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Deborah McGuinness Co-Director Knowledge Systems, Artificial Intelligence Laboratory

Next Generation Scientific Digital Libraries (or The Semantic Web and Digital Libraries as Knowledge Systems). Deborah McGuinness Co-Director Knowledge Systems, Artificial Intelligence Laboratory Stanford University

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Deborah McGuinness Co-Director Knowledge Systems, Artificial Intelligence Laboratory

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  1. Next Generation Scientific Digital Libraries (or The Semantic Web and Digital Libraries as Knowledge Systems) Deborah McGuinness Co-Director Knowledge Systems, Artificial Intelligence Laboratory Stanford University dlm@ksl.stanford.edu McGuinness JCIS June 11, 2005

  2. Outline • Introduction • The Semantic Web, Ontologies, and the Ontology Web Language • Selected Technical Benefits of Semantic Technologies • Discussion and Directions McGuinness JCIS June 11, 2005

  3. Semantic Web Perspectives • The Semantic Web means different things to different people. It is multi-dimensional • Distributed data access • Inference • Data Integration • Logic • Services • Search (based on term meaning) • Configuration • Agents • … • Different users value these dimensions differently • Theme: Machine-operational declarative specification of the meaning of terms McGuinness JCIS June 11, 2005

  4. Semantic Web Layers Ontology Level • Languages (CLASSIC, DAML-ONT, DAML+OIL, OWL, …) • Environments (FindUR, Chimaera, OntoBuilder/Server, Sandpiper Tools, …) • Standards (NAPLPS, …, W3C’s WebOnt, W3C’s Semantic Web Best Practices, EU/US Joint Committee, OMG ODM, … Rules • SWRL (previously CLASSIC Rules, explanation environment, extensibility issues, contracts, …) Logic • Description Logics Proof • PML, Inference Web Services and Infrastructure Trust • IWTrust, Policy encodings, … McