Interactive Knowledge Capture

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1 Interactive Knowledge Capture Director, Knowledge Technologies Associate Division Director for Research Research Professor, Computer Science Intelligent Systems Division Information Sciences Institute University of Southern California 1

2 Knowledge Technologies at USC/ISI: Major Threads 2

3 Semantic Workflows in Wings ( [Gil et al JETAIʼ11; Gil et al IEEE-ISʼ11; Gil et al e-scienceʼ09; Kim et al JWSʼ08] Unique capability to reason about application tasks and data, has uses in science and intel Semantic descriptions of datasets (RDF, OWL) Metadata properties Semantic constraints How computations transform the data Automatic propagation of constraints Assistance - Parameters - Algorithms Generation Validation Execution in grids or clouds Example: Workflow for Pixel Intensity Quantification of brain imagery [Kumar et al 10; Kurc et al 09] Compact workflow template (left) Automatically generated executable workflow for 2560x2400 pixels (right) 3

4 LinkedDataLens: Extracting Networks of Interest from 25B+ Linked Data Cloud [Groth and Gil ʼ11] A growing large structured source of data that can be exploited in many application areas Web of Data: 25B RDF triples (statements) with 395M links from 203 data sets [Berners-Lee et al 09] Community-created through extraction from web sources News sources, events, geospatial information, bioinformatics, academic LinkedDataLens: a system to extract networks of interest Framework accessible over the web, no need to install any software Workflows extract RDF triples through queries, create network, and use social network analysis algorithms to extract interesting statistics Size, centrality, connected components, etc. Extracted networks can be integrated with other existing networks and used by other applications Networks about people, places, events, etc Eg, Pharmaceutical firms doing clinical trials in California for same drug Pharmaceutical companies who make the same drug Nodes=609 Edges=3032 density=0.016 AvgClusterCoeff= isconnected=false NbConnectedComp=1 7 US Semators who share Alma Mater Nodes=136 Edges=340 density=0.037 AvgClusterCoeff= isconnected=false NConnectedComp=17 Our group contributed BibBase Honorary Mention Triplification Challenge

5 Social Knowledge Collection for Communities of Interest ( [Vrandecic et al 2010; Vrandecic et al 2011] Unique capability for social collection of structured content, with documented provenance Why: Social content collection tools are either lightly structured or too rigid Wikis and collaborative tools provide community repositories, but are not structured or aggregated in a searchable manner Use of pre-defined schema/ontology that community fills out with contributions Semantic wiki is a framework that enables contributors to define organic characterizations that lead to emerging unified models Users define vocabulary/ontology Voluntarily adopt definitions by others Formal queries can retrieve structured content Can use proactive normalization techniques to encourage consensus where possible Provenance-aware semantic wiki Alternative views can be accommodated Eg, Android is jailbreakable and bricked by RF software Structured provenance records Document sources and evidence Can filter query results according to provenance Eg,Software that bricks phones according to NIST 3 rd Place Semantic Web Challenge 2010 Communitycreated structured content Emerging semantics lead to dynamically aggregated content 5

6 Unifying Provenance Models for Trusted Systems [Groth et al ʼ11; Sahoo et al ʼ11; Moreau et al ʼ10] Unifying provenance models would enable the computation of trust metrics for content Why: Provenance is rarely captured, hampering trust assessment When captured, it is diverse in its nature and underlying implementation - Document-based: where information was found/extracted (e.g., NYT) - Attribution-based: who created the information - Process-based: how information was derived from documents or datasets Unifying models of provenance Can assess trust in content (existing trust models are attribution-based only) Builds on open standards Semantic Web standards (OWL, RDF) Open Provenance Model Dublin Core Unifying provenance infrastructure Access provenance records across heterogeneous systems to assess trust Integrate data taking provenance records into account Emerging standards (Open Provenance Model) Mappings across existing provenance vocabularies Chaired W3C Provenance Group ( ): Mappings across 10 popular provenance vocabularies Use cases and requirements Charter for a Working Group with 17 core concepts 6

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