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0 EarthCube Building Blocks: OceanLink Leveraging Semantics and Linked Data for Geoscience Data Sharing and Discovery

EarthCube's OceanLink - Project Overview and Presentation Updates (March 2014)

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EAGER: Collaborative Research: EarthCube Building Blocks, Leveraging Semantics and Linked Data for Geoscience Data Sharing and Discovery or "OceanLink" is one of 15 EarthCube-funded components. This presentation includes an OceanLink Project Overview (slides 1-12), followed by several presentations highlighting separate project efforts and updates to different audiences: Slide 13: "Ontologies in a data-driven world." Montana State University Computer Science Department, March 3, 2014. Slide 44: "Towards ontology patterns for ocean science repository integration", Ontology Summit 2014, Ontolog online session January 2014. Slide 82: OceanLink: Using Patterns for Discovery in EarthCube, GeoVoCampSB2014, Santa Barbara, March, 2014 Slide 118: "Ontologies in a data driven world," IBM T.J. Watson Research Center, January 2014.

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Page 1: EarthCube's OceanLink - Project Overview and Presentation Updates (March 2014)

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EarthCube Building Blocks:

OceanLink

Leveraging Semantics and Linked Data for

Geoscience Data Sharing and Discovery

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Overview

Goal

•  Enable discovery of geoscience data and knowledge, and ultimately, integration

Strategy

•  Publish content from existing network of repositories as Linked Open Data (LOD)

•  Enable horizontal semantic integration

•  Provide tools + services useful to working scientists

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Domain

Ocean Science

•  Research vessels collect data from the solid earth, water column, atmosphere

•  Many repositories already interoperate

•  Approach is extensible to other geo domains

U.S. academic oceanographic research fleet (above), and recent expedition tracks (left)

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Project Team Lamont-Doherty Earth Observatory

Robert Arko Suzanne Carbotte

Marymount University Tom Narock (lead)

University of Maryland, Baltimore County Tim Finin

Woods Hole Oceanographic Institution Cynthia Chandler Lisa Raymond Adam Shepherd Peter Wiebe

Wright State University Pascal Hitzler Michelle Cheatham Adila Krisnadhi

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Collections

•  Biological & Chemical Oceanography Data Management Office (BCO-DMO)

•  Rolling Deck to Repository (R2R) •  cruise catalog +underway enviro sensor data

•  Marine Biological Laboratory / Woods Hole Oceanographic

Institution (MBLWHOI) Library •  published articles, theses, tech reports, datasets

•  AGU meeting abstracts

•  NSF funding award abstracts

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ODPs

Ontology Design Patterns

•  Core set of conceptual primitives from Ocean Science Vessel Cruise Instrument Dataset Person Organization etc.

•  Reuse existing standard vocabularies where they exist (DCAT, FOAF, PROV)

•  Maximize reusability, minimize commitment

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ODPs

•  Patterns published as OWL files with embedded axioms and local vocabularies eg. Cruise must have a Vessel Cruise may have a Person in the Role of Chief Scientist

•  Leverage existing alignment among repositories that use eg. NERC Vocabulary Server

•  Inference to find relationships among cruises, datasets, people, publications, etc.

(cont.)

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Work Plan 1.  Model, align, inference over existing LOD collections

(BCO-DMO + R2R) •  Develop use cases eg. "find publications related to

cruises at the Bermuda Rise that produced CTD profiles and/or seafloor mapping data”

•  Develop ODPs •  Map existing collections to ODPs

2.  Publish LOD for other collections (Library, AGU, NSF) and map to ODPs

3.  Prototype end-user tools and services •  Search/browse across federated LOD collections •  Edit ontologies •  Annotate LOD resources incl. provenance

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Initial Results “An Ontology Pattern for Oceanographic Cruises” (Krisnadhi et al.)

Technical Report and draft set of ODPs Reuses existing patterns including

•  Semantic Trajectory (Janowicz et al.) •  Information Object •  Simple Event Model

to model a Cruise and ship’s track

R/V Atlantis cruise AT22 (Scotian Shelf Survey, August 2012) Basemap: GMRT

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Lessons

•  Recurrent themes in EarthCube Workshop Reports eg. •  Data are still difficult to discover and access •  Data attribution and citation are critical •  Reuse of data still hampered by need for implicit

understanding

•  Collaboration between Geo Science and Computer Science works best with Use Cases

•  In-person working meetings are key to initial progress Oct. 2013 Woods Hole

Nov. 2013 Baltimore Jan. 2014 Washington

(probably more)

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Acknowledgements

“EAGER: Collaborative Research: EarthCube Building Blocks, Leveraging Semantics and Linked Data for Geoscience Data Sharing and Discovery” NSF Funding Awards: ICER 13-54990 LDEO ICER 13-54693 UMBC ICER 13-54778 WSU ICER 13-54107 WHOI September 15, 2013 - August 31, 2014

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Thank you.

www.oceanlink.org

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March 2014 – Montana State University – Pascal Hitzler

Metadata Semantics: What Semantic Web technologies can contribute to scientific

data and information sharing and discovery

Pascal Hitzler DaSe Lab for Data Semantics

Wright State University http://www.pascal-hitzler.de

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Distributing scientific information

• Since the rise of the World Wide Web, the role of publishing houses and scientific libraries is changing.

• Scientific publishing houses are redefining their roles and are investigating new revenue models.

• What exactly is the role of libraries? • What will the role of libraries be in, say, 20 years?

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Library discovery issues

• “I’m looking for an easy but introductory text on discrete mathematics suitable for computer scientists, with high quality in the mathematical formalization and notation, and including (besides the usual stuff) at least brief treatments of Russel’s paradox and of countable versus uncountable sets, e.g. uncountability of the real numbers.”

• “I’m looking for a textbook for a second-year introductory class on logic for computer scientists. Formal treatment of mathematics, tableaux algorithms for propositional and predicate logic, and preferably some coverage of datalog.”

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Semantic Web journal

• EiCs: Pascal Hitzler Krzysztof Janowicz

• Established 2010. Going strong.

• We very much welcome contributions at the “rim” of traditional Semantic Web research – e.g., work which is strongly inspired by a different field.

• Non-standard (open & transparent) review process.

• http://www.semantic-web-journal.net/

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Semantic Web journal

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Summary Statistics

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Citation Maps

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Collaboration Networks

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Topic Trends

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Publications analysis

• Provide analysis of citations, topic trends, research networks, etc., which can be obtained from (suitable!) metadata.

• Establish the social, economical and computational infrastructure to provide such data: open access, legal reusability of text and data, rich metadata (citations and beyond)

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Data Discovery

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Scenario

Determine if a GMRT grid contains high-resolution data from a ship’s multibeam sonar in the proximity of a specified physiographic feature. Return the list of ship expeditions that contributed high-resolution data to those grid cells. For these expeditions, determine which, if any, are found in the R2R catalog and contain quality-controlled geophysical (gravity/ magnetics) profiles along the same ship track. Further determine which investigators are linked to those expeditions; which expeditions and investigators are linked to journal publications and/or meeting abstracts that contain thematic keywords pertaining to the physiographic feature; and what other data are available from the same expeditions in other repositories such as BCO-DMO.

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“Inside” and beyond the publications

• Make paper contents available through rich metadata.

• Combine papers with data and datasets, and with information from “outside” the publishing process proper, such as funding awards, geographical information, affiliations, etc.

• More importantly, help in providing a social, economical and technological infrastructure where such information is provided to scientists and students.

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EarthCube

EarthCube: Developing a Community-Driven Data and Knowledge Environment for the Geosciences “concepts and approaches to create integrated data management infrastructures across the Geosciences.” “EarthCube aims to create a well-connected and facile environment to share data and knowledge in an open, transparent, and inclusive manner, thus accelerating our ability to understand and predict the Earth system.”

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OceanLink

• An EarthCube Building Block

• Integrating ocean science respositories BCO-DMO and R2R, as well as datasets from the WHOI Library, AGU abstracts, NSF projects.

• Demonstrable added value (faceted integrated search).

• Key: extensible architecture that has the potential to grow to EarthCube size

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Integration approach

• Well-established: – using controlled vocabularies – which are standardized through a social process

• How many vocabularies do you need to – answer circumstantial queries? – cover all scientific paper contents? – even just to cover the earth sciences?

• What do you do if scientific notions or perspectives change?

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E.g., “Event”

Event

xsd:dateTime xsd:string

occursAtPlace occursAtTime

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Better Event (more general)

Event

<TemporalThing> <SpatialThing>

occursAtPlace occursAtTime

But what about events taking place in Second Life?

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Perhaps even …

Event

<TemporalThing> <Place>

occursAtPlace occursAtTime

<Agent>

hasParticipant

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Different representations

person/101396

“Smith, John”

name

R2R:

foaf:Person

type Person_752

name

foaf:Person

type

“John Smith”

familyName

“Smith”

givenName

“John”

BCO-DMO:

What about other countries?

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Semantic Web

• Research field in computer science. • Took off in ca. the year 2000.

• Significant funding, initially DARPA, then large-scale in the EU. • In the meantime, large international effort, with significant

investment by funding agencies and companies.

• The Semantic Web vision is about seamless integration of data, knowledge, and services. It is not restricted to the WWW.

• The Semantic Web approach has (whatever type of) formal knowledge representation as a key ingredient.

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Knowledge Representation

• Vocabularies on steroids. – Complex relationships between notions are part of the formal

and machine-processable vocabulary definitions, e.g. “Every cruise must have a chief scientist who is PI on one of the research awards which pays for the expenses of the cruise.”

• Standardization of languages for defining vocabularies.

E.g., the Web Ontology Language OWL. – Rather than standardizing vocabularies themselves. – Requires establishing best practices for defining and sharing

vocabularies.

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Libraries?

• Libraries could again be at the forefront of being providers for scientific information.

• Trends go towards integrated information spaces with a plethora of differing and heterogeneous information sources.

• How to organize this information space conceptually, technologically, and socially, is a key quest in the Big Data age.

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Thanks!

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OceanLink Collaborators Robert Arko, Columbia University Suzanne Carbotte, Columbia University Cynthia Chandler, Woods Hole Oceanographic Institution Michelle Cheatham, Wright State University Timothy Finin, University of Maryland, Baltimore County Pascal Hitzler, Wright State University Krzysztof Janowicz, University of California, Santa Barbara Adila Krisnadhi, Wright State University Thomas Narock, Marymount University Lisa Raymond, Woods Hole Oceanographic Institution Adam Shepherd, Woods Hole Oceanographic Institution Peter Wiebe, Woods Hole Oceanographic Institution Some of the presented work is part of the NSF OceanLink project: EarthCube Building Blocks, Leveraging Semantics and Linked Data for Geoscience Data Sharing and Discovery

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References

• Pascal Hitzler, Frank van Harmelen, A reasonable Semantic Web. Semantic Web 1 (1-2), 39-44, 2010.

• Prateek Jain, Pascal Hitzler, Peter Z. Yeh, Kunal Verma, Amit P. Sheth, Linked Data is Merely More Data. In: Dan Brickley, Vinay K. Chaudhri, Harry Halpin, Deborah McGuinness: Linked Data Meets Artificial Intelligence. Technical Report SS-10-07, AAAI Press, Menlo Park, California, 2010, pp. 82-86. ISBN 978-1-57735-461-1. Proceedings of LinkedAI at the AAAI Spring Symposium, March 2010.

• Pascal Hitzler, Krzysztof Janowicz, What’s Wrong with Linked Data? http://blog.semantic-web.at/2012/08/09/whats-wrong-with-linked-data/ , August 2012.

• Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph, Foundations of Semantic Web Technologies. Chapman and Hall/CRC Press, 2009.

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References

• Pascal Hitzler, Krzysztof Janowicz, Linked Data, Big Data, and the 4th Paradigm. Semantic Web 4 (3), 2013, 233-235.

• Krzysztof Janowicz, Pascal Hitzler, The Digital Earth as Knowledge Engine. Semantic Web 3 (3), 213-221, 2012.

• Gary Berg-Cross, Isabel Cruz, Mike Dean, Tim Finin, Mark Gahegan, Pascal Hitzler, Hook Hua, Krzysztof Janowicz, Naicong Li, Philip Murphy, Bryce Nordgren, Leo Obrst, Mark Schildhauer, Amit Sheth, Krishna Sinha, Anne Thessen, Nancy Wiegand, Ilya Zaslavsky, Semantics and Ontologies for EarthCube. In: K. Janowicz, C. Kessler, T. Kauppinen, D. Kolas, S. Scheider (eds.), Workshop on GIScience in the Big Data Age, In conjunction with the seventh International Conference on Geographic Information Science 2012 (GIScience 2012), Columbus, Ohio, USA. September 18th, 2012. Proceedings.

• Krzysztof Janowicz, Pascal Hitzler, Thoughts on the Complex Relation Between Linked Data, Semantic Annotations, and Ontologies. In: Paul N. Bennett, Evgeniy Gabrilovich, Jaap Kamps, Jussi Karlgren (eds.), Proceedings of the 6th International Workshop on Exploiting Semantic Annotation in Information Retrieval, ESAIR 2013, ACM, San Francisco, 2013, pp. 41-44.

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References

• Prateek Jain, Pascal Hitzler, Amit P. Sheth, Kunal Verma, Peter Z. Yeh, Ontology Alignment for Linked Open Data. In P. Patel-Schneider, Y. Pan, P. Hitzler, P. Mika, L. Zhang, J. Pan, I. Horrocks, B. Glimm (eds.), The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I. Lecture Notes in Computer Science Vol. 6496. Springer, Berlin, 2010, pp. 402-417.

• Amit Krishna Joshi, Prateek Jain, Pascal Hitzler, Peter Z. Yeh, Kunal Verma, Amit P. Sheth, Mariana Damova, Alignment-based Querying of Linked Open Data. In: Meersman, R.; Panetto, H.; Dillon, T.; Rinderle-Ma, S.; Dadam, P.; Zhou, X.; Pearson, S.; Ferscha, A.; Bergamaschi, S.; Cruz, I.F. (eds.), On the Move to Meaningful Internet Systems: OTM 2012, Confederated International Conferences: CoopIS, DOA-SVI, and ODBASE 2012, Rome, Italy, September 10-14, 2012, Proceedings, Part II. Lecture Notes in Computer Science Vol. 7566, Springer, Heidelberg, 2012, pp. 807-824.

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References

• Yingjie Hu, Krzysztof Janowicz, David Carral, Simon Scheider, Werner Kuhn, Gary Berg-Cross, Pascal Hitzler, Mike Dean, Dave Kolas, A Geo-Ontology Design Pattern for Semantic Trajectories. In: Thora Tenbrink, John G. Stell, Antony Galton, Zena Wood (Eds.): Spatial Information Theory - 11th International Conference, COSIT 2013, Scarborough, UK, September 2-6, 2013. Proceedings. Lecture Notes in Computer Science Vol. 8116, Springer, 2013, pp. 438-456.

• Yingjie Hu, Krzysztof Janowicz, Grant McKenzie, Kunal Sengupta, Pascal Hitzler, A Linked Data-driven Semantically-enabled Journal Portal for Scientometrics. In: H. Alani, L. Kagal, A. Fokoue, P. Groth, C. Biemann, J.X. Parreira, L. Aroyo, N. Noy, C. Welty, K. Janowicz (eds.), The Semantic Web - ISWC 2013. 12th International Semantic Web Conference, Sydney, NSW, Australia, October 21-25, 2013, Proceedings, Part II. Lecture Notes in Computer Science Vol. 8219, Springer, Heidelberg, 2013, pp. 114-129.

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References

• Prateek Jain, Peter Z. Yeh, Kunal Verma, Reymonrod G. Vasquez, Mariana Damova, Pascal Hitzler, Amit P. Sheth, Contextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton. In: Grigoris Antoniou, Marko Grobelnik, Elena Paslaru Bontas Simperl, Bijan Parsia, Dimitris Plexousakis, Pieter De Leenheer, Jeff Pan (Eds.): The Semantic Web: Research and Applications - 8th Extended Semantic Web Conference, ESWC 2011, Heraklion, Crete, Greece, May 29-June 2, 2011, Proceedings, Part I. Lecture Notes in Computer Science 6643, Springer, 2011, pp. 80-92.

• Prateek Jain, Pascal Hitzler, Kunal Verma, Peter Yeh, Amit Sheth, Moving beyond sameAs with PLATO: Partonomy detection for Linked Data. In: Ethan V. Munson, Markus Strohmaier (Eds.): 23rd ACM Conference on Hypertext and Social Media, HT '12, Milwaukee, WI, USA, June 25-28, 2012. ACM, 2012, pp. 33-42.

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References

• Sebastian Rudolph, Markus Krötzsch, Pascal Hitzler, Cheap Boolean Role Constructors for Description Logics. In: Steffen Hölldobler and Carsten Lutz and Heinrich Wansing (eds.), Proceedings of 11th European Conference on Logics in Artificial Intelligence (JELIA), volume 5293 of LNAI, pp. 362-374. Springer, September 2008.

• Adila Alfa Krisnadhi, Frederick Maier, Pascal Hitzler, OWL and Rules. In: A. Polleres, C. d'Amato, M. Arenas, S. Handschuh, P. Kroner, S. Ossowski, P.F. Patel-Schneider (eds.), Reasoning Web. Semantic Technologies for the Web of Data. 7th International Summer School 2011, Galway, Ireland, August 23-27, 2011, Tutorial Lectures. Lecture Notes in Computer Science Vol. 6848, Springer, Heidelberg, 2011, pp. 382-415.

• Adila Krisnadhi, Robert Arko, Suzanne Carbotte, Cynchia Chandler, Michelle Cheatham, Timothy Finin, Pascal Hitzler, Krzysztof Janowicz, Thomas Narock, Lisa Raymond, Adam Shepherd, Peter Wiebe, An Ontology Pattern for Oceanograhic Cruises: Towards an Oceanograhper's Dream of Integrated Knowledge Discovery. OceanLink Technical Report 2014.1.

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Towards ontology patterns for ocean science repository integration

Pascal Hitzler DaSe Lab for Data Semantics

Wright State University http://www.pascal-hitzler.de

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Collaborators Robert Arko, Columbia University Suzanne Carbotte, Columbia University Cynthia Chandler, Woods Hole Oceanographic Institution Michelle Cheatham, Wright State University Timothy Finin, University of Maryland, Baltimore County Pascal Hitzler, Wright State University Krzysztof Janowicz, University of California, Santa Barbara Adila Krisnadhi, Wright State University Thomas Narock, Marymount University Lisa Raymond, Woods Hole Oceanographic Institution Adam Shepherd, Woods Hole Oceanographic Institution Peter Wiebe, Woods Hole Oceanographic Institution The presented work is part of the NSF OceanLink project: EarthCube Building Blocks, Leveraging Semantics and Linked Data for Geoscience Data Sharing and Discovery

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OceanLink and EarthCube

EarthCube: Developing a Community-Driven Data and Knowledge Environment for the Geosciences “concepts and approaches to create integrated data management infrastructures across the Geosciences.” “EarthCube aims to create a well-connected and facile environment to share data and knowledge in an open, transparent, and inclusive manner, thus accelerating our ability to understand and predict the Earth system.”

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OceanLink

Bottom-up constructed project. Currently first phase: • Integrating ocean science respositories BCO-DMO and R2R, as

well as datasets from the WHOI Library, AGU abstracts, NSF projects.

• Demonstrable added value (faceted integrated search).

• Key: extensible architecture that has the potential to grow to EarthCube size

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Logic

Many axioms / strong theory

Few axioms / weak theory

Few models Many inferences Many models Few inferences

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Ontologies

Strong / many ontological commitments

Weak / few ontological commitments

Few models Many inferences Not very reusable Many models Few inferences More easily reusable

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Ontology Design Patterns

Strong / many ontological commitments

Weak / few ontological commitments

Few models Many inferences Not very reusable Many models Few inferences More easily reusable

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Ontology Design Patterns

“An ontology design pattern is a reusable successful solution to a recurrent modeling problem.” So-called content patterns usually encode specific abstract notions, such as process, event, agent, etc.

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E.g., “Event”

Event

xsd:dateTime xsd:string

occursAtPlace occursAtTime

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Better Event (more general)

Event

<TemporalThing> <SpatialThing>

occursAtPlace occursAtTime

This is a pattern!

But what about events taking place in Second Life?

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Perhaps even …

Event

<TemporalThing> <Place>

occursAtPlace occursAtTime

<Agent>

hasParticipant

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Event

<Place>

occursAtPlace

Event

xsd:string

occursAtPlace

Shortcuts / views

xsd:string

hasName

There are several things wrong here!

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Event

<Place>

a:occursAtPlace

Event

xsd:string

b:occursAtPlace

Shortcuts / views

xsd:string

a:hasName

Better, but …

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Event

<Place>

a:occursAtPlace

Event

xsd:string

b:occursAtPlace

Shortcuts / views

xsd:string

a:hasName

The latter is not in OWL!

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Event

<Place>

a:occursAtPlace

Shortcuts / views

xsd:string

a:hasName

The latter is not in OWL!

b:occursAtPlace

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Similar problem

Splitting a role: hasParent

hasFather

hasMother

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Cruise

For us: ocean science cruise. A cruise is a type of event. But what kind of place does it occur at?

