Scientific Profiling based on Semantic Analysis in Social ... · Semantic Annotate Profiling...

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Scientific Profiling based on Semantic Analysis in Social Networks Laurens De Vocht Supervisors Gonzalo Parra Selver Softic Promotors Erik Duval Martin Ebner April 11, 2011

Transcript of Scientific Profiling based on Semantic Analysis in Social ... · Semantic Annotate Profiling...

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Scientific Profiling based on Semantic Analysis in Social Networks

Laurens De Vocht

SupervisorsGonzalo ParraSelver Softic

PromotorsErik Duval

Martin Ebner

April 11, 2011

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‣Problem Statement‣Solution‣Architecture‣Implementation‣Evaluation‣Conclusion‣Discussion

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Agenda

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Problem Statement

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Who has scientific information relevant for me?

Web users generate a massive unstructured data flow

?

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Solution

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Community approvedontologies: FOAF, SIOC

Annotate Data from Social Networks

Linked Open Data

Scientific Profiling Framework

Applications

Connect People and Resources that share Scientific Affinities

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Architecture

‣Overview‣Grabeeter‣Framework‣API

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Architecture: Overview

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Linked Open Data Cloud

Social Networks

Aggregate

Archived/Cached Data Linked Data

Annotate

Interlink

Scientific InformationAnalyse

Output Format

PublishFramework

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Architecture: Overview

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DBPediaColinda

GeoNamesTwitter

Aggregate

Grabeeter Semantic Profiling NetworkAnnotate

Interlink

Scientific Profiling APIAnalyse

JSONRDF (XML)

Publish

Linked Open Data Cloud

Social Networks

Aggregate

Archived/Cached Data Linked Data

Annotate

Interlink

Scientific InformationAnalyse

Output Format

PublishFramework

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Architecture: Overview

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DBPediaColinda

GeoNamesTwitter

Aggregate

Grabeeter Semantic Profiling NetworkAnnotate

Interlink

Scientific Profiling APIAnalyse

JSONRDF (XML)

Publish

Linked Open Data Cloud

Social Networks

Aggregate

Archived/Cached Data Linked Data

Annotate

Interlink

Scientific InformationAnalyse

Output Format

PublishFramework

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Architecture: Grabeeter

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= Twitter aggregation & archiving tool(developed at TUGraz)

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Architecture: Grabeeter

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= Twitter aggregation & archiving tool(developed at TUGraz)

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Architecture: Overview

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DBPediaColinda

GeoNamesTwitter

Aggregate

Grabeeter Semantic Profiling NetworkAnnotate

Interlink

Scientific Profiling APIAnalyse

JSONRDF (XML)

Publish

Linked Open Data Cloud

Social Networks

Aggregate

Archived/Cached Data Linked Data

Annotate

Interlink

Scientific InformationAnalyse

Output Format

PublishFramework

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Architecture: Framework

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Interlinking

Analysis

Extraction

Grabeeter

Applications

Programming Interface

SQL Queries Triplification SPARQL Queries

RDF Store

High Level Queries

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Architecture: API

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‣ User Profile

http://api.semanticprofiling.net/profile.php?user=screen_name

‣ Discover people, events...

http://api.semanticprofiling.net/discovery.php?

find=persons|events|popular_friends|popular_mentions|popular_events

user=screen_name

‣ Register new Twitter user

http://api.semanticprofiling.net/register.php?user=twitter_user

‣ Event Details

http://api.semanticprofiling.net/event.php?name=event_name

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Architecture: API

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Architecture: API

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Implementation‣Hashtags as Identifiers‣not always strong or consistent enough‣properties of good hashtags formalized‣helpful in assessment of valuable identifiers

‣Expert Search/Profiling with Linked Data‣aggregate and analyze certain types of data‣need to surpass limits of closed data sets‣LOD delivers multi-purpose data

ReferencesLaniado, D., Mika. P.: Making sense of twitter. In: The Semantic Web–ISWC 2010 (2010)Stankovic M., Wagner C., Laublet P., Jovanovic J.: Looking for experts? what can linked data do for you. In: Linked Data on the Web.

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Evaluation

‣Approach‣Usability Case Study‣Search Quality‣Results so far

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Evaluation: Approach

‣Test usability & search quality‣Web application: “Scientific Affinity Browser” as case study.‣Development in 3 phases:‣1. User Interface only‣2. Fixed Data Set‣3. Integrated with Framework

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Evaluation: Usability Case Study

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Connecting researchers based on shared scientific events (conferences)

Researchers

Scientific Profiling

Researcher (User)

User Model Event ModelScientific Conferences Resource

Profiler/Analyzer

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Evaluation: Search quality

‣Satisfaction questionnaireUsers rate their search results‣Measure Relevance‣Recall‣Precision

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Evaluation: Results so far

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‣Definitely useful application‣Use of the map view makes sense‣People - Event split confusing‣View of own profile‣not a suitable starting point‣only useful in comparison‣shouldn’t be always visible‣Person-specific affinities‣too much hidden

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Evaluation: Results so far

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Challenges

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‣Rank tags

by importance, not just frequency of use

‣Visualization

of similarities and links between users and entities

‣Detect communities

using a graph-based algorithm on the linked data network

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Schedule

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Iteration 8 feb 21 - mar 2Implement

framework feedback

Iteration 9 mar 3 - mar 16 Literature study 2

Iteration 10 mar 17 - apr 8Evaluation phase 1 & Report/presentation

Iteration 11 apr 9 - apr 28 Evaluation phase 2

Iteration 12 apr 29 - may 15 Evaluation phase 3

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Conclusion

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‣ Framework designed to support a broad range of

social scientific semantic-based applications

‣Realized with current state of the art technologies

‣ Interlinking with Linked Open Data Cloud

‣ Evaluation case study: scientific affinity browser web app

‣ Follow project blog http://blog.semanticprofiling.net

‣All References: see reports