Selected Semantic Web Trends, Progress, and Directions

Selected Semantic Web Trends, Progress, and Directions

Deborah McGuinness

Acting Director and Senior Research ScientistKnowledge Systems, AI Laboratory

Stanford Universityhttp://www.ksl.stanford.edu/people/dlm

CEO McGuinness Associates

(soon Tetherless World Constellation Chair RPI)

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

InfrastructureTrust

– IWTrusthttp://www.w3.org/2003/Talks/1023-iswc-tbl/slide26-0.html, http://flickr.com/photos/pshab/291147522/


Applications

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…


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


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


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


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.


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


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, …


Content: Mauna Loa Solar ObservatoryNear 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


Content: Volcanoes…Mt. Spurr, AK. 8/18/1992 eruption, USGS

http://www.avo.alaska.edu/image.php?id=319


Eruption cloud movement from Mt.Spurr, AK,1992

USGS

Tropopause

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


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


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.


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.


Atmosphere (portions from SWEET)


Atmosphere II


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


www.vsto.org


•


Partial exposure of Instrument class hierarchy - users seem to like this

Semantic filtering by domain or instrument hierarchy


Inferred plot type and return required axes data


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, …)


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 PrototypeOpen World:

Evolve, Iterate, Redesign, Redeploy

Use Tools

Expert Review & Iteration

Develop model/

ontology

Joint with P. Fox


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)


Explanation Transition


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.

Provide interoperable knowledge provenance infrastructure that supports explanations of

sources, assumptions, learned information, and answers as an enabler for trust.

General Motivation


Requirements gathered from…DARPA Agent Markup Language (DAML)

Enable the next generation of the webDARPA Personal Assistant that Learns (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)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)Allow distributed teams of subject matter experts to quickly and easily build, maintain,

and use knowledge bases without need for specialized trainingDTO Novel Intelligence for Massive Data (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, …)NSF Cybertrust Transparent Accountable Data Mining (TAMI)Govt Classified applications that must defend their conclusions

http://www.daml.org/

http://www.darpa.mil/ipto/programs/pal/

http://www.darpa.mil/ipto/solicitations/open/05-43_PIP.htm

http://reliant.teknowledge.com/RKF/

http://www.ic-arda.org/Novel_Intelligence/

http://vsto.hao.ucar.edu/

http://www.geongrid.org/geoinformatics2006/

http://sesdi.hao.ucar.edu/

http://www.ksl.stanford.edu/people/dlm/papers/egov-tami-abstract.html


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

provenanceregistration

search enginebased publishing

Expert friendlyVisualization

End-user friendly visualization

Trust computationSemantic 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


How PML Works

Justification Trace

IWBase

NodeSet foo:ns1(hasConclusion …)

Query foo:query1<formal internal structured query>

Question foo:question1 <Input language question>

Mapping

NodeSet foo:ns2(hasConclusion …)

SourceUsage

hasAnswer

hasAntecendent

fromQuery

fromAnswer

…

isQueryFor

InferenceEngine

InferenceRule

hasVariableMapping

hasInferencEngine

hasRuleInferenceStep

Language hasLanguage

InferenceStep

Source

isConsequentOf

hasSourceUsage hasSource isConsequentOf

usageTime …


PML in Swoop


Example Usage

• DARPA’s PAL program – explaining cognitive assistant suggestions.

• Video


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)


Architecture for Explaining Task Processing

Collaboration Agent

Justification Generator

Task Manager (TM)

TM WrapperExplanation Dispatcher

Task State Database

TM Explainer

TaskLearner1

TaskLearner2

TaskLearner3


Task Explanation

Ability to ask “why” at any point…

Context appropriate follow-up questions are presented


IWBrowser - Browse & Debug (TAMI)


Multiple Interfaces… Browsing Proofs


Browsing & Debugging


Example Abstraction (using tactics)


Intelligence Tool Explanation(similar to other applications that reason with statements

that may not be 100% correct)


Follow-up : Metadata


Follow-up: Assumptions


Explaining Extracted Entities (Techies)

Sentences in English

Sentences in annotated English

Sentences in logical format, i.e., KIF


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


Estimated trustworthiness of the IBM extraction and integration components

IBM Cross-Annotator Coreference Resolver 0.82 IBM Cross-Document Coreference Resolver 0.63 IBM EAnnotator 0.91 IBM GlossOnt 0.33 IBM JResporator 0.31 IBM KANI holdsDuring Relation Detector 0.20 IBM Knowledge Integrator 0.88 IBM Knowledge Structures Group's Relation Detector 0.94 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 annotator 0.83


Trustworthiness of Ramazi report

From CIA

From FBI

From intercepts


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

Trust Representation, Calculation, & Propagation

- Begin with simple representation of trust

- Present trust coloring view

- Calculation & propagation research options

52

fragment

A Sample PML encodinghttp://inferenceweb.stanford.edu/2006/02/example1-iw-wiki.owl

fragment trust

author trust

<iw:NodeSet rdf:about="http://foto.stanford.edu/mediawiki-1.4.12/index.php/Natural_number"> <In mathematics, a natural number is either a positive integer … </iw:hasConclusion> <iw:hasLanguage rdf:resource="http://inferenceweb.stanford.edu/registry/LG/English.owl#English"/> <iw:isConsequentOf> <iw:InferenceStep> <iw:hasRule rdf:resource="http://inferenceweb.stanford.edu/registry/DPR/Told.owl#Told"/> <iw:hasInferenceEngine rdf:resource="http://inferenceweb.stanford.edu/registry/IE/CitationTrust.owl#CitationTrust"/> <iw:hasSourceUsage> <iw:SourceUsage> <iw:hasSource> <iw:Source rdf:about="http://inferenceweb.stanford.edu/wp/registry/PER/Alexandrov.owl#Alexandrov"/> </iw:hasSource> </iw:SourceUsage> </iw:hasSourceUsage> </iw:InferenceStep> </iw:isConsequentOf></iw:NodeSet>

<iw:AggregatedTrustRelation> <iw:hasTrustingParty rdf:resource="http://inferenceweb.stanford.edu/wp/registry/ORG/wikipedia.owl#wikipedia"/> <iw:hasTrustedParty rdf:resource="http://foto.stanford.edu/mediawiki-1.4.12/index.php/Natural_number"/> <iw:hasTrustValue rdf:datatype="http://www.w3.org/2001/XMLSchema#float">0.1766</iw:hasTrustValue></iw:AggregatedTrustRelation>

<iw:AggregatedTrustRelation> <iw:hasTrustingParty rdf:resource="http://inferenceweb.stanford.edu/wp/registry/ORG/wikipedia.owl#wikipedia"/> <iw:hasTrustedParty rdf:resource="http://inferenceweb.stanford.edu/wp/registry/PER/Alexandrov.owl#Alexandrov"/> <iw:hasTrustValue rdf:datatype="http://www.w3.org/2001/XMLSchema#float">0.1766</iw:hasTrustValue></iw:AggregatedTrustRelation>


ConclusionSemantic Web languages and tools are evolving and currently are

enabling next generation collaboration and applicationsSome 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/ )- Virtual Solar Terrestrial Observatory- www.vsto.org - Semantic Technology Conference - 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, …- [email protected]


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


Extras


KSL Wine AgentSemantic 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


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


Ontology Spectrum

Catalog/ID

Disjointness, Inverse, part-of…

Terms/glossary

Thesauri“narrower

term”relation

Formalis-a

Frames(properties)

Informalis-a

Formalinstance Value

Restrs.

GeneralLogicalconstraints

Originally from AAAI 1999- Ontologies Panel – updated by McGuinness

Markup such as DAML+OIL, OWL can be used to encode the spectrum


• 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


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?

Selected Semantic Web Trends, Progress, and Directions

Documents

Transcript of Selected Semantic Web Trends, Progress, and Directions