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Cruise

Cruise

<TemporalThing> <Place>

occursAtPlace occursAtTime

<Agent>

hasParticipant

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Semantic Trajectories

[Hu, Janowicz, Carral, Scheider, Kuhn, Berg-Cross, Hitzler, Dean, COSIT2013]

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Semantic Trajectories

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Semantics in OWL

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Semantics in OWL

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Ocean Science Cruise (draft)

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Cruise trajectory (draft)

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Cruise trajectory

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Cruise trajectory

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Cruise trajectory

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Cruise trajectory

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Why ODPs?

Traditionally, ODPs are thought of as building blocks for ontology modeling. This idea is certainly valid in the context of special purpose ontology-based systems. However, it can be argued that ODPs can be much more than mere building blocks.

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Horizontal alignment

“Horizontal” alignment via patterns

Pattern1 Pattern1

Pattern2 Pattern2

Pattern2

Pattern3

Pattern3

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OceanLink setup

OceanLink Patterns

R2R BCO-DMO MBLWHOI Library

AGU NSF

UI Views

User Interface

mappings

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Other added values of patterns

• Pattern-driven GUIs • Pattern-driven mapping tools • Pattern-driven query rewriting • Pattern-driven reasoning modularization • …

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OceanLink setup

EarthCube Patterns

repository repository repository repository repository

UI Views

User Interface

mappings

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Thanks!

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References

• BCO-DMO: Biological & Chemical Oceanography Data Management Office, http://www.bco-dmo.org/

• R2R: Rolling Deck to Repository, http://www.rvdata.us • OceanLink website and publications are forthcoming • Yingjie Hu, Krzysztof Janowicz, David Carral, Simon Scheider,

Werner Kuhn, Gary Berg-Cross, Pascal Hitzler, Mike Dean, Dave Kolas, A Geo-Ontology Design Pattern for Semantic Trajectories. In: Thora Tenbrink, John G. Stell, Antony Galton, Zena Wood (Eds.): Spatial Information Theory - 11th International Conference, COSIT 2013, Scarborough, UK, September 2-6, 2013. Proceedings. Lecture Notes in Computer Science Vol. 8116, Springer, 2013, pp. 438-456.

• http://ontologydesignpatterns.org

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General References

• Pascal Hitzler, Frank van Harmelen, A reasonable Semantic Web. Semantic Web 1 (1-2), 39-44, 2010.

• Prateek Jain, Pascal Hitzler, Peter Z. Yeh, Kunal Verma, Amit P. Sheth, Linked Data is Merely More Data. In: Dan Brickley, Vinay K. Chaudhri, Harry Halpin, Deborah McGuinness: Linked Data Meets Artificial Intelligence. Technical Report SS-10-07, AAAI Press, Menlo Park, California, 2010, pp. 82-86. ISBN 978-1-57735-461-1. Proceedings of LinkedAI at the AAAI Spring Symposium, March 2010.

• Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph, Foundations of Semantic Web Technologies. Chapman and Hall/CRC Press, 2009.

• Krzysztof Janowicz, Pascal Hitzler, The Digital Earth as Knowledge Engine. Semantic Web 3 (3), 213-221, 2012.

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General References

• Pascal Hitzler, Krzysztof Janowicz, Linked Data, Big Data, and the 4th Paradigm. Semantic Web 4 (3), 2013, 233-235.

• Gary Berg-Cross, Isabel Cruz, Mike Dean, Tim Finin, Mark Gahegan, Pascal Hitzler, Hook Hua, Krzysztof Janowicz, Naicong Li, Philip Murphy, Bryce Nordgren, Leo Obrst, Mark Schildhauer, Amit Sheth, Krishna Sinha, Anne Thessen, Nancy Wiegand, Ilya Zaslavsky, Semantics and Ontologies for EarthCube. In: K. Janowicz, C. Kessler, T. Kauppinen, D. Kolas, S. Scheider (eds.), Workshop on GIScience in the Big Data Age, In conjunction with the seventh International Conference on Geographic Information Science 2012 (GIScience 2012), Columbus, Ohio, USA. September 18th, 2012. Proceedings.

• Krzysztof Janowicz, Pascal Hitzler, Thoughts on the Complex Relation Between Linked Data, Semantic Annotations, and Ontologies. In: Paul N. Bennett, Evgeniy Gabrilovich, Jaap Kamps, Jussi Karlgren (eds.), Proceedings of the 6th International Workshop on Exploiting Semantic Annotation in Information Retrieval, ESAIR 2013, ACM, San Francisco, 2013, pp. 41-44.

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General References

• Sebastian Rudolph, Markus Krötzsch, Pascal Hitzler, Cheap Boolean Role Constructors for Description Logics. In: Steffen Hölldobler and Carsten Lutz and Heinrich Wansing (eds.), Proceedings of 11th European Conference on Logics in Artificial Intelligence (JELIA), volume 5293 of LNAI, pp. 362-374. Springer, September 2008.

• Adila Alfa Krisnadhi, Frederick Maier, Pascal Hitzler, OWL and Rules. In: A. Polleres, C. d'Amato, M. Arenas, S. Handschuh, P. Kroner, S. Ossowski, P.F. Patel-Schneider (eds.), Reasoning Web. Semantic Technologies for the Web of Data. 7th International Summer School 2011, Galway, Ireland, August 23-27, 2011, Tutorial Lectures. Lecture Notes in Computer Science Vol. 6848, Springer, Heidelberg, 2011, pp. 382-415.

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OceanLink: Using Patterns for Discovery in EarthCube

Pascal Hitzler DaSe Lab for Data Semantics

Wright State University http://www.pascal-hitzler.de

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OceanLink Collaborators Robert Arko, Columbia University Suzanne Carbotte, Columbia University Cynthia Chandler, Woods Hole Oceanographic Institution Michelle Cheatham, Wright State University Timothy Finin, University of Maryland, Baltimore County Pascal Hitzler, Wright State University Krzysztof Janowicz, University of California, Santa Barbara Adila Krisnadhi, Wright State University Thomas Narock, Marymount University Lisa Raymond, Woods Hole Oceanographic Institution Adam Shepherd, Woods Hole Oceanographic Institution Peter Wiebe, Woods Hole Oceanographic Institution The presented work is part of the NSF OceanLink project: EarthCube Building Blocks, Leveraging Semantics and Linked Data for Geoscience Data Sharing and Discovery

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Classical ontology-based integration

Query Upper level ontology

LOD IMDB

Dataset

LOD

Wikipedia Dataset

(DBPedia)

Answer

[ODBASE 2012, JWS 2007]

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Example querying LoD

“Identify congress members, who have voted “No” on pro environmental legislation in the past four years, with high-pollution industry in their congressional districts.”

In principle, all the knowledge is there: • GovTrack • GeoNames • DBPedia • US Census

But even with LoD we cannot answer this query.

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Example querying LoD

“Identify congress members, who have voted “No” on pro environmental legislation in the past four years, with high-pollution industry in their congressional districts.”

Some missing puzzle pieces: • Where is the data?

