1. Selected Semantic Web Trends, Progress, and Directions Deborah McGuinness Acting Director and Senior Research Scientist Knowledge Systems, AI Laboratory Stanford University http://www.ksl.stanford.edu/people/dlm CEO McGuinness Associates (soon Tetherless World Constellation Chair RPI)

2. June 22, 2007 Deborah L. McGuinness 2 Semantic Web Layers (and DLM) Ontology Level –Language (OWL, IKL, DAML+OIL, OIL, CLASSIC, …) –Environments (FindUR, Chimaera, Ontolingua, OntoBuilder / Server, Sandpiper Tools, Cerebra, …) –Services OWL-S, SWSL, … –Standards body leverage (W3C’s WebOnt, W3C’s Semantic Web Best Practices, EU/US Joint Com, OMG ODM, W3C’s RIF, Scientific Markup Standards, NAPLPS…) Query –OWL-QL, … Rules –RIF, SWRL, … Logic –Description Logics, FOL Proof –PML, Inference Web Services and Infrastructure Trust –IWTrust http://www.w3.org/2003/Talks/1023-iswc-tbl/slide26-0.html, http://flickr.com/photos/pshab/291147522/

3. June 22, 2007 Deborah L. McGuinness 3 Applications –Virtual Solar Terrestrial Observatory, SESDI, SKIF, SSOA, BISTI, … –Cognitive Assistants (PAL, CALO, GILA, …) –Domain ontologies (immunology, atmosphere, volcano, …) & environments –Tools academic & industry (Sandpiper,…) –Startups: Health Information Flow, Blue Chip Expert, Katalytik, Radar Networks…

4. June 22, 2007 Deborah L. McGuinness 4 Two Trends Trust/Explanation –Trust Motivation and Requirements –Inference Web approach Interlingua: Proof Markup Language Explanation & Provenance Browsing / Abstraction / Presentation Strategies (examples from DARPA PAL, NSF TAMI, DTO NIMD, NSF VSTO) Trust (examples from Wikipedia study for explainable knowledge aggregation with extensions to text analytics, connection to NSF TAMI) Semantic Integration of Scientific Data –Virtual Observatories (e.g., NSF-funded Virtual Solar Terrestrial Observatory) –Semantically-Enabled Scientific Data Integration (NASA- funded) Conclusion / Discussion

5. June 22, 2007 Deborah L. McGuinness 5 Virtual Observatories Scientists should be able to access a global, distributed knowledge base of scientific data that: appears to be integrated appears to be locally available But… data is obtained by multiple instruments, using various protocols, in differing vocabularies, using (sometimes unstated) assumptions, with inconsistent (or non- existent) meta-data. It may be inconsistent, incomplete, evolving, and distributed

6. June 22, 2007 Deborah L. McGuinness 6 Virtual Observatory Defined Workshop: A Virtual Observatory (VO) is a suite of software applications on a set of computers that allows users to uniformly find, access, and use resources (data, software, document, and image products and services using these) from a collection of distributed product repositories and service providers. A VO is a service that unites services and/or multiple repositories. VxOs - x is one discipline

7. June 22, 2007 Deborah L. McGuinness 7 Virtual Observatories in Practice Make data and tools quickly and easily accessible to a wide audience. Operationally, virtual observatories need to find the right balance of data/model holdings, portals and client software that a researchers can use without effort or interference as if all the materials were available on his/her local computer using the user’s preferred language. They are likely to provide controlled vocabularies that may be used for interoperation in appropriate domains along with database interfaces for access and storage and “smart” search functions and tools for evolution and maintenance.

8. June 22, 2007 Deborah L. McGuinness 8 Virtual Solar Terrestrial Observatory (VSTO) a distributed, scalable education and research environment for searching, integrating, and analyzing observational, experimental, and model databases. subject matter covers the fields of solar, solar-terrestrial and space physics it provides virtual access to specific data, model, tool and material archives containing items from a variety of space- and ground-based instruments and experiments, as well as individual and community modeling and software efforts bridging research and educational use 3 year NSF-funded project just beginning the second year

9. June 22, 2007 Deborah L. McGuinness 9 Content: Coupling Energetics and Dynamics of Atmospheric Regions WEB Community data archive for observations and models of Earth's upper atmosphere and geophysical indices and parameters needed to interpret them. Includes browsing capabilities by periods, instruments, models, …

10. June 22, 2007 Deborah L. McGuinness 10 Content: Mauna Loa Solar Observatory Near real-time data from Hawaii from a variety of solar instruments. Source for space weather, solar variability, and basic solar physics Other content used too – CISM – Center for Integrated Space Weather Modeling

