price ?room) (/(*(Price ?room) ?Discount) 100))) :domains (Room Number) :range Number) (defconceptTravel "A journey from place to place" :is-primitive (:and (:all arrivalDate Date)(:exactly 1 arrivalDate) (:all departureDate Date)(:exactly 1 departureDate) (:all companyName String) (:all singleFare Number)(:at-most singleFare 1))) (defrelationconnects "A road connects two different cities" :arity 3 :domains (Location Location) :range RoadSection :predicate ((?city1 ?city2 ?road) (:not (part-of ?city1 ?city2)) (:not (part-of ?city2 ?city1)) (:or (:and (start ?road ?city1)(end ?road ?city2)) (:and (start ?road ?city2)(end ?road ?city1))))) (tellm (AA7462 AA7462-08-Feb-2002) (singleFare AA7462-08-Feb-2002 300) (departureDate AA7462-08-Feb-2002 Feb8-2002) (arrivalPlace AA7462-08-Feb-2002 Seattle)) Semantic Data Integration for Humanities

  13. Using UML for Modeling Ontologies Semantic Data Integration for Humanities

  14. Using the Entity Relationship Model for Modeling Ontologies Semantic Data Integration for Humanities

  15. A semantic continuum Pump: “a device for moving a gas or liquid from one place or container to another” (pump has (superclasses (…)) Semantics hardwired; used at runtime Semantics processed and used at runtime Text descriptions Informal (explicit) Formal (for humans) Formal (for machines) Shared human consensus Implicit  Further to the right  • Less ambiguity • Better inter-operation • More robust – less hardwiring • More difficult Uschold M Semantic Data Integration for Humanities

  16. Types of vocabularies. Formality Add your vocabularies here  Lassila O, McGuiness D. The Role of Frame-Based Representation on the Semantic Web. Technical Report. Knowledge Systems Laboratory. Stanford University. KSL-01-02. 2001. Semantic Data Integration for Humanities

  17. Contents • Introduction to ontologies • Definitions • Ontology development • Ontologies and the Semantic Web • Ontologies for humanities • TGN, ULAN, HASSET, CULTOS, VICODI • Projects using ontologies for humanities • Historillo • Cultural tour (Residencia de Estudiantes) • Semantic data/information integration approaches • Open issues and discussion Semantic Data Integration for Humanities

  18. Principles for the Design of Ontologies (I) Clarity: To communicate the intended meaning of defined terms Coherence: To sanction inferences that are consistent with definitions Extendibility: To anticipate the use of the shared vocabulary Minimal Encoding Bias: To be independent of the symbolic level Minimal Ontological Commitments: To make as few claims as possible about the world • Gruber, T.; Towards Principles for the Design of Ontologies. • KSL-93-04. Knowledge Systems Laboratory. • Stanford University. 1993 Semantic Data Integration for Humanities

  19. Principles for the Design of Ontologies (II) • The representation of disjoint and exhaustive knowledge. If the set of subclasses of a concept are disjoint, we can define a disjoint decomposition. The decomposition is exhaustive if it defines the superconcept completely. • To improve the understandability and reusability of the ontology, we should implement the ontology trying to minimize the syntactic distance between sibling concepts. • The standardization of names. To ease the understanding of the ontology the same naming conventions should be used to name related terms. Arpírez JC, Gómez-Pérez A, Lozano A, Pinto HS (1998) (ONTO)2Agent: An ontology-based WWW broker to select ontologies. In: Gómez-Pérez A, Benjamins RV (eds) ECAI’98 Workshop on Applications of Ontologies and Problem-Solving Methods. Brighton, United Kingdom, pp 16–24 Semantic Data Integration for Humanities

  20. Types of Ontologies Issue of the Conceptualization Content Ontologies Domain O. Representation O. Scalpel, scanner anesthetize, give birth • Conceptualization • of KR formalisms Task O. goal, schedule to assign, to classify Generic O. • Reusable across D. General/Common O. Domain O. Things, Events, Time, Space Causality, Behavior, Function • Reusable Application O. Mizoguchi, R. Vanwelkenhuysen, J.; Ikeda, M. Task Ontology for Reuse of Problem Solving Knowledge. Towards Very Large Knowledge Bases: Knowledge Building & Knowledge Sharing. IOS Press. 1995. 46-59. Van Heist, G.; Schreiber, T.; Wielinga, B. Using Explicit Ontologies in KBS International Journal of Human-Computer Studies. Vol. 46. (2/3). 183-292. 1997 • Non reusable • Usable Semantic Data Integration for Humanities

  21. Libraries of Ontologies (I) Reusability Usability - + Application Domain O. : heart-diseases Application Domain Task O.:surgery heart Domain Task O.:plan-surgery Domain O.:body Generic Task O.:plan Generic Domain O.: components General/Common Ontology: Time, Units, Space, ... - + Representation Ontology: Frame-Ontology, OWL KR Ontology Semantic Data Integration for Humanities

  22. Relationship between Ontologies in the Library Environmental Pollutants Monoatomic-Ions Poliatomic-ions Chemical-Elements Standard-Units Standard-Dimensions Physical-Quatities Kif-Numbers Frame-Ontology Semantic Data Integration for Humanities

