KAnOE: Research Centre for Knowledge Analytics and Ontological Engineering Managing Semantic Data NACLIN-2014, 10 Dec 2014 Dr. Kavi Mahesh Dean of Research, PES University 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 1

Managing Semantic Data in Research Data Services  Trend: Publish research data along with paper  Digital library of research data  How do we manage this data?  E.g., our research requires:  Several Tera Bytes of data  5 billion data elements so far 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 2

Inverting the Publication Model Past:  Description of research results in English  Show samples of data  “Results, Discussion, Conclusion” framework Present:  Publish article and entire dataset  No links between article and data 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 3

The Inverted Publication Model Future:  Inverted model:  Publish self-contained data  Publish data analytics  Annotate the data with English descriptions where needed  Rich linkage between datasets  Web of linked data… 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 4

Illustration of Publishing Data 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 5

1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 6

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Self-Contained Dataset Requirements:  Have a proper and consistent structure;  Define each element both syntactically and semantically;  Specify all the semantic constraints on permissible data values, their types and cardinalities; and  Specify data provenance, etc. 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 8

Ontology of Research Data  In other words, an ontology of research data  Where is the “Dublin Core” of research data?  E.g., CERIF 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 9

Why Semantic Data Management?  Epistemology of science: Verifying research results  Making sense of someone else’s data  Documenting the usage scenario of data 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 10

1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 11

 Ontology-based multi-domain metadata for research data management using triple stores  Full Text: PDF Buy this Article Authors: João Rocha da Silva Universidade do Porto/INESC TEC, Portugal Cristina Ribeiro DEI, Universidade do Porto/INESC TEC, Portugal João Correia Lopes DEI, Universidade do Porto/INESC TEC, PortugalBuy this ArticleJoão Rocha da SilvaUniversidade do Porto/INESC TEC, PortugalCristina RibeiroDEI, Universidade do Porto/INESC TEC, PortugalJoão Correia LopesDEI, Universidade do Porto/INESC TEC, Portugal 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 12

Data on the Web: 5-Star Rating System * Data on the Web: E.g., data published as a set of scanned images ** Machine-Readable Data: E.g., data published as a spreadsheet *** Non-Proprietary Format: E.g., data published as a CSV file **** RDF Data: E.g., a drug database published in RDF ***** Linked RDF Data: Links to other people’s data are included. E.g., the Dbpedia dataset extracted from wikipedia 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 13

Linked Open Data: Principles  Use URIs as names of things: E.g, mention author by URI, not just name.  Use HTTP URIs so that people can look up those names.  When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL).  Include links to other URIs, so people can discover more things. Sir Tim Berners-Lee 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 14

Linked Open Research Data Services Requirements:  Uniquely identify all entities used in datasets such as experiments, specimens, locations, organizations, etc.;  Interlink parts of datasets with precise parts of an article in both directions;  Classify datasets using a suitable universal classification scheme;  Cite other datasets, i.e., refer to them through links;  Manage multiple versions and revisions of datasets; and  Incorporate a suitable controlled vocabulary or ontology. 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 15

Architecture of Digital Library of Data 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 16

An Ontology for Research Data 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 17

Concluding Remarks  Publishing and citing research data will be a common practice  Digital libraries need to manage research data  Data needs to be self-contained, therefore semantic  Linked open data is promising  We need a proper ontology of research data  Keyword search may be good enough for documents, but not for datasets 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 18

Questions?  Thank you! 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 19

1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 20

How?  By applying Natural Language Generation Techniques on structure and semantics of Linked Open Datasets and underlying Ontologies. 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Input Triples SubjectPredicateobject 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Ontology for Discourse Structuring 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Classes 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Subclasses 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Individuals 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Ontology as a Chart 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

A few snapshots of the “ MECHANISM ” ontology, in the protégé software, are shown: 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

The 12 subgroups: 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

The functions accommodated under every subgroup: 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Hierarchy of mechanisms: 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Object & data properties being added to each mechanism: 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 33

Subclasses and their Descriptions 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Object properties 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Data properties added 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Linked Open Data Tools Pallavi Karanth ©KAnOE, PES Institute of Technology 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Data  Data ©KAnOE, PES Institute of Technology 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Web for Data Discovery ©KAnOE, PES Institute of Technology 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Web for Data Discovery ©KAnOE, PES Institute of Technology 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Machine Understandable Data ©KAnOE, PES Institute of Technology 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Machine Understandable Data ©KAnOE, PES Institute of Technology Ram Nickname DOB Location Bangalore 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Open Data and Linked Data  Open Data - open access  Linked Data  Semantic  Machine Readable ©KAnOE, PES Institute of Technology 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Linked Open Data  Five Star Rating of Linked Data by Tim Berners Lee  ★ make your stuff available on the Web (whatever format) under an open license  ★★ make it available as structured data (e.g., Excel instead of image scan of a table)  ★★★ use non-proprietary formats (e.g., CSV instead of Excel)  ★★★★ use URIs to denote things, so that people can point at your stuff  ★★★★★ link your data to other data to provide context  About 50 billion triples as of 2013 ©KAnOE, PES Institute of Technology 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

LOD - IT (Kappa)  For Software Developers  Technical Helpdesk  LOD-IT Video LOD-IT Video  LOD-IT Demo LOD-IT Demo ©KAnOE, PES Institute of Technology 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

LODScape  Ontology based Multiple LOD Object Browser  DbPedia and Freebase datasets used  LODScape Demo LODScape Demo ©KAnOE, PES Institute of Technology 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Semantic Smart-Aleck  Automatic Fact Generator  Based on Interestingness Algorithm  Uses Dbpedia and Yago datasets  SemanticSmartAleck Demo SemanticSmartAleck Demo ©KAnOE, PES Institute of Technology 1/25/ (c) Dr. Kavi Mahesh; Do not copy or distribute

Acknowledgments 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 48

Suggestions?  Thank you! 1/25/2016 (c) Dr. Kavi Mahesh; Do not copy or distribute 49