11. June 22, 2007 Deborah L. McGuinness 11 Content: Volcanoes… Mt. Spurr, AK. 8/18/1992 eruption, USGS http://www.avo.alaska.edu/image.php?id=319

12. June 22, 2007 Deborah L. McGuinness 12 Eruption cloud movement from Mt.Spurr, AK,1992 USGS

13. Tropopause http://aerosols.larc.nasa.gov/volcano2.swf

14. June 22, 2007 Deborah L. McGuinness 14 Atmosphere Use Case Determine the statistical signatures of both volcanic and solar forcings on the height of the tropopause From paleoclimate researcher – Caspar Ammann – Climate and Global Dynamics Division of NCAR - CGD/NCAR Layperson perspective: - look for indicators of acid rain in the part of the atmosphere we experience… (look at measurements of sulfur dioxide in relation to sulfuric acid after volcanic eruptions at the boundary of the troposphere and the stratosphere) Nasa funded effort with Fox - NCAR, Sinha - Va. Tech, Raskin - JPL

15. June 22, 2007 Deborah L. McGuinness 15 Use Case detail: A volcano erupts Preferentially it’s a tropical mountain (+/- 30 degrees of the equator) with ‘acidic’ magma; more SiO2, and it erupts with great intensity so that material and large amounts of gas are injected into the stratosphere. The SO2 gas converts to H2SO4 (Sulfuric Acid) + H2O (75% H2SO4 + 25% H2O). The half life of SO2 is about 30 - 40 days. The sulfuric acid condensates to little super-cooled liquid droplets. These are the volcanic aerosol that will linger around for a year or two. Brewer Dobson Circulation of the stratosphere will transport aerosol to higher latitudes. The particles generate great sunsets, most commonly first seen in fall of the respective hemisphere. The sunlight gets partially reflected, some part gets scattered in the forward direction. Result is that the direct solar beam is reduced, yet diffuse skylight increases. The scattering is responsible for the colorful sunsets as more and more of the blue wavelength are scattered away.in mid-latitudes the volcanic aerosol starts to settle, but most efficient removal from the stratosphere is through tropopause folds in the vicinity of the storm tracks. If particles get over the pole, which happens in spring of the respective hemisphere, then they will settle down and fall onto polar ice caps. Its from these ice caps that we recover annual records of sulfate flux or deposit. We get ice cores that show continuous deposition information. Nowadays we measure sulfate or SO4(2-). Earlier measurements were indirect, putting an electric current through the ice and measuring the delay. With acids present, the electric flow would be faster. What we are looking for are pulse like events with a build up over a few months (mostly in summer, when the vortex is gone), and then a decay of the peak of about 1/e in 12 months. The distribution of these pulses was found to follow an extreme value distribution (Frechet) with a heavy tail.

16. June 22, 2007 Deborah L. McGuinness 16 Use Case detail: … climate So reflection reduces the total amount of energy, forward scattering just changes the beam, path length, but that's it. The dry fogs in the sky (even after thunderstorm) still up there, thus stratosphere not troposphere. The tropical reservoir will keep delivering aerosol for about two years after the eruption. The particles are excellent scatterers in short wavelength. They do absorb in NIR and in IR. Because of absorption, there is a local temperature change in the lower stratosphere. This temperature change will cause some convective motion to further spread the aerosol, and second: Its good factual stuff. Once it warms up, it will generate a temperature gradient. Horizontal temperature gradients increase the baroclinicity and thus storms, and they speedup the local zonal winds. This change in zonal wind in high latitudes is particularly large in winter. This increased zonal wind (Westerly) will remove all cold air that tries to buildup over winter in high arctic. Therefore, the temperature anomaly in winter time is actually quite okay. Impact of volcanoes is to cool the surface through scattering of radiation. In winter time over the continents there might be some warming. In the stratosphere, the aerosol warm. The amount of GHG emitted is comparably small to the reservoir in the air. The hydrologic cycle responds to a volcanic eruption.

17. June 22, 2007 Deborah L. McGuinness 17 Atmosphere (portions from SWEET)

18. June 22, 2007 Deborah L. McGuinness 18 Atmosphere II

19. June 22, 2007 Deborah L. McGuinness 19

20. June 22, 2007 Deborah L. McGuinness 20 A few observations worth noting CMAPS have been convenient knowledge capture tools We facilitate knowledge acquisition meetings AND provide a starting point We are experiencing good reuse of ontologies and infrastructure Next – Quick VSTO walk thru

21. June 22, 2007 Deborah L. McGuinness 21 www.vsto.org

22. June 22, 2007 Deborah L. McGuinness 22

23. June 22, 2007 Deborah L. McGuinness 23 Partial exposure of Instrument class hierarchy - users seem to like this Semantic filtering by domain or instrument hierarchy

24. June 22, 2007 Deborah L. McGuinness 24

25. June 22, 2007 Deborah L. McGuinness 25 Inferred plot type and return required axes data

26. June 22, 2007 Deborah L. McGuinness 26 VSTO Conceptual model and architecture developed by combined team; KR experts, domain experts, and software engineers Semantic framework developed and built with a small, cohesive, carefully chosen team in a relatively short time (deployments in 1st year) Production portal released, includes security, etc. with community migration (and so far endorsement) VSTO ontology version 1.0, (vsto.owl) Web Services encapsulation of semantic interfaces More Solar Terrestrial use-cases to drive the completion of the ontologies - filling out the instrument ontology Using ontologies in other applications (volcanoes, climate, …)

27. June 22, 2007 Deborah L. McGuinness 27 Semantic Web Methodology and Technology Development Process Establish and improve a well-defined methodology vision for Semantic Technology-based application development Use Case Small Team, mixed skills Analysis Adopt Technology Approach Leverage Technology Infrastructure Rapid Prototype Open World: Evolve, Iterate, Redesign, Redeploy Use Tools Expert Review & Iteration Develop model/ ontology Joint with P. Fox

28. June 22, 2007 Deborah L. McGuinness 28 Benefits Unified query workflow Decreased input requirements for query: in one base reducing the number of selections from eight to three Interface generates only syntactically correct queries: which was not always true in previous implementations without semantics Semantic query support: by using background ontologies and a reasoner, our application has the opportunity to only expose coherent queries Semantic integration: in the past users had to remember (and maintain codes) to account for numerous different ways to combine and plot the data whereas now semantic mediation provides the level of sensible data integration required –understanding of coordinate systems, relationships, data synthesis, transformations, etc. A broader range of potential users (PhD scientists, students, professional research associates and those from outside the fields)

29. June 22, 2007 Deborah L. McGuinness 29 Explanation Transition

30. June 22, 2007 Deborah L. McGuinness 30 Interoperability – as systems use varied sources and multiple information manipulation engines, they benefit more from encodings that are shareable & interoperable Provenance – if users (humans and agents) are to use and integrate data from unknown, unreliable, or evolving sources, they need provenance metadata for evaluation Explanation/Justification – if information has been manipulated (i.e., by sound deduction or by heuristic processes), information manipulation trace information should be available Trust – if some sources are more trustworthy than others, representations should be available to encode, propagate, combine, and (appropriately) display trust values Provide interoperable knowledge provenance infrastructure that supports explanations of sources, assumptions, learned information, and answers as an enabler for trust. General Motivation

31. June 22, 2007 Deborah L. McGuinness 31 Requirements gathered from… DARPA Agent Markup Language (DAML)DAML Enable the next generation of the web DARPA Personal Assistant that Learns (PAL)PAL Enable computer systems that can reason, learn, be told what to do, explain actions, reflect on their experience, & respond robustly to surprise DARPA Integrated Learning (IL)IL Enable learning general plans or processes from human users by being shown one example by opportunistically assembling knowledge from many different sources, including generating it by reasoning, in order to learn. DARPA Rapid Knowledge Formation (RKF)RKF Allow distributed teams of subject matter experts to quickly and easily build, maintain, and use knowledge bases without need for specialized training DTO Novel Intelligence for Massive Data (NIMD)NIMD Avoid strategic surprise by helping analysts be more effective (focus attention on critical information and help analyze/prune/refine/explain/reuse/…) DTO IKRIS – Interoperable knowledge representation for intelligence apps NSF & NASA Scientific Data Integration (NSF Virtual Observatories (VSTO), NSF GEON, NASA SESDI, NASA SKIF, …)VSTOGEONSESDI NSF Cybertrust Transparent Accountable Data Mining (TAMI)TAMI Govt Classified applications that must defend their conclusions

32. June 22, 2007 Deborah L. McGuinness 32 Files/WWW Toolkit Proof Markup Language (PML) CWM (NSF TAMI) JTP (DAML/NIMD) SPARK (DARPA CALO) UIMA (DTO NIMD Exp Aggregation) IW Explainer/ Abstractor IWBase IWBrowser IWSearch Trust Justification Provenance N3 KIF SPARK-L Text Analytics IWTrust provenance registration search engine based publishing Expert friendly Visualization End-user friendly visualization Trust computation Semantic Discovery Service (DAML/SNRC) OWL-S/BPEL Framework for explaining question answering tasks by abstracting, storing, exchanging, combining, annotating, filtering, segmenting, comparing, and rendering proofs and proof fragments provided by question answerers. Inference Web Infrastructure primary collaborators Pinheiro da Silva, Ding, Chang, Fikes, Glass, Zeng