– GovTrack GeoNames US Census requires intimate knowledge of the LoD data sets

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Example querying LoD

“Identify congress members, who have voted “No” on pro environmental legislation in the past four years, with high-pollution industry in their congressional districts.”

Some missing puzzle pieces: • Where is the data?

(smart federation needed) • Missing background (schema) knowledge.

(enhancements of the LoD cloud) • Crucial info still hidden in texts.

(ontology learning from texts) • Added reasoning capabilities (e.g., spatial).

(new ontology language features)

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Linked Data: Variety

“Nancy Pelosi voted in favor of the Health Care Bill.”

Bills:h3962

H.R. 3962: Affordable Health Care for America

Act

Votes:2009-887/+

people/P000197

Nancy Pelosi On Passage: H R 3962 Affordable Health Care for

America Act

Vote: 2009-887

vote:hasAction

vote:vote

dc:title

vote:hasOption

rdfs:label Aye

dc:title

vote:votedBy

name

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Querying approach

Works very well, but only in some very limited cases. Cannot deal with graph representations of even very minimal complexity.

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Automated federation?

person/101396

“Smith, John”

name

R2R:

foaf:Person

type

Person_752

name

foaf:Person

type

“John Smith”

familyName

“Smith”

givenName

“John”

BCO-DMO:

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Automated federation?

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Ways forward?

How to establish a flexible conceptual architecture using data and ontological modeling?

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Ontology Design Patterns

“An ontology design pattern is a reusable successful solution to a recurrent modeling problem.” So-called content patterns usually encode specific abstract notions, such as process, event, agent, etc. Patterns provide modular, reusable, replaceable, pieces. By agreeing on reuse of generic patterns (but leaving the relationships between the patterns to a specific assembly for a special purpose), we can have reuse while preserving heterogeneity.

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Ontology Design Patterns

• Bottom-up homogenization of data representation.

• Avoidance of strong ontological commitments.

• Avoidance of standardization of specific modeling details.

• Well thought-out patterns can be very strong and versatile, thus serve many needs.

We are currently establishing many geo-patterns in a series of

hands-on workshops, the GeoVoCamps, see http://vocamp.org/

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Ontology Design Patterns

“Horizontal” alignment via patterns

Pattern1 Pattern1

Pattern2 Pattern2

Pattern2

Pattern3

Pattern3

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EarthCube

EarthCube: Developing a Community-Driven Data and Knowledge Environment for the Geosciences “concepts and approaches to create integrated data management infrastructures across the Geosciences.” “EarthCube aims to create a well-connected and facile environment to share data and knowledge in an open, transparent, and inclusive manner, thus accelerating our ability to understand and predict the Earth system.”

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OceanLink

NSF EarthCube project “OceanLink”: • Integration of existing ocean science data repositories.

• For faceted browsing and semantic search.

• To be done in a flexible, extendable, modular way.

• With minimal effort for additional data providers to integrate

their content.

National Science Foundation award 1354778 "EAGER: Collaborative Research: EarthCube Building Blocks, Leveraging Semantics and Linked Data for Geoscience Data Sharing and Discovery."

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OceanLink setup

OceanLink Patterns

R2R BCO-DMO WHOI Library

AGU NSF

additional application-specific modeling

User Interface

mappings

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OceanLink patterns

Some central patterns: • Cruise • Trajectory • Person • Organization • Roles of Agents • Repository Object • Data Set • Document

We’re not starting from zero of course.

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Ocean Science Cruise (draft)

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Cruise trajectory (draft)

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Cruise trajectory

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Cruise trajectory

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Cruise trajectory

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Cruise trajectory

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Ways forward

• Establish a flexible conceptual architecture using data and

ontological modeling. • A principled use of patterns, including

– the development of a theory of patterns and – the provision of a critical amount of central patterns may provide a primary path forward.

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Thanks!

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Semantic Trajectories

[Hu, Janowicz, Carral, Scheider, Kuhn, Berg-Cross, Hitzler, Dean, COSIT2013]

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Semantic Trajectories

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Semantics in OWL

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Semantics in OWL

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References

• Pascal Hitzler, Frank van Harmelen, A reasonable Semantic Web. Semantic Web 1 (1-2), 39-44, 2010.

• Prateek Jain, Pascal Hitzler, Peter Z. Yeh, Kunal Verma, Amit P. Sheth, Linked Data is Merely More Data. In: Dan Brickley, Vinay K. Chaudhri, Harry Halpin, Deborah McGuinness: Linked Data Meets Artificial Intelligence. Technical Report SS-10-07, AAAI Press, Menlo Park, California, 2010, pp. 82-86. ISBN 978-1-57735-461-1. Proceedings of LinkedAI at the AAAI Spring Symposium, March 2010.

• Pascal Hitzler, Krzysztof Janowicz, What’s Wrong with Linked Data? http://blog.semantic-web.at/2012/08/09/whats-wrong-with-linked-data/ , August 2012.

• Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph, Foundations of Semantic Web Technologies. Chapman and Hall/CRC Press, 2009.

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References

• Pascal Hitzler, Krzysztof Janowicz, Linked Data, Big Data, and the 4th Paradigm. Semantic Web 4 (3), 2013, 233-235.

• Krzysztof Janowicz, Pascal Hitzler, The Digital Earth as Knowledge Engine. Semantic Web 3 (3), 213-221, 2012.

• Gary Berg-Cross, Isabel Cruz, Mike Dean, Tim Finin, Mark Gahegan, Pascal Hitzler, Hook Hua, Krzysztof Janowicz, Naicong Li, Philip Murphy, Bryce Nordgren, Leo Obrst, Mark Schildhauer, Amit Sheth, Krishna Sinha, Anne Thessen, Nancy Wiegand, Ilya Zaslavsky, Semantics and Ontologies for EarthCube. In: K. Janowicz, C. Kessler, T. Kauppinen, D. Kolas, S. Scheider (eds.), Workshop on GIScience in the Big Data Age, In conjunction with the seventh International Conference on Geographic Information Science 2012 (GIScience 2012), Columbus, Ohio, USA. September 18th, 2012. Proceedings.

• Krzysztof Janowicz, Pascal Hitzler, Thoughts on the Complex Relation Between Linked Data, Semantic Annotations, and Ontologies. In: Paul N. Bennett, Evgeniy Gabrilovich, Jaap Kamps, Jussi Karlgren (eds.), Proceedings of the 6th International Workshop on Exploiting Semantic Annotation in Information Retrieval, ESAIR 2013, ACM, San Francisco, 2013, pp. 41-44.

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References

• Prateek Jain, Pascal Hitzler, Amit P. Sheth, Kunal Verma, Peter Z. Yeh, Ontology Alignment for Linked Open Data. In P. Patel-Schneider, Y. Pan, P. Hitzler, P. Mika, L. Zhang, J. Pan, I. Horrocks, B. Glimm (eds.), The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I. Lecture Notes in Computer Science Vol. 6496. Springer, Berlin, 2010, pp. 402-417.

