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Ontologies Come of Age: The Next Generation

Ontologies Come of Age: The Next Generation. Deborah L. McGuinness Tetherless World Senior Constellation Chair Professor of Computer and Cognitive Science Rensselaer Polytechnic Institute Troy, NY, USA. OCAS October 24, 2011 Bonn, Germany. Background. Ontologies Come of Age (1)

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Ontologies Come of Age: The Next Generation

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  1. Ontologies Come of Age: The Next Generation Deborah L. McGuinness Tetherless World Senior Constellation Chair Professor of Computer and Cognitive Science Rensselaer Polytechnic Institute Troy, NY, USA OCAS October 24, 2011 Bonn, Germany

  2. Background • Ontologies Come of Age (1) • Invited presentation at the Semantics for the Web Dagstuhl meeting in March 2000. • Followed an Ontologies AAAI panel in 1999 where the first ontology spectrum was generated • First Ontologies Come of Age paper described all points on the ontology expressiveness spectrum and provided examples of value at each point (in Spinning the Sem Web, 2003) • Built on current academic experiences: large knowledge base and ontology programs such as HPKB • Built on current consulting experiences – building ontologies and ontology groups (then private ontologies for competitive advantage; now public ontologies for interoperability)

  3. What is an Ontology? Thesauri “narrower term” relation Frames (properties) Formal is-a General Logical constraints Catalog/ ID Informal is-a Formal instance Value Restrs. Disjointness, Inverse, part-of… Terms/ glossary Ontologies Come of Age McGuinness, 2001, and From AAAI Panel 99 – McGuinness, Welty, Uschold, Gruninger, Lehmann Plus basis of Ontologies Come of Age – McGuinness, 2003

  4. A Few Observations about Ontologies (March 2000 / 2011) • Simple ontologies can be built by non-experts • Consider Verity’s Topic Editor, Collaborative Topic Builder, GFP interface, Chimaera, etc. More tools, more ontologies, more expressiveness points • Ontologies can be semi-automatically generated (now more machine learning as well as from social collaborative settings) • from crawls of site such as yahoo!, amazon, excite, etc. • Semi-structured sites can provide starting points • Ontologies are exploding (business pull instead of technology push) • most e-commerce sites are using them - MySimon, Affinia, Amazon, Yahoo! Shopping,, etc. • Controlled vocabularies (for the web) abound - SIC codes, UMLS, UN/SPSC, Open Directory, Rosetta Net, … • Business ontologies are including roles • DTDs are making more ontology information available • Businesses have ontology directors • “Real” ontologies are becoming more central to applications (and real ontologies arising from massive data ) • New models such as virtual observatories accelerating pull

  5. But now…. Past emphasis was more on expressiveness and building (usually by trained experts) Now the emphasis is more about the ecosystem in which ontology-enabled applications are embedded, maintained, understood, trusted, and used...

  6. Semantic Agents Semantically-enabled advisors utilize: • Ontologies • Reasoning • Social • Mobile • Provenance • Context Patton & McGuinness.et. al tw.rpi.edu/web/project/Wineagent

  7. Ontology Ecosystem Discussion & Directions • Base ontology very simple • Wine, Winery, Grape, Flavor, Body, Color, Sugar • Stood the test of time: Living with Classic (1991),, CLASSIC tutorials, Ontologies 101, OWL Guide, … • To scale however, needs to be compatible with WIDE range of menus, wine lists, vocabularies. Not hard to obtain but significant enhancement required. • Needs more ecosystem support – explanation, provenance, validation, inconsistency detection, prioritization scheme, UI considerations, additional social connections, citizen-oriented maintenance and evolution schemes, scale, partitioning, … www.ksl.stanford.edu/people/dlm/papers/living-with-classic-abstract.html

  8. SemantAqua / SemantEco Aimed at helping people investigate local water quality • Diverse datasets, regulations, datatypes • Uses lightweight semantic technologies to produce mashups that make data accessible that would be otherwise difficult to view in perspective • Maintains provenance about data and manipulations • Potential to empower citizens with contextualized data and support citizen scientist questions and reporting • Tues Demo & Wed aft talk