33. June 22, 2007 Deborah L. McGuinness 33 How PML Works Justification Trace IWBase NodeSet foo:ns1 (hasConclusion …) Query foo:query1 Question foo:question1 Mapping NodeSet foo:ns2 (hasConclusion …) SourceUsage hasAnswer hasAntecendent fromQuery fromAnswer … isQueryFor InferenceEngine InferenceRule hasVariableMapping hasInferencEngine hasRule InferenceStep Language hasLanguage InferenceStep Source isConsequentOf hasSourceUsage hasSource isConsequentOf usageTime …

34. June 22, 2007 Deborah L. McGuinness 34 PML in Swoop

35. June 22, 2007 Deborah L. McGuinness 35 Example Usage DARPA’s PAL program – explaining cognitive assistant suggestions. Video

36. June 22, 2007 Deborah L. McGuinness 36 Explainer Strategy (for cognitive assistants) Present –Query –Answer –Abstraction of justification (using PML encodings) –Provide access to meta information –Suggests drill down options (also provides feedback options)

37. June 22, 2007 Deborah L. McGuinness 37 Architecture for Explaining Task Processing Collaboration Agent Justification Generator Task Manager (TM) TM Wrapper Explanation Dispatcher Task State Database TM Explainer TaskLearner1 TaskLearner2 TaskLearner3

38. June 22, 2007 Deborah L. McGuinness 38 Task Explanation Ability to ask “why” at any point… Context appropriate follow-up questions are presented

39. June 22, 2007 Deborah L. McGuinness 39 IWBrowser - Browse & Debug (TAMI)

40. June 22, 2007 Deborah L. McGuinness 40 Multiple Interfaces… Browsing Proofs

41. June 22, 2007 Deborah L. McGuinness 41 Browsing & Debugging

42. June 22, 2007 Deborah L. McGuinness 42 Example Abstraction (using tactics)

43. June 22, 2007 Deborah L. McGuinness 43 Intelligence Tool Explanation (similar to other applications that reason with statements that may not be 100% correct)

44. June 22, 2007 Deborah L. McGuinness 44 Follow-up : Metadata

45. June 22, 2007 Deborah L. McGuinness 45 Follow-up: Assumptions

46. June 22, 2007 Deborah L. McGuinness 46 Explaining Extracted Entities (Techies) Sentences in English Sentences in annotated English Sentences in logical format, i.e., KIF

47. June 22, 2007 Deborah L. McGuinness 47 Trustworthiness of Extracted Entities The combined conclusion is highly trustworthy A trustworthy conclusion from IBM STAG KDD-model Annotator A highly trustworthy conclusion from IBM EAnnotator

48. June 22, 2007 Deborah L. McGuinness 48 Estimated trustworthiness of the IBM extraction and integration components IBM Cross-Annotator Coreference Resolver0.82 IBM Cross-Document Coreference Resolver0.63 IBM EAnnotator0.91 IBM GlossOnt0.33 IBM JResporator 0.31 IBM KANI holdsDuring Relation Detector0.20 IBM Knowledge Integrator0.88 IBM Knowledge Structures Group's Relation Detector0.94 IBM Statistical Text Analytics Group's ACE-model Annotator0.80 IBM Statistical Text Analytics Group's KDD-model Annotator0.73 IBM TAF/Talent plus a collection of miscellaneous TFST grammars 0.78 IBM Talent time annotator0.83

49. June 22, 2007 Deborah L. McGuinness 49 Trustworthiness of Ramazi report From CIA From FBI From intercepts

50. June 22, 2007 Deborah L. McGuinness 50 Trustworthiness of revised Ramazi report This text fragment changed from “Neutral” to “Trustworthy” after it was revised by an analyst (the phone number was corrected).

51. 51 Trust Representation, Calculation, & Propagation - Begin with simple representation of trust - Present trust coloring view - Calculation & propagation research options

52. 52 fragment A Sample PML encoding http://inferenceweb.stanford.edu/2006/02/example1-iw-wiki.owl fragment trust author trust 0.1766 0.1766