• Amit Krishna Joshi, Prateek Jain, Pascal Hitzler, Peter Z. Yeh, Kunal Verma, Amit P. Sheth, Mariana Damova, Alignment-based Querying of Linked Open Data. In: Meersman, R.; Panetto, H.; Dillon, T.; Rinderle-Ma, S.; Dadam, P.; Zhou, X.; Pearson, S.; Ferscha, A.; Bergamaschi, S.; Cruz, I.F. (eds.), On the Move to Meaningful Internet Systems: OTM 2012, Confederated International Conferences: CoopIS, DOA-SVI, and ODBASE 2012, Rome, Italy, September 10-14, 2012, Proceedings, Part II. Lecture Notes in Computer Science Vol. 7566, Springer, Heidelberg, 2012, pp. 807-824.

• Yingjie Hu, Krzysztof Janowicz, David Carral, Simon Scheider, Werner Kuhn, Gary Berg-Cross, Pascal Hitzler, Mike Dean, Dave Kolas, A Geo-Ontology Design Pattern for Semantic Trajectories. In: Thora Tenbrink, John G. Stell, Antony Galton, Zena Wood (Eds.): Spatial Information Theory - 11th International Conference, COSIT 2013, Scarborough, UK, September 2-6, 2013. Proceedings. Lecture Notes in Computer Science Vol. 8116, Springer, 2013, pp. 438-456.

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References

• Prateek Jain, Peter Z. Yeh, Kunal Verma, Reymonrod G. Vasquez, Mariana Damova, Pascal Hitzler, Amit P. Sheth, Contextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton. In: Grigoris Antoniou, Marko Grobelnik, Elena Paslaru Bontas Simperl, Bijan Parsia, Dimitris Plexousakis, Pieter De Leenheer, Jeff Pan (Eds.): The Semantic Web: Research and Applications - 8th Extended Semantic Web Conference, ESWC 2011, Heraklion, Crete, Greece, May 29-June 2, 2011, Proceedings, Part I. Lecture Notes in Computer Science 6643, Springer, 2011, pp. 80-92.

• Prateek Jain, Pascal Hitzler, Kunal Verma, Peter Yeh, Amit Sheth, Moving beyond sameAs with PLATO: Partonomy detection for Linked Data. In: Ethan V. Munson, Markus Strohmaier (Eds.): 23rd ACM Conference on Hypertext and Social Media, HT '12, Milwaukee, WI, USA, June 25-28, 2012. ACM, 2012, pp. 33-42.

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References

• D. Oberle, A. Ankolekar, P. Hitzler, P. Cimiano, M. Sintek, M. Kiesel, B. Mougouie, S. Vembu, S. Baumann, M. Romanelli, P. Buitelaar, R. Engel, D. Sonntag, N. Reithinger, B. Loos, R. Porzel, H.-P. Zorn, V. Micelli, C. Schmidt, M. Weiten, F. Burkhardt, J. Zhou, DOLCE ergo SUMO: On Foundational and Domain Models in the SmartWeb Integrated Ontology (SWIntO). Journal of Web Semantics: Science, Services and Agents on the World Wide Web 5 (3), 2007, 156-174.

• Adila Krisnadhi, Robert Arko, Suzanne Carbotte, Cynchia Chandler, Michelle Cheatham, Timothy Finin, Pascal Hitzler, Krzysztof Janowicz, Thomas Narock, Lisa Raymond, Adam Shepherd, Peter Wiebe, An Ontology Pattern for Oceanograhic Cruises: Towards an Oceanograhper's Dream of Integrated Knowledge Discovery. OceanLink Technical Report 2014.1.

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References

• Sebastian Rudolph, Markus Krötzsch, Pascal Hitzler, Cheap Boolean Role Constructors for Description Logics. In: Steffen Hölldobler and Carsten Lutz and Heinrich Wansing (eds.), Proceedings of 11th European Conference on Logics in Artificial Intelligence (JELIA), volume 5293 of LNAI, pp. 362-374. Springer, September 2008.

• Adila Alfa Krisnadhi, Frederick Maier, Pascal Hitzler, OWL and Rules. In: A. Polleres, C. d'Amato, M. Arenas, S. Handschuh, P. Kroner, S. Ossowski, P.F. Patel-Schneider (eds.), Reasoning Web. Semantic Technologies for the Web of Data. 7th International Summer School 2011, Galway, Ireland, August 23-27, 2011, Tutorial Lectures. Lecture Notes in Computer Science Vol. 6848, Springer, Heidelberg, 2011, pp. 382-415.

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Ontologies in a Data-driven World

Pascal Hitzler DaSe Lab for Data Semantics

Wright State University http://www.pascal-hitzler.de

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Textbook

Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph Foundations of Semantic Web Technologies Chapman & Hall/CRC, 2010 Choice Magazine Outstanding Academic Title 2010 (one out of seven in Information & Computer Science) http://www.semantic-web-book.org

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Textbook – Chinese translation

Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph

语义Web技术基础 Tsinghua University Press (清华大学出版社), 2013. Translators: Yong Yu, Haofeng Wang, Guilin Qi (俞勇,王昊奋,漆桂林)

http://www.semantic-web-book.org

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Semantic Web journal

• EiCs: Pascal Hitzler Krzysztof Janowicz

• New journal with significant uptake.

• We very much welcome contributions at the “rim” of traditional Semantic Web research – e.g., work which is strongly inspired by a different field.

• Non-standard (open & transparent) review process.

• http://www.semantic-web-journal.net/

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Ontologies?

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• ... Agent 1

Thing

Person 2

Ontology description

Agent 2

exchange of symbols

‘‘Duck“

Concept MA1

HA1 HA2 MA2

Symbol

Specific Domain, e.g. Animals

agreement Ontology

Semantics

Person 1

exchange of symbols

agreement

A Basic Idea of the Semantic Web

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Ontology represents

general domain knowledge

Reconciling OWL and Rules Knorr, Hitzler, Maier ECAI 2012

Data e.g. on Websites

e.g. every publication has an author

A Basic Idea of the Semantic Web

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Reconciling OWL and Rules Knorr, Hitzler, Maier ECAI 2012

e.g. every publication has an author

Publication

Event

Title

Author

A Basic Idea of the Semantic Web

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Ontology represents

general domain knowledge

Data e.g. on Websites

e.g. every publication has an author

A Basic Idea of the Semantic Web

Reconciling OWL and Rules Knorr, Hitzler, Maier ECAI 2012

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The ontology hype

• Large, well-thought-out ontologies (foundational/domain/etc). • Networked, interlinked ontologies

• “You just have to get your formal definitions right, and a lot of

the rest will just fall into place.”

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The ontology hype

• Large, well-thought-out ontologies (foundational/domain/etc). • Networked, interlinked ontologies

• “You just have to get your formal definitions right, and a lot of

the rest will just fall into place.” – This does not even work for

• scientists • wanting to share and reuse scientific data • through well-kept data repositories

– So how is this supposed to work for the web at large?

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Multiple perspectives

• Try to find a universal definition for – Forest – Mountain – City – River

– Etc.

• The stronger our ontological commitments, the more we loose

reusability.

• We need to accept that conceptualizations are often very local, resulting in “micro-ontologies”.

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Multiple perspectives

Two ontologies. Left: transportation domain Right: agriculture domain We cannot simply equate a:Canal and b:Canal !

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The well-done ontologies

• Brittle • Expensive • Sometimes unintuitive • Unwieldy • Single-perspective • Difficult to reuse

• Work in some contexts. • Work if a lot of central control is imposed. • Take a lot of manpower.