  23. How to Reuse Ontologies from a Library Top level ontologies Knowledge representation ontologies REPRESENTATION ENTITY CONCEPT Subclass of ENTITY Subclass of Subclass of Instance of PHYSICAL SUBSTANTIAL ATTRIBUTE Subclass of Instance of Instance of ELEMENT Oxidation-State (0, N) Atomic-Weight (1, 1) Atomic-Number (1, 1) Domain ontologies Subclass of Subclass of Partition REACTIVELESS REACTIVENESS Semantic Data Integration for Humanities

  24. Seamark Demo: ID new drug candidates for BRKCB-1 GO2Keyword.rdf Keywords.rdf ProbeSet.rdf Keyword GO2OMIM.rdf GO2UniProt.rdf Protein Gene Probe MIM Id OMIM.rdf IntAct.rdf GO.rdf GO2Enzyme.rdf UniProt.rdf Enzyme Organism Citation Compound Taxonomy.rdf Enzymes.rdf PubMed.xml KEGG.rdf Pathway Courtesy Joanne Luciano Semantic Data Integration for Humanities

  25. Libraries of Ontologies (II) OWL ontologies Protégé ontology library http://protege.stanford.edu/download/ontologies.html OWL ontology library http://www.daml.org/ontologies/ SWOOGLE http://swoogle.umbc.edu/ Oyster http://oyster.ontoware.org/oyster/oyster.html Other ontologies SHOE ontology library http://www.cs.umd.edu/projects/plus/SHOE/onts/index.html Ontolingua ontology library http://ontolingua.stanford.edu/ WebOnto ontology library http://webonto.open.ac.uk WebODE ontology library http://webode.dia.fi.upm.es/ (KA)2 ontology library http://ka2portal.aifb.uni-karlsruhe.de/ Semantic Data Integration for Humanities

  26. Ontology development process (I) Semantic Data Integration for Humanities

  27. Ontology development process (II) Semantic Data Integration for Humanities

  28. Ontology development process (III) Semantic Data Integration for Humanities

  29. Ontological Engineering Applications Build Ontologies Methodologies and methods Tools Reasoners Languages Semantic Data Integration for Humanities

  30. Contents • Introduction to ontologies • Definitions • Ontology development • Ontologies and the Semantic Web • Ontologies for humanities • TGN, ULAN, HASSET, CULTOS, VICODI • Projects using ontologies for humanities • Historillo • Cultural tour (Residencia de Estudiantes) • Semantic data/information integration approaches • Open issues and discussion Semantic Data Integration for Humanities

  31. What is the Semantic Web? “The Semantic Web is an extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. It is based on the idea of having data on the Web defined and linked such that it can be used for more effective discovery, automation, integration, and reuse across various applications.” Hendler, J., Berners-Lee, T., and Miller, E. Integrating Applications on the Semantic Web, 2002, http://www.w3.org/2002/07/swint.html Ontologies Annotation Semantic Data Integration for Humanities

  32. Semantic Web Languages URI, HTML, HTTP WWW Semantic Web RDF, RDFS, OWL Dynamic Static Semantic richness Semantic Data Integration for Humanities

  33. Resource Description Framework [instanceOf] urn:data1 SwissProt_seq [similar_sequence_to] [input] urn:hit1… [performsTask] [instanceOf] urn:BlastNInvocation3 urn:hit2…. [contains] [output] Find similar sequence urn:hit50….. urn:data2 Sequence_hit urn:data12 [input] [hasHits] [instanceOf] urn:compareinvocation3 Blast_report [directlyDerivedFrom] [distantlyDerivedFrom] [instanceOf] [output] urn:hit5… urn:data:3 urn:hit8…. [contains] Data generated by services/workflows [output] urn:hit10….. [output] urn:data:f1 urn:invocation5 [ ] Properties [type] [hasName] urn:data:f2 Concepts [type] [hasName] Services Missed sequence DatumCollection New sequence LSDatum literals Semantic Data Integration for Humanities

  34. The 3 faces of the Semantic Web SWRL Expressive models Inference Model fusion OWL Controlled vocabularies RDF(S) Data fusion Integration Integration Extensible metadata schemas that you don’t have to nail down RDF Annotation XML • Semantic Annotation Web • Semantic Data (Integration) Web • Semantic Knowledge (Reasoning) Web Semantic Data Integration for Humanities

  35. Annotation assert facts using terms (metadata in RDF) Represent terms and their relationships (ontology in RDFS/OWL) News Videocast Grant Application Research Events Organisation Gene Database Semantic Data Integration for Humanities

