SEDE:
An Ontology for Scholarly Event
         Description
                 Senator Jeong
               senator@snu.ac.kr...
Publications

Senator Jeong. Toward Scholarly Event Digital Library Services. Bulletin of IEEE Technical
Committee on Digi...
Table of Contents
1   Introduction & Background


2   Generic Event Model


3   The SEDE Model & Implementation


4   Appl...
INTRODUCTION & BACKGROUND
Scholarly Events

• Conferences, Workshops, Seminars, Symposia
• A sequentially and spatially organized collection of scho...
Scholarly Events


Publish up-to-date scientific research results,

Get feedback from scientific communities

Exchange res...
Information Needs wrt Scholarly
              Events
Information need of a simple magnitude

• Event Name, Topics
• Event ...
Information Needs wrt Scholarly
              Events
Scientifically meaningful inference
• prominent scientists
• prominen...
Research Goal
Satisfy scientists’ basic information needs
• by collecting, archiving and providing access to scholarly eve...
Previous Work
• EventSeer, PapersInvited, Conference Alerts
  – focus on calls for papers
  – simple metadata about forthc...
GENERIC EVENT MODEL

provide enough descriptive power and granularity to
span over multiple scientific disciplines and cap...
Generic Event Model
          Event≡ (∃Agent) ∧ (∃Action) ∧ (∃Entity) ∧ (∃Place) ∧ (∃Time)

                              ...
The classes of the generic event
             model
THE SEDE MODEL &
IMPLEMENTATION
Ontology modelling principle
Scholarly event description structure
Key concepts in the SED...
Scholarly Event Description Structure
     Scholarly Event
                                       Session
      Track     ...
foaf:Agent


                           foaf:Person                   playedBy               foaf:Group

                 ...
RDFS/OWL




           18
http://eventography.org/sede
http://eventography.org/sede
UML representation of Scholarly
           Event.




                                  21
The reified relationship btw. Committee
   and Agent via CommitteeRole




                                      22
APPLICATION USE CASE SCENARIOS
Semantic        Event           Knowledge   Domain KOS      Academic
      Search &       Coupling         Structure   Gen...
Semantic        Event           Knowledge   Domain KOS      Academic
      Search &       Coupling         Structure   Gen...
Ontology-based Information
        Extraction




                             26
Ontology-based Information
             Extraction
• The limitations of fully automatic information
  extraction technique...
Method: Rule based Pattern Matching
                                                       Start                          ...
Method: Tag Cassification
                                                      Tag




Punctuations    Literal         Da...
Method: Tag Cassification
                                                                          Tag




       Punctua...
Realms: Example
There were few surprises about the submission of the paper            TEXT_CHUNK
It will take place at the...
Implementation: Workbench




                            32
Implementation: Export to RDF KB




                               33
Semantic        Event           Knowledge   Domain KOS      Academic
      Search &       Coupling         Structure   Gen...
Semantic S&R on Scholarly
                 Events(1)
• Finding events with a specific call-for-paper topic, a
  submission...
Semantic S&R on Scholarly
              Events(2)
• Retrieving artifacts from an atom event:
• A user missed an invited ta...
Data Repositories
                                Bibliographic Repositories




                                         ...
Semantic S&R on Scholarly
                 Events(2)
SELECT ?Topic ?Presenter ?Video_Clip ?Event ?Session
WHERE {
?x a sed...
Semantic S&R on Scholarly
                 Events(3)
• Finding domain experts

SELECT DISTINCT ?Domain ?Expert ?Affiliatio...
Semantic        Event           Knowledge   Domain KOS      Academic
      Search &       Coupling         Structure   Gen...
Coupling of Events and Scientists
                       sim ( Ei , E j ) =
                                             ∑...
Semantic        Event           Knowledge   Domain KOS      Academic
      Search &       Coupling         Structure   Gen...
Domain Knowledge Structure
                 Analysis




(data mining and its usage context in Bioinformatics, cosine ≥0.1...
*Co-word Analysis: Assumption

            Topic C
 article

            Topic A
 article              These two
         ...
*Co-word Analysis
         t1 t2 t3 t4                          Event                   Papers from
d1           1 0 1 0  ...
*Tool: BiKE Text Analyzer (BTA)
•   Java Application
•   Vocabulary Manager
•   Synonym Manager
•   Stopword Manager
•   S...
*Tool: BTA: Identify variables




              47
*Tool: BTA: SNA data file




            48
Semantic        Event           Knowledge   Domain KOS      Academic
      Search &       Coupling         Structure   Gen...
Generation of Domain KOS
<skos:Concept rdf:ID="BiomedicalInformaticsAndComputation">
  <skos:prefLabel>Biomedical informat...
Semantic        Event           Knowledge   Domain KOS      Academic
      Search &       Coupling         Structure   Gen...
Academic Performance Evaluation




                              52
Scholar’s Prominence Evaluation

Definition (1)
                                                    # of Elite Group
 Prom...
Scholarly Event’s Prominence
     Evaluation Metrics




                               54
Scholarly Event’s Prominence
            Evaluation Metrics
Definition (2)
                                               ...
Event Series’ Prominence Evaluation




                                      56
Event Series’ Prominence Evaluation

Definition (3)
                                                             Event
   ...
ONTOLOGY EVALUATION
Ontology Evaluation
Ontology Evaluation
Competency Question                      SEDE                                                   SWC

D...
DISCUSSION & CONCLUSION
Discussion & Conclusion
• The SEDE ontology provides a backbone to represent,
  collect, share and allow inference from sc...
SEDE:
An Ontology for Scholarly Event
         Description
                  Senator Jeong
                senator@snu.ac....
SEDE:  An Ontology For Scholarly Event Description
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I present the design and implementation of an ontology for scholarly event description (SEDE) to provide a backbone to represent, collect, share and allow inference from scholarly event information

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SEDE: An Ontology For Scholarly Event Description