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Pre-LOD Semantic Web

• Foundational ontologies • Networked ontologies • Sophisticated ontology languages

Scientific Hypothesis: These will solve your data and information management problems Remember that scientific progress is fundamentally about falsification, not verification

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Linked Data?

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The linked data counter-hype

• “Ontologies don’t work, let’s just link data”

• “Okay, with a little bit of ontologies on top.”

• “The Linked Data Web is the true Semantic Web.”

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Linked Data 2011

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DBpedia: LOTR page

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Information as RDF graph

LOTR hasAuthor Tolkien . Hobbit hasAuthor Tolkien . LOTR hasCharacter Bilbo . Hobbit hasCharacter Bilbo .

LOTR

Hobbit

Tolkien

Bilbo

hasAuthor

hasAuthor

hasCharacter

hasCharacter

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Linked Data: Volume

Number of Datasets 2011-09-19 295 2010-09-22 203 2009-07-14 95 2008-09-18 45 2007-10-08 25 2007-05-01 12

Number of triples (Sept 2011) 31,634,213,770 with 503,998,829 out-links

From http://www4.wiwiss.fu-berlin.de/lodcloud/state/

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Linked Data: Volume Geoindexed Linked Data – courtesy of Krzysztof Janowicz http://stko.geog.ucsb.edu/location_linked_data

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Linked Data: Volume

October 2013: Ca. 25,000,000,000 schema.org references on the web. 15% of all pages now have schema.org markup. That’s just schema.org references …

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Example querying LoD

“Identify congress members, who have voted “No” on pro environmental legislation in the past four years, with high-pollution industry in their congressional districts.”

In principle, all the knowledge is there: • GovTrack • GeoNames • DBPedia • US Census

But even with LoD we cannot answer this query.

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Example querying LoD

“Identify congress members, who have voted “No” on pro environmental legislation in the past four years, with high-pollution industry in their congressional districts.”

Some missing puzzle pieces: • Where is the data?

– GovTrack GeoNames US Census requires intimate knowledge of the LoD data sets

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Example querying LoD

“Identify congress members, who have voted “No” on pro environmental legislation in the past four years, with high-pollution industry in their congressional districts.”

Some missing puzzle pieces: • Where is the data?

(smart federation needed) • Missing background (schema) knowledge.

(enhancements of the LoD cloud) • Crucial info still hidden in texts.

(ontology learning from texts) • Added reasoning capabilities (e.g., spatial).

(new ontology language features)

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Linked Data: Variety

“Nancy Pelosi voted in favor of the Health Care Bill.”

Bills:h3962

H.R. 3962: Affordable Health Care for America

Act

Votes:2009-887/+

people/P000197

Nancy Pelosi On Passage: H R 3962 Affordable Health Care for

America Act

Vote: 2009-887

vote:hasAction

vote:vote

dc:title

vote:hasOption

rdfs:label Aye

dc:title

vote:votedBy

name

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Linked Data federated querying

Query Upper level ontology

LOD IMDB

Dataset

LOD

Wikipedia Dataset

(DBPedia)

Answer

Joshi, Jain, Hitzler et al. ODBASE 2012

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Bootstrapping-based alignment

Jain, Hitzler et al, ISWC2010

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Linked Data federated querying

Query Upper level ontology

LOD IMDB

Dataset

LOD

Wikipedia Dataset

(DBPedia)

Answer

Joshi, Jain, Hitzler et al. ODBASE 2012

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ALOQUS Illustration

“Identify films, the nations where they were shot and the population of these countries” SELECT ?film ?nation ?pop WHERE { ?film protonu:ofCountry ?nation. ?film rdf:type protonu:Movie. ?film rdfs:label ?film_name. ?nation protont:populationCount ?pop. }

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Querying approach

Works very well, but only in some very limited cases. Cannot deal with graph representations of even very minimal complexity.

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Automated federation?

person/101396

“Smith, John”

name

R2R:

foaf:Person

type

Person_752

name

foaf:Person

type

“John Smith”

familyName

“Smith”

givenName

“John”

BCO-DMO:

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Automated federation?

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Automated federation? Copernicus lunar crater located on earth – courtesy of Krzysztof

Janowicz http://stko.geog.ucsb.edu/location_linked_data (missing reference coordinate system)

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The linked data counter-hype

• “Ontologies don’t work, let’s just link data”

• “Okay, with a little bit of ontologies on top.”

• But then we don’t even know how to effectively query over multiple linked datasets (without using a lot of manpower to manually integrate them).

• It seems rather obvious that we need to get ontologies into the picture, but how to do it while avoiding the drawbacks of strong ontological commitments?

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So What Now?

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Ways forward?

How to establish a flexible conceptual architecture using data and ontological modeling?

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Ontology Design Patterns

“An ontology design pattern is a reusable successful solution to a recurrent modeling problem.” So-called content patterns usually encode specific abstract notions, such as process, event, agent, etc.

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Ontology Design Patterns

• Bottom-up homogenization of data representation.

• Avoidance of strong ontological commitments.

• Avoidance of standardization of specific modeling details.

• Well thought-out patterns can be very strong and versatile, thus serve many needs.

We are currently establishing many geo-patterns in a series of

hands-on workshops, the GeoVoCamps, see http://vocamp.org/

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Ontology Design Patterns

“Horizontal” alignment via patterns

Pattern1 Pattern1

Pattern2 Pattern2

Pattern2

Pattern3

Pattern3

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Semantic Trajectories

[Hu, Janowicz, Carral, Scheider, Kuhn, Berg-Cross, Hitzler, Dean, COSIT2013]

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Semantic Trajectories

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Semantics in OWL

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Semantics in OWL

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Helpfulness of patterns

person/101396

“Smith, John”

name

R2R:

foaf:Person

type

Person_752

name

foaf:Person

type

“John Smith”

familyName

“Smith”

givenName

“John”

BCO-DMO: Even minimalistic reuse is helpful:

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Patterns

• Help to focus when modeling (one key notion at a time). • Good ontology modeling implicitly employs the patterns idea

anyway. It’s just that you expose the patterns. • An ontology composed of patterns exposes its internal

conceptual structure (as a composition of formal vocabulary pieces).

• Well-designed patterns are widely reusable and adaptable. • You don’t have to buy a whole ontology when you adopt a few

patterns from it. • You can easily modify a pattern without giving up on a lot of

similarity to the original pattern (which can be leveraged for data integration).

• You can separate the patterns from specific (application-driven) modifications.

• You can separate the patterns from specific axiomatically defined “views”.

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Patterns Example

NSF EarthCube project “OceanLink”: • Integration of existing ocean science data repositories.

• For faceted browsing and semantic search.

• To be done in a flexible, extendable, modular way.

• With minimal effort for additional data providers to integrate

their content.

National Science Foundation award 1354778 "EAGER: Collaborative Research: EarthCube Building Blocks, Leveraging Semantics and Linked Data for Geoscience Data Sharing and Discovery."

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OceanLink and EarthCube

EarthCube: Developing a Community-Driven Data and Knowledge Environment for the Geosciences “concepts and approaches to create integrated data management infrastructures across the Geosciences.” “EarthCube aims to create a well-connected and facile environment to share data and knowledge in an open, transparent, and inclusive manner, thus accelerating our ability to understand and predict the Earth system.”

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OceanLink setup

OceanLink Patterns

R2R BCO-DMO WHOI Library

AGU NSF

UI Views

User Interface

mappings

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OceanLink patterns

Some central patterns: • Cruise • Trajectory • Person • Organization • Roles of Agents • Repository Object • Data Set • Document

We’re not starting from zero of course.

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Ocean Science Cruise (draft)

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Cruise trajectory (draft)

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Cruise trajectory

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Cruise trajectory

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Cruise trajectory

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Cruise trajectory

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Ways forward

• Establish a flexible conceptual architecture using data and

ontological modeling. • A principled use of patterns, including

– the development of a theory of patterns and – the provision of a critical amount of central patterns may provide a primary path forward.

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Thanks!

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References

• Pascal Hitzler, Frank van Harmelen, A reasonable Semantic Web. Semantic Web 1 (1-2), 39-44, 2010.

• Prateek Jain, Pascal Hitzler, Peter Z. Yeh, Kunal Verma, Amit P. Sheth, Linked Data is Merely More Data. In: Dan Brickley, Vinay K. Chaudhri, Harry Halpin, Deborah McGuinness: Linked Data Meets Artificial Intelligence. Technical Report SS-10-07, AAAI Press, Menlo Park, California, 2010, pp. 82-86. ISBN 978-1-57735-461-1. Proceedings of LinkedAI at the AAAI Spring Symposium, March 2010.

• Pascal Hitzler, Krzysztof Janowicz, What’s Wrong with Linked Data? http://blog.semantic-web.at/2012/08/09/whats-wrong-with-linked-data/ , August 2012.

• Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph, Foundations of Semantic Web Technologies. Chapman and Hall/CRC Press, 2009.

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References

• Pascal Hitzler, Krzysztof Janowicz, Linked Data, Big Data, and the 4th Paradigm. Semantic Web 4 (3), 2013, 233-235.

• Krzysztof Janowicz, Pascal Hitzler, The Digital Earth as Knowledge Engine. Semantic Web 3 (3), 213-221, 2012.

• Gary Berg-Cross, Isabel Cruz, Mike Dean, Tim Finin, Mark Gahegan, Pascal Hitzler, Hook Hua, Krzysztof Janowicz, Naicong Li, Philip Murphy, Bryce Nordgren, Leo Obrst, Mark Schildhauer, Amit Sheth, Krishna Sinha, Anne Thessen, Nancy Wiegand, Ilya Zaslavsky, Semantics and Ontologies for EarthCube. In: K. Janowicz, C. Kessler, T. Kauppinen, D. Kolas, S. Scheider (eds.), Workshop on GIScience in the Big Data Age, In conjunction with the seventh International Conference on Geographic Information Science 2012 (GIScience 2012), Columbus, Ohio, USA. September 18th, 2012. Proceedings.

• Krzysztof Janowicz, Pascal Hitzler, Thoughts on the Complex Relation Between Linked Data, Semantic Annotations, and Ontologies. In: Paul N. Bennett, Evgeniy Gabrilovich, Jaap Kamps, Jussi Karlgren (eds.), Proceedings of the 6th International Workshop on Exploiting Semantic Annotation in Information Retrieval, ESAIR 2013, ACM, San Francisco, 2013, pp. 41-44.

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References

• Prateek Jain, Pascal Hitzler, Amit P. Sheth, Kunal Verma, Peter Z. Yeh, Ontology Alignment for Linked Open Data. In P. Patel-Schneider, Y. Pan, P. Hitzler, P. Mika, L. Zhang, J. Pan, I. Horrocks, B. Glimm (eds.), The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I. Lecture Notes in Computer Science Vol. 6496. Springer, Berlin, 2010, pp. 402-417.

• Amit Krishna Joshi, Prateek Jain, Pascal Hitzler, Peter Z. Yeh, Kunal Verma, Amit P. Sheth, Mariana Damova, Alignment-based Querying of Linked Open Data. In: Meersman, R.; Panetto, H.; Dillon, T.; Rinderle-Ma, S.; Dadam, P.; Zhou, X.; Pearson, S.; Ferscha, A.; Bergamaschi, S.; Cruz, I.F. (eds.), On the Move to Meaningful Internet Systems: OTM 2012, Confederated International Conferences: CoopIS, DOA-SVI, and ODBASE 2012, Rome, Italy, September 10-14, 2012, Proceedings, Part II. Lecture Notes in Computer Science Vol. 7566, Springer, Heidelberg, 2012, pp. 807-824.

• Yingjie Hu, Krzysztof Janowicz, David Carral, Simon Scheider, Werner Kuhn, Gary Berg-Cross, Pascal Hitzler, Mike Dean, Dave Kolas, A Geo-Ontology Design Pattern for Semantic Trajectories. In: Thora Tenbrink, John G. Stell, Antony Galton, Zena Wood (Eds.): Spatial Information Theory - 11th International Conference, COSIT 2013, Scarborough, UK, September 2-6, 2013. Proceedings. Lecture Notes in Computer Science Vol. 8116, Springer, 2013, pp. 438-456.

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References

• Prateek Jain, Peter Z. Yeh, Kunal Verma, Reymonrod G. Vasquez, Mariana Damova, Pascal Hitzler, Amit P. Sheth, Contextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton. In: Grigoris Antoniou, Marko Grobelnik, Elena Paslaru Bontas Simperl, Bijan Parsia, Dimitris Plexousakis, Pieter De Leenheer, Jeff Pan (Eds.): The Semantic Web: Research and Applications - 8th Extended Semantic Web Conference, ESWC 2011, Heraklion, Crete, Greece, May 29-June 2, 2011, Proceedings, Part I. Lecture Notes in Computer Science 6643, Springer, 2011, pp. 80-92.

• Prateek Jain, Pascal Hitzler, Kunal Verma, Peter Yeh, Amit Sheth, Moving beyond sameAs with PLATO: Partonomy detection for Linked Data. In: Ethan V. Munson, Markus Strohmaier (Eds.): 23rd ACM Conference on Hypertext and Social Media, HT '12, Milwaukee, WI, USA, June 25-28, 2012. ACM, 2012, pp. 33-42.

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References

• Sebastian Rudolph, Markus Krötzsch, Pascal Hitzler, Cheap Boolean Role Constructors for Description Logics. In: Steffen Hölldobler and Carsten Lutz and Heinrich Wansing (eds.), Proceedings of 11th European Conference on Logics in Artificial Intelligence (JELIA), volume 5293 of LNAI, pp. 362-374. Springer, September 2008.

• Adila Alfa Krisnadhi, Frederick Maier, Pascal Hitzler, OWL and Rules. In: A. Polleres, C. d'Amato, M. Arenas, S. Handschuh, P. Kroner, S. Ossowski, P.F. Patel-Schneider (eds.), Reasoning Web. Semantic Technologies for the Web of Data. 7th International Summer School 2011, Galway, Ireland, August 23-27, 2011, Tutorial Lectures. Lecture Notes in Computer Science Vol. 6848, Springer, Heidelberg, 2011, pp. 382-415.