  36. Ontologies and Metadata Instance of Instance of Ontologies Belongs_To Organization Person xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#' xmlns:NS0='http://www.esperonto.net/semanticportal/RDFS/Person_Ontology#' xmlns:NS1='http://www.esperonto.net/semanticportal/RDFS/Organization_Ontology#' Has_contact_Person Subclass of Subclass of Associate Prof. Partner <rdf:Description rdf:about='AsunciónGómez-Pérez'> <rdf:type rdf:resource=‘Associate Prof'/> <NS0:Full_Name>A. GomezPerez</NS0:Full_Name> <NS0:Belongs_To>UPM</NS0: Belongs_To > <NS0:e-mail>asun@fi.upm.es</NS0:e-mail> <rdf:Description rdf:about='UPM'> <rdf:type rdf:resource='Partner'/> <NS1:Acronym>UPM</NS1:Acronym> <NS1:Has_Contact_Person>Asunción Gómez-Pérez </NS1:Has_Contact_Person > Annotation (RDF) Web Page URL http://www.esperonto.net http://www.esperonto.net Semantic Data Integration for Humanities

  37. Metadata annotation Ontology-based document annotation: trends and open research problems. Corcho, O. International Journal of Metadata, Semantics and Ontologies 1(1):47-57. 2006 • Different types of annotation depending on the type of vocabulary used Based on Dublin Core The contributor and creator is the flight booking service “www.flightbookings.com”. The date would be January 1st, 2003, in case that the HTML page has been generated on that specific date. The description would be something like “flight details for a travel between Madrid and Seattle via Chicago on February 8th, 2004”. The document format is “HTML”. The document language is “en”, which stands for English Based on thesauri Madrid is a reference to the term with ID 7010413 in the thesaurus, which refers to the city of Madrid in Spain. Spain is a reference to the term with ID 1000095, which refers to the kingdom of Spain in Europe. Chicago is a reference to the term with ID 7013596, which refers to the city of Chicago in Illinois, US. United States of America is a reference to the term “United States” with ID 7012149, which refers to the US nation. Seattle is a reference to the term with ID 7014494, which refers to the city of Seattle in Washington, US. Based on ontologies Concept instances relate a part of the document to one or several concepts in an ontology. For example, “Flight details” may represent an instance of the concept Flight, and can be named as AA7615_Feb08_2003, although concept instances do not necessarily have a name. Attribute values relate a concept instance with part of the document, which is the value of one of its attributes. For example, “American Airlines” can be the value of the attribute companyName. Relation instances that relate two concept instances by some domain-specific relation. For example, the flight AA7615_Feb08_2003 and the location Madrid can be connected by the relation departurePlace Semantic Data Integration for Humanities

  38. Integration use a uniform common model in RDF Connecting through shared terms and shared instances Preserving context and provenance Agents Smart portals Data mining Social networking Smart search Knowledge Discovery Information Integration and aggregation D2R R2O BIRN Mediator OBSERVER CARNOT TSIMMIS Semantic Data Integration for Humanities

  39. Data Integration in the Semantic Web • Flexible and extensible self describing schemas that don’t have to be nailed down • “Lets describe my data set, or the output format of my tool, that changes frequently” • Open world • “I need to comment on that historial document” • “That fact is now incorrect because …” • Data fusion across different data models • cross linked by shared instances and shared concepts • Global naming scheme • LSID: Life Science Identifiers • URIs: Uniform Resource Identifiers Semantic Data Integration for Humanities

  40. Inference Logic-based classification, validity checking with OWL Rules using SWRL (Semantic Web Rule Language) RDF queries Just making connections because so much stuff is connected! Rearrangement of a DNA sequence homologous to a cell-virus junction fragment in several Moloney murine leukemia virus-induced rat thymomas 8q24 PVT1 Semantic Data Integration for Humanities James Hendler Science and the Semantic Web Science 299: 520-521, 2003

  41. Conclusion. An Ontology should be just the Beginning Databases Declare structure Ontologies Knowledge bases The Semantic Web Provide domain description Software agents Problem-solving methods Domain-independent applications Semantic Data Integration for Humanities Source: Alan Rector

  42. Main References Gómez-Pérez, A.; Fernández-López, M.; Corcho, O. Ontological Engineering. Springer Verlag. 2003 http://www.ontoweb.org http://knowledgeweb.semanticweb.org • Deliverables • D1.1 • D1.2 • D1.3 • D1.4 • D1.5 Research deliverables Industry deliverables Neches, R.; Fikes, R.; Finin, T.; Gruber, T.; Patil, R.; Senator, T.; Swartout, W.R. Enabling Technology for Knowledge Sharing. AI Magazine. Winter 1991. 36-56. Gruber, T. A translation Approach to portable ontology specifications. Knowledge Acquisition. Vol. 5. 1993. 199-220. Uschold, M.; Grüninger, M. ONTOLOGIES: Principles, Methods and Applications. Knowledge Engineering Review. Vol. 11; N. 2; June 1996. Semantic Data Integration for Humanities

  43. Acknowledgements • Asunción Gómez-Pérez and Mariano Fernández-López • Most of the slides have been done jointly with them • Carole Goble, Pinar Alper • Ontologies and the Semantic Web Semantic Data Integration for Humanities

  44. Contents • Introduction to ontologies • Definitions • Ontology development • Ontologies and the Semantic Web • Ontologies for humanities • TGN, ULAN, HASSET, CULTOS, VICODI • Projects using ontologies for humanities • Historillo • Cultural tour (Residencia de Estudiantes) • Semantic data/information integration approaches • Open issues and discussion Semantic Data Integration for Humanities

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