  1. 1. SEDE: An Ontology for Scholarly Event Description Senator Jeong senator@snu.ac.kr Biomedical Knowledge Engineering Lab., Seoul National University
  2. 2. Publications Senator Jeong. Toward Scholarly Event Digital Library Services. Bulletin of IEEE Technical Committee on Digital Libraries. 2008 Fall 2008;4(2). Senator Jeong, Hong-Gee Kim. “SEDE: An Ontology for Scholarly Event Description“. Journal of Information Science. [in press] DOI: 10.1177/0165551509358487. Senator Jeong, Sungin Lee, Hong-Gee Kim. “Are You an Invited Speaker?: A Bibliometric Analysis of Elite Groups for Scholarly Events in Bioinformatics“. Journal of the American Society for Information Science and Technology. 2009;60(6). pp.1118-1131. Senator Jeong, Hong-Gee Kim. “Intellectual Structure of Biomedical Informatics reflected in Scholarly Events“. Scientometrics. [in press]. 2
  3. 3. Table of Contents 1 Introduction & Background 2 Generic Event Model 3 The SEDE Model & Implementation 4 Application Use Case Scenarios 5 Ontology Evaluation 6 Discussion & Conclusion
  4. 4. INTRODUCTION & BACKGROUND
  5. 5. Scholarly Events • Conferences, Workshops, Seminars, Symposia • A sequentially and spatially organized collection of scholars’ interactions • with the intention of • Delivering and Sharing knowledge, • Exchanging Research Ideas, and • Performing related activities. 5
  6. 6. Scholarly Events Publish up-to-date scientific research results, Get feedback from scientific communities Exchange research interests and ideas with each other Demonstrate current research trends
  7. 7. Information Needs wrt Scholarly Events Information need of a simple magnitude • Event Name, Topics • Event Date, Venue, Organizer • Due dates for Calls for Paper A scientist does not gets a full and exhaustive picture of scholarly events held in the world • Due to the sheer volume of events held by various academic societies and organizations • no single information channel has been successful at keeping track of ever-growing conferences and providing their information to scientists
  8. 8. Information Needs wrt Scholarly Events Scientifically meaningful inference • prominent scientists • prominent events • best scientists suited for consultations and collaboration might be met partially at a minimal level • since almost all event websites list leadership members such as • general chairs, committee members, invited speakers and/or award winners • Users are not able to get the whole picture • existing library services do not provide this kind of meaningful information in an integrated and collective manner
  9. 9. Research Goal Satisfy scientists’ basic information needs • by collecting, archiving and providing access to scholarly event information. Satisfy users’ in-depth information needs • by excavating scholarly meaningful information through reasoning about knowledge To define a description base for scholarly events • to enable software agents to crawl and extract event data, and • to facilitate the unified access to, and reason about, the collected data
  10. 10. Previous Work • EventSeer, PapersInvited, Conference Alerts – focus on calls for papers – simple metadata about forthcoming events – proprietary description formats • Semantic Web Conference ontology – best only for the ESWC conference • Event Driven Model – ABC ontology, INDECS, OntologX, FRBR, CIDOC- CRM, Enterprise Architecture, Event Ontology
  11. 11. GENERIC EVENT MODEL provide enough descriptive power and granularity to span over multiple scientific disciplines and capture as many varied event types as possible
  12. 12. Generic Event Model Event≡ (∃Agent) ∧ (∃Action) ∧ (∃Entity) ∧ (∃Place) ∧ (∃Time) Presentation Event Event Agent (Who) “John Smith” Agent Action (How) “Present” Action Entity (What) “Biomedical Modeling” Entity Time (When) “2008-11-08” Time Place (Where) “Washington, DC” Place (∃Agent(John.Smith)) ∧ (∃Action(present)) ∧ (∃Entity(Biomedical Modelling)) ∧ (∃Place(Washington)) ∧ (∃Time(2008–11–08)). 12
  13. 13. The classes of the generic event model
  14. 14. THE SEDE MODEL & IMPLEMENTATION Ontology modelling principle Scholarly event description structure Key concepts in the SEDE ontology n-ary relations and reification heuristics Ontology improvement
  15. 15. Scholarly Event Description Structure Scholarly Event Session Track Track Atom Session Session Event Atom Atom … Event Event Atom … … Event Atom Atom Event Event Scholarly … Event … … Session Session Atom Atom Event Event … … … Scholarly Atom Atom Event Event Event 15
  16. 16. foaf:Agent foaf:Person playedBy foaf:Group Role Event Series hasSessionChair hasPresenter CommitteeRole isMemberEventOf hasCommitteeRole Session hasSession Committee hasAtomEvent hasSession startDate Track AtomEvent hasCommittee Time hasTrack endDate hasTopic hasTopic Event hasChildEvent hasArtifact skos:Concept geo:SpatialThing Artifact hasTopic heldAt VideoClip hasCall Place Country Call hasProgram City foaf:Document skos:inScheme Program hasTheme Venue hasProceedings Paper Presentation skos:ConceptScheme Proceedings 16
  17. 17. RDFS/OWL 18
  18. 18. http://eventography.org/sede
  19. 19. http://eventography.org/sede
  20. 20. UML representation of Scholarly Event. 21
  21. 21. The reified relationship btw. Committee and Agent via CommitteeRole 22
  22. 22. APPLICATION USE CASE SCENARIOS
  23. 23. Semantic Event Knowledge Domain KOS Academic Search & Coupling Structure Generation Prominence Retrieval Analysis Evaluation ….. APIs Knowledge SEDE Base Ontology Event Data Ontology Extractor Editor Event Data Crawler Crawled Data Web 24
  24. 24. Semantic Event Knowledge Domain KOS Academic Search & Coupling Structure Generation Prominence Retrieval Analysis Evaluation ….. APIs Knowledge SEDE Base Ontology Event Data Ontology Extractor Editor Event Data Crawler Crawled Data Web 25
  25. 25. Ontology-based Information Extraction 26
  26. 26. Ontology-based Information Extraction • The limitations of fully automatic information extraction techniques • The heterogeneous nature of event web pages • Strategy – to make use of a more simple approach of data extraction, – utilizes manually defined patterns of text content and HTML formatting based on general conventions for listing data in human-readable formats on the web. 27
  27. 27. Method: Rule based Pattern Matching Start Tag form: /aBCD • a: Tag Category Tag • BCD: Tag description HTML Document Array Opening HTML Tags: • tr, p, div à newlines List of rules for identifying similar patterns of tags • td à Tab HTML Parser (Grammar Parser) • li à bullet Chainer Parse HTML Closing HTML tags: • p, table, li, h1-5, br à newlines String + Text string chain index +chain Type • Tokenize text • pre-tag Text Tokenizer • Separate punctuation marks Holds a hierarchy of realms Tokenize Realmer Each realm correspond to a different chain in the document (/n, “”, ,, !, (), :,;, .) text • append EOF tag • split text by spaces • return array of tokens Realm Data Token Array Extender • Directory class call Modify Directory Add Realm ‘createTagIndex’ function Realm Data • Match Tags using REG keyword matches and Assign Tags gazetter lookup Data Lookup match Exporter Lookup Extraction Rule Extracted Regular Data Expression Gazetteer Keyword End 28
  28. 28. Method: Tag Cassification Tag Punctuations Literal Data & Numbers Grammar related Name-Related Keywords Additional /pCOM /lEML /iYEA /gOF /nTTL /kUNI /xCAP Category Tag Meaning Grammer /gART [article ex. the|this|its|...] Category /gOF of /gFOR for /gON on /gAT at /gIN in /gABT about /gFRM from /gTO To | through | until /gCNJ [conjunction = and | or | &] 29
  29. 29. Method: Tag Cassification Tag Punctuations Literal Data & Numbers Grammar related Name-Related Keywords Additional /pCOM /lEML /iYEA /gOF /nTTL /kUNI /xCAP Tag Meaning Example /UNI university universtiy|college|academy|Universitat... /CTR center center|centre|institute|department|division /ORG organization society|association|council|consortium /EVT event conference|conf|symposium|meeting|congress|roundtable|colloquium|seminar|summit|convention|forum|program /QUA qualifier annual|biannual|biennial|interdisciplinary|special|joint|asian|european|international|metropolitan|national|polytechnic|glob al|graduate|limited|ltd(.)?|incorporated|inc(.)?|int(.)|applied) /SBJ subject (Aeronautics|aerospace|Agriculture|applications|Astronomy|Biology|Biotechnology|Biochemistry|bioinformatics|business |Chemistry|Cryptology|Ecology|economics|Electronics|Energy|Engineering|Environment|Forensics|Geography|health|info rmatics|information|Mathematics|Mechanical|medicine|Meteorology|Nanotechnology|Oceanography|Paleontology|Physic s|Policy|Psychology|Research|science(s)?|security|securities|solution(s)?|Space|systems|technology|Vibrations|Wireless)" /OTH other (webpage- "(Main|Media|Home|you|of|(Us)|((?i)(tutorial|proceeding(s?)|download|PDF|PostScript|HTML|MSWord|LaTex|Format|A related) SCII|collocated|copyright|see|contact)))
  30. 30. Realms: Example There were few surprises about the submission of the paper TEXT_CHUNK It will take place at the University of Technology, Brahms, Canada. SUBMISSION_MARKER UNIVERSITY_NAME COUNTRY Submission due date: September 5th, 2009 DEADLINE_CONTAINER SUBMISSION_MARKER DATE Notification date: November 6th, 2009 DEADLINE_CONTAINER NOTIFICATION_MARKER DATE Program Committee: COMMITTEE_MARKER Dolldrum Flannery, University of Texas, USA AFFILIATION_GROUP NAME UNIVERSITY_NAME COUNTRY HTML Text Realms
  31. 31. Implementation: Workbench 32
  32. 32. Implementation: Export to RDF KB 33
  33. 33. Semantic Event Knowledge Domain KOS Academic Search & Coupling Structure Generation Prominence Retrieval Analysis Evaluation ….. APIs Knowledge SEDE Base Ontology Event Data Ontology Extractor Editor Event Data Crawler Crawled Data Web 34
  34. 34. Semantic S&R on Scholarly Events(1) • Finding events with a specific call-for-paper topic, a submission deadline, and an event start date SELECT DISTINCT ?Topic ?Event ?Deadline ?Event_Start WHERE { ?x a sede:Event; rdfs:label ?Event. ?x sede:hasCall ?y.?y rdfs:label ?Call. ?y sede:hasTopic ?z. ?z skos:prefLabel ?Topic. ?y sede:submissionDeadline ?Deadline. ?x sede:startDate ?Event_Start. FILTER ( (regex(?Topic, "data mining")||regex(?Topic, "Data mining") )|| (regex(?Topic, "Ontolog*")||regex(?Topic, "ontolog*") ) ) }ORDER BY ?Topic 35
  35. 35. Semantic S&R on Scholarly Events(2) • Retrieving artifacts from an atom event: • A user missed an invited talk session on the topic of “semantic search” at the ESWC2008 Conference. So, the user searches for invited talk session covering that topic to come up with its video clip URI. 36
  36. 36. Data Repositories Bibliographic Repositories Video Clip Repositories Presentation Repositories Artifacts Presentation Paper VersionOf Presentation VideoClip hasArtifact hasArtifact hasArtifact AtomEvent SPARQL Query Track hasAtomEvent hasAuthor hasTrack hasPresenter End User hasSession RDF Endpoint: hasTopic foaf:Person http://eventography.org/query/ Session Event skos:Concept 37
  37. 37. Semantic S&R on Scholarly Events(2) SELECT ?Topic ?Presenter ?Video_Clip ?Event ?Session WHERE { ?x a sede:Event. ?x skos:altLabel ?Event. ?x sede:hasSession ?y. ?y rdfs:label ?Session. ?y sede:hasAtomEvent ?z.?z sede:hasPresenter ?p. ?p foaf:name ?Presenter.?z rdfs:label ?AtomEvent. ?z sede:hasArtifact ?c. ?c dc:identifier ?Video_Clip. ?z sede:hasTopic ?t. ?t skos:prefLabel ?Topic. FILTER ((regex(?Event, "ESWC*"))&& ((regex(?Session, "Invited Talk")||regex(?Session, "invited talk")))&& ((regex(?Topic, "Semantic Search")||regex(?Topic, "semantic search"))) )} 38
  38. 38. Semantic S&R on Scholarly Events(3) • Finding domain experts SELECT DISTINCT ?Domain ?Expert ?Affiliation WHERE{ ?x a sede:Session. ?x sede:hasTopic ?topic. ?topic skos:prefLabel ?Domain. ?x sede:hasSessionChair ?chair. ?chair foaf:name ?Expert. FILTER (regex(?Domain, "Decision")|| regex(?Domain, "decision”)) OPTIONAL{?chair sede:hasAffiliation ?y. ?y foaf:name ?Affiliation.} }ORDER BY ?Domain 39
  39. 39. Semantic Event Knowledge Domain KOS Academic Search & Coupling Structure Generation Prominence Retrieval Analysis Evaluation ….. APIs Knowledge SEDE Base Ontology Event Data Ontology Extractor Editor Event Data Crawler Crawled Data Web 40
  40. 40. Coupling of Events and Scientists sim ( Ei , E j ) = ∑w w t ,i t, j ∑w ∑w 2 t ,i 2 t, j 41
  41. 41. Semantic Event Knowledge Domain KOS Academic Search & Coupling Structure Generation Prominence Retrieval Analysis Evaluation ….. APIs Knowledge SEDE Base Ontology Event Data Ontology Extractor Editor Event Data Crawler Crawled Data Web 42
  42. 42. Domain Knowledge Structure Analysis (data mining and its usage context in Bioinformatics, cosine ≥0.1; k-nn 2; n=69) 43
  43. 43. *Co-word Analysis: Assumption Topic C article Topic A article These two topics are likely to be related Topic B article …… …… 44
  44. 44. *Co-word Analysis t1 t2 t3 t4 Event Papers from d1 1 0 1 0 Topics Events d2 0 1 1 0 d3 0 1 1 1 t1 t2 t3 t1 0 1 3 t2 5 0 2 fi , j N t3 Wi , j = × IDF = TF × log ∑ 1 2 0 k nk , j ni n n t i i ∑x y ∑x y i i = = 1= 1 i i Cosine( x, y ) = n n n n t t i ∑ xi2 = 1= 1 i ∑ yi2 = 1 i (∑ xi2 ) × (∑ yi2 ) = 1 i t t t t t SNA.dat file t t t 45 t
  45. 45. *Tool: BiKE Text Analyzer (BTA) • Java Application • Vocabulary Manager • Synonym Manager • Stopword Manager • Stemming Manager 46
  46. 46. *Tool: BTA: Identify variables 47
  47. 47. *Tool: BTA: SNA data file 48
  48. 48. Semantic Event Knowledge Domain KOS Academic Search & Coupling Structure Generation Prominence Retrieval Analysis Evaluation ….. APIs Knowledge SEDE Base Ontology Event Data Ontology Extractor Editor Event Data Crawler Crawled Data Web 49
  49. 49. Generation of Domain KOS <skos:Concept rdf:ID="BiomedicalInformaticsAndComputation"> <skos:prefLabel>Biomedical informatics and computation</skos:prefLabel> <skos:Concept rdf:ID="Semantic_Web"> <skos:inScheme rdf:resource="#BIBE2007Themes"/> <skos:narrower rdf:resource="#Bio-molecularAndPhylogeneticDatabases"/> <skos:prefLabel>Semantic Web</skos:prefLabel> <skos:narrower rdf:resource="#DataVisualization"/> <skos:inScheme rdf:resource="#ICSD2009CfPTopics"/> <skos:narrower rdf:resource="#Interoperability"/> <skos:topConceptOf rdf:resource="#ICSD2009CfPTopics"/> <skos:narrower rdf:resource="#BiomedicalImaging"/> …………….. <skos:narrower rdf:resource="#DrugDiscoveryGeneExpressionAnalysis"/> <skos:narrower rdf:resource="#Knowledge_Organization_and_Ontologies"/> <skos:narrower rdf:resource="#MolecularEvolutionAndPhylogeny"/> <skos:narrower rdf:resource="#Bio-Ontology"/> </skos:Concept> <skos:narrower rdf:resource="#BioinformaticsEngineering"/> <skos:narrower rdf:resource="#ProteinStructurePredictionAndMolecularSimulation"/> <skos:narrower rdf:resource="#SystemBiology"/> skos:related <skos:narrower rdf:resource="#SignalingAndComputationBiomedicalDataEngineering"/> <skos:narrower rdf:resource="#ModelingAndSimulation"/> <skos:narrower rdf:resource="#QueryLanguages"/> owl:sameAs <skos:Concept rdf:ID="ComputingLearningOrBehaviour"> <skos:narrower rdf:resource="#SequenceSearchAndAlignment"/> <skos:prefLabel>Computing learning or behaviour</skos:prefLabel> <skos:narrower rdf:resource="#Proteomics"/> <skos:topConceptOf rdf:resource="#BSBT2009Theme"/> <skos:narrower rdf:resource="#Telemedicine"/> <skos:narrower rdf:resource="#FunctionalGenomics"/> <skos:inScheme rdf:resource="#BSBT2009Theme"/> <skos:narrower rdf:resource="#IdentificationAndClassificationOfGenes"/> <rdfs:label>Computing learning or behaviour</rdfs:label> <skos:narrower rdf:resource="#Biolanguages"/> <skos:narrower rdf:resource="#Ontologies"/> </skos:Concept> <skos:narrower rdf:resource="#MathematicalBiology"/> <skos:narrower rdf:resource="#ModellingLearningInLivingSystems"/> <skos:Concept rdf:ID="Bio-Ontologies"> skos:broader <skos:narrower rdf:resource="#TeachingHumanoidRobots"/> <skos:prefLabel>Bio-Ontologies</skos:prefLabel> </skos:Concept> <skos:inScheme rdf:resource="#Bio-OntologiesBioLink2006Topics"/> <skos:narrower rdf:resource="#Current_Research_In_Ontology_Languages_and_its_implication_for_Bio-Ontologies"/> <skos:narrower rdf:resource="#Biological_Applications_of_Ontologies"/> <skos:narrower rdf:resource="#Reports_on_Newly_Developed_or_Existing_Bio-Ontologies"/> <skos:narrower rdf:resource="#Tools_for_Developing_Ontologies"/> <skos:narrower rdf:resource="#Use_of_Semantic_Web_technologies_in_Bioinformatics"/> <skos:narrower rdf:resource="#The_implications_of_Bio-Ontologies_or_the_Semantic_Web_for_the_drug_discovery_process"/> </skos:Concept> 50
  50. 50. Semantic Event Knowledge Domain KOS Academic Search & Coupling Structure Generation Prominence Retrieval Analysis Evaluation ….. APIs Knowledge SEDE Base Ontology Event Data Ontology Extractor Editor Event Data Crawler Crawled Data Web 51
  51. 51. Academic Performance Evaluation 52
  52. 52. Scholar’s Prominence Evaluation Definition (1) # of Elite Group Prominence Weight Membership of Scholar S Field ∑ n t =1 ( wt kt | f ) P( S ) = τ t∈T nf Normalizer Elite Group # of Events in a Type Specific Field 53
  53. 53. Scholarly Event’s Prominence Evaluation Metrics 54
  54. 54. Scholarly Event’s Prominence Evaluation Metrics Definition (2) Scholar’s Event’s Prominence(Def. 1) Prominence ∑ n s =1 P( S ) P( E ) = τ s∈S cf # of Elite Group Member for an Event belong to a Specific Field 55
  55. 55. Event Series’ Prominence Evaluation 56
  56. 56. Event Series’ Prominence Evaluation Definition (3) Event Event Series Prominence(Def. 2) Prominence ∑ g∈G n g =1 P( E ) P(ε ) = τ zf # of event instances (e.g.,AMIA2009)belonging to Event Series (AMIA)in a given subject field (Medical Informatics) 57
  57. 57. ONTOLOGY EVALUATION
  58. 58. Ontology Evaluation
  59. 59. Ontology Evaluation Competency Question SEDE SWC Does it have a Yes. It uses SKOS to describe No. It uses SWRC’s research topic which has container for topics? topics. a limited number of topics. Does it have a Yes. It has the Committee class No. container for committees? Does it identify Yes. It defines a generic class Role No. It enumerates Chair, Delegate, Presenter, various roles in a identifiable with a label. Program Committee Member, resulting in no committee? mechanisms to identify variant names such as co-chair, vice-chair, founder, etc. Does it support the Yes. It is more flexible than SWC, Arguable. The WorkshopEvent, TutorialEvent, representation of an in that it furnishes the class from the ConferenceEvent, and PanelEvent should be event’s structure in a top level (Event) down to the leaf deprecated, since they can be described with flexible way? level classes (AtomEvent). the top level class, such as AcademicEvent, TrackEvent and SessionEvent. Does it have a Yes, it has the Call class No. The Call class was deprecated, and it uses container for Call? the CfP ontology. CfP Vocabulary Specification, http://sw.deri.org/2005/08/conf/cfp.html 60 [1]
  60. 60. DISCUSSION & CONCLUSION
  61. 61. Discussion & Conclusion • The SEDE ontology provides a backbone to represent, collect, share and allow inference from scholarly event information in a logical way • Basic information needs – semantic search and retrieval using the facts stored in the KB • Scientifically meaningful information needs – unearth hidden knowledge for the academic community • SEDE – helps to improve information accessibility through greater semantic interoperability of information. – makes it possible to build a scholarly semantic web • isolated pieces of scholarly event data integrated through relationships with other scientific data on the web thus creating added information.
  62. 62. SEDE: An Ontology for Scholarly Event Description Senator Jeong senator@snu.ac.kr Biomedical Knowledge Engineering Lab., Seoul National University

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