53. June 22, 2007 Deborah L. McGuinness 53 Conclusion Semantic Web languages and tools are evolving and currently are enabling next generation collaboration and applications Some examples here include support for - explainable question answering systems - semantic integration of information - trustable applications - usable, integrated, explainable virtual observatories For more info on talk topics: - Inference Web - iw.stanford.edu (OWL - www.w3.org/TR/owl-features/ )iw.stanford.eduwww.w3.org/TR/owl-features/ -Virtual Solar Terrestrial Observatory- www.vsto.orgwww.vsto.org -Semantic Technology Conference - www.semantic-conference.com/www.semantic-conference.com/ -AAAI workshops: Explanation-Aware Computing, Semantic eScience, IAAI -Special Issue for Elsevier Computers and GeoSciences, New Springer Journal – Journal of Earth Science Informatics, … - dlm@ksl.stanford.edu

54. June 22, 2007 Deborah L. McGuinness 54 More Information http://iw.stanford.edu/2.0/publications.html -Best Inference Web Paper: Deborah L. McGuinness and Paulo Pinheiro da Silva. Explaining Answers from the Semantic Web: The Inference Web Approach. Journal of Web Semantics. Vol.1 No.4., pages 397-413, October 2004. -Best PML paper: Paulo Pinheiro da Silva, Deborah L. McGuinness and Richard Fikes. A Proof Markup Language for Semantic Web Services. Information Systems. Volume 31, Issues 4-5, June-July 2006, Pages 381-395. Best Requirements paper (from interviewing Intelligence Analysts) Cowell, McGuinness, Varley, &Thurman. Knowledge-Worker Requirements for Next Generation Query Answering and Explanation Systems. Workshop on Intelligent User Interfaces for Intelligence Analysis, (IUI 2006), Sydney, Australia Best UI paper - Explanation Interfaces for the Semantic Web. SWUI ’06. http://www.ksl.stanford.edu/KSL_Abstracts/KSL-06-14.html VSTO. McGuinness, Fox, Cinquini, Darnell, West, Benedict, Garcia, Middleton. Ontology-Enabled Virtual Observatories: Semantic Integration in Practice. ISWC06. Athens, Georgia, USA. ksl.stanford.edu/KSL_Abstracts/KSL-06-20.html www.vsto.org

55. June 22, 2007 Deborah L. McGuinness 55 Extras

56. June 22, 2007 Deborah L. McGuinness 56 KSL Wine Agent Semantic Web Integration Wine Agent receives a meal description and retrieves a selection of matching wines available on the Web, using an ensemble of emerging standards and tools: OWL for representing a domain ontology of foods, wines, their properties, and relationships between them JTP theorem prover for deriving appropriate pairings OWL-QL for querying a knowledge base consisting of the above Inference Web for explaining and validating the response [Web Services for interfacing with vendors] Utilities for conducting and caching the above transactions

57. June 22, 2007 Deborah L. McGuinness 57

58. June 22, 2007 Deborah L. McGuinness 58 Processing Given a description of a meal, –Use OWL-QL to state a premise (the meal) and query the knowledge base for a suggestion for a wine description or set of instances –Use JTP to deduce answers (and proofs) –Use Inference Web to explain results (descriptions, instances, provenance, reasoning engines, etc.) –Access relevant web sites (wine.com, …) to access current information –Use OWL-S for markup and protocol* This general scheme used in other projects like TAMI, IL, etc. http://www.ksl.stanford.edu/projects/wine/explanation.html

59. June 22, 2007 Deborah L. McGuinness 59 Ontology Spectrum Catalog/ ID Disjointness, Inverse, part-of… Terms/ glossary Thesauri “narrower term” relation Formal is-a Frames (properties) Informal is-a Formal instance Value Restrs. General Logical constraints Originally from AAAI 1999- Ontologies Panel – updated by McGuinness Markup such as DAML+OIL, OWL can be used to encode the spectrum

60. June 22, 2007 Deborah L. McGuinness 60 Scaling to large numbers of data providers Crossing disciplines Security, access to resources, policies Branding and attribution (where did this data come from and who gets the credit, is it the correct version, is this an authoritative source?) Provenance/derivation (propagating key information as it passes through a variety of services, copies of processing algorithms, …) Data quality, preservation, stewardship, rescue Interoperability at a variety of levels (~3) Issues for Virtual Observatories Semantics can help with many of these

61. June 22, 2007 Deborah L. McGuinness 61 Impact: Virtual Observatories Changing Science Scientists: What if you… -could not only use your data and tools but remote colleague’s data and tools? -understood their assumptions, constraints, etc and could evaluate applicability? -knew whose research currently (or in the future) would benefit from your results? -knew whose results were consistent (or inconsistent) with yours?… Funders/Managers: What if you … -could identify how one research effort would support other efforts? -(and your fundees/employees) could reuse previous results? -(and your fundees/employees) could really interoperate? CS: What if you… -could apply your techniques across very large distributed teams of people with related but different apps? -could compare your techniques with colleagues trying to solve similar problems?