  9. Discussion and Directions • Base ontology also very simple - Water, contaminant, threshold, test • Simple use of recognition and easily extensible (e.g., recently with health impact data) • To scale however, needs to be compatible with wide range of data source vocabularies, including a wide range of tests • New processes create new vocabulary needs (e.g., protectingourwaters.wordpress.com/2011/06/16/black-water-and-brazenness-gas-drilling-disrupts-lives-endangers-health-in-bradford-county-pa/ ) • Needs more ecosystem support – explanation, provenance, validation, inconsistency detection, prioritization scheme, UI considerations, additional social connections, citizen-oriented maintenance and evolution schemes, scale (3 billion triples and counting), partitioning…

  10. Interdisciplinary Virtual Observatory (VSTO) • General: Find data subject to certain constraints and plot appropriately • Specific: Plot the observed/measured Neutral Temperature as recorded by the Millstone Hill Fabry-Perot interferometer while looking in the vertical direction at any timeofhigh geomagnetic activity in a way that makes sense for the data.

  11. Semantic Web Methodology Originally developed for VSTO, now in SSIII, SESDI, SESF, OOI … McGuinness, Fox, West, Garcia, Cinquini, Benedict, Middleton The Virtual Solar-Terrestrial Observatory: A Deployed Semantic Web Application Case Study for Scientific Research. Proc. 19 Conf. on Innovative Applications of Artificial Intelligence (IAAI-07), http://www.vsto.org

  12. Inference Web: Making Data Transparent and Actionable Using Semantic Technologies • How and when does it make sense to use smart system results & how do we interact with them? (Mobile) Intelligent Agents Knowledge Provenance in Virtual Observatories NSF Interops: SONET SSIII – Sea Ice Intelligence Analyst Tools Hypothesis Investigation / Policy Advisors 12 12

  13. Ontology Ecosystem Discussion & Directions • Base ontology relatively simple - Instrument, Observatory, Data Product, … • Initially done for solar terrestrial physics but has been used in volcanology, plate tectonics, sea ice, water, … with relatively little rework (NSF: VSTO, SPCDIS, SESF, SSI, SONET … NASA: SESDI, …) • Modularity has been key – both to reusing other ontologies (e.g., SWEET) and in expanding our reuse • To scale and be maintainable however, need to be compatible with WIDE range of evolving vocabularies. (Unlike the wine agent and to some extent the water quality portal, this is not as uncomplicated,) • Needs more ecosystem support – explanation, provenance, validation, inconsistency detection, prioritization scheme, UI considerations, additional social connections, citizen-oriented maintenance and evolution schemes, scaling, partitioning, …

  14. What is different now (10+ years later)? • Ontologies (at many points on an expressiveness spectrum) are in use in wide variety of settings and disciplines and are built by a broad(er) range of users • Ontologies are becoming longer lived…. With that some best practices are emerging including • Modularity • Designing for reuse (minimizing tight constraints, naming, …) • Modules with more depth • Provenance considerations – provenance info included and service connection example • Recommended Web Ontology Language (and business consequences), Rules recommendation, Provenance on its way • Issues are much less about starting points for ontologies – they are now about mapping, reusability, maintenance, and sustainability • Issues are not only technical – social issues of team acquisition and maintenance are at least as important

  15. Linked Data Cloud

  16. What might we do? • Guidelines for creating ontologies for reuse – modularity, limited conflict generators, ease of use considerations – one early one was explanation for debugging along the lines of McGuinness’ thesis • Provenance - Representation (e.g., W3C working group), Watermarking , … • Semi-automatic tools for ontology creation and maintenance • Checking • Expanding • Mapping • Hybrid tools for working with learning / discovery tools AND humans • Exploit citizen xx and social • More testimonials in forms that serve as operational specifications • Directions for examples such as Watson-style work • What do you need for ontologies to be practically and sustainably used in commodity computing? - forthcoming 4th paradigm blog post Questions / Suggestions? dlm @ cs . rpi . edu

  17. Questions? Come to Demo session on SemantAqua on Tuesday Come to talk on Semantic Monitoring on Wednesday afternoon dlm <at> cs <dot> rpi <dot> edu

  18. Ontologies for the Real World Deborah L. McGuinness Tetherless World Senior Constellation Chair Professor of Computer and Cognitive Science Rensselaer Polytechnic Institute

  19. BACKUP SLIDES

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