all rights reserved, copyright © fujitsu laboratories ltd. 20031 an approach to know-who using rdf...
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LTD. 2003 1
An approach to KNOW-WHO using RDF
Nobuyuki Igata, Hiroshi Tsuda,Isamu Watanabe and Kunio
MatsuiFujitsu Laboratories Ltd.
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LTD. 2003 2
What is KNOW-WHO Function of Knowledge Management.
Ex) Looking for experts with the specific skill.
How to collect, represent and maintain personal knowledge (so-called “Profile”)?
Previous Approaches Manual Profile Registration.
High maintenance costs. Attribute-Value pairs.
Too simple to represent complex personal knowledge.
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LTD. 2003 3
Our Approach How to collect and maintain profiles
1. Automatic Profile Extraction from Related Resources.
How to represent profiles2. Graph Expression by using RDF.
How to retrieve profiles3. Combination of Structured full-text Search and
Text Mining Visualization.
MeetingDocument(E-mail, Paper,…)
Services Persons
PlaceContents
SearchEngine
Group
An example ofRelated Resourcesof humans routine work
What information does he output?(= personal skill)
What information does he input?
(= personal interest)
With whom does he work?
(= personal connection network)
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Automatic Profile Extraction
WorkWare++ A Web-based groupwar
e. Relation of information
of some applications semantically in the Metadata Layer.
Automatic Creation of metadata.
EmployeeDatabase Office
DocumentsE-Mail Scheduler
RDF
Employeeobjects RDF
Documentobjects
RDF
Meetingobjects
RelationshipNetwork
WorkWare++WorkWare++
ScheduleView
User View
DocumentView
Text MiningVisualizer
ApplicationLayer
MetadataLayer
Multiple View Layer
RDF
Scheduleobjects
XMLSearch Engine
relevance & closenessCalculator
OntologyMatching
Architecture of WorkWare++
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Graph Expression (using RDF)
scheduleOwner Title
2003/3/11
DateScheduleIgata
SW-WG
Owner Title
2003/3/11
Date
Employee Meeting
Document
N.Igata
Nobuyuki Igata
Igata
Document Proc Lab.
Author
NameE-mail
Organization
Study of SW
Tsuda Participant
OntologyMatching
Title
2003/3/11
Date
2003/3/10Date Report of
RDFTitle
Employee object Meeting object
OntologyMatching
Semantic Web RDF XML
Keyword
Relationshipby Manual
Document objectSchedule object
Integratethe same meetingfrom different personal schedules
scheduleOwner Title
2003/3/11
DateScheduleIgata
Study of SW
Owner Title
2003/3/11
Date
OntologyMatching
Semi-automatic Connections of some objects. Large-Scale Network Structure.
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Search and Visualization
Implementation: a combination of the Structured Full-text Search Engine and the Text Mining Visualizer of the RDF data.
Know-Who searching procedure in WorkWare++ with the following steps.
M1. Find target technologies from topic keywords. (Technical Term Map).
M2. Find skilled groups of the target technology. (Personal Connection Map).
M3. Find the most skilled people in the group. (Personal Skill Map).
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Technical Term Map
To find target technologies from topic keywords.
Visualizing technical terms, organizations, and their relations, that relate to a starting topic keyword.
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How to make Technical Term Map
1. Search Document objects with a topic keyword.
2. Select Employee objects with the connection link from Document’s Author to Employee’s Name.
3. Get an organization name from Employee.
4. Calculate relevance of each terms by co-occurrence in Document.
Employee
Nobuyuki Igata
Document Lab.
NameE-mail
Organization
Employee object
Document
N.Igata Author
2003/3/10Date Report of
RDF, SWTitle
Semantic Web RDF XML
Keyword
Document object
①
②
③
④
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Personal Connection Map
To find skilled groups of the target technology
Visualizing human-network with the closeness of people.
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EmployeeHiroshi Tsuda
Document Lab.Name
Organization
Employee object
How to make Personal Connection Map
Meeting
Igata
Study of SW
Tsuda
Participant
Title
2003/3/11
Date
Meeting object
Document
N.Igata Author
2003/3/10Date Report of
RDF, SWTitle
Semantic Web RDF XML
Keyword
Document object
EmployeeNobuyuki Igata
Document Lab.Name
Organization
1. Search Document objects with keywords.
2. Select Meeting objects with the connection link from Document to Meeting.
3. Calculate Closeness of people by the co-participant relations of Meeting objects.
①
②
③
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Personal Skill Map
To find the most skilled person in the group.
Visualizing personal skill keywords in the time series.
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Document
N.Igata Author
2003/3/10Date TREC
Title
Semantic Web RDF XML
Keyword③④
How to make Personal Skill Map
Employee
Nobuyuki Igata
Document Lab.
NameE-mail
Organization
Employee object
Document object
①
②
1. Select the Employee object of a specific person.
2. Select Document objects with the connection link from Employee’s Name to Document’s Author.
3. Calculate relevance of each keywords by co-occurrence in Document objects.
4. Arrange keywords in order of the time series.
Document
N.Igata Author
2003/3/10Date Report of
RDF, SWTitle
Semantic Web RDF XML
Keyword③④
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Conclusion
Advantages: 1. Automatic Profile Extraction
Reduce maintenance costs. 2. Graph Expression (using RDF)
Connect metadata of some applications. Represent complex information.
3. Search and Visualization Handling and Understanding of a huge RDF
network.
Future Works: Application to Fujitsu intranet and
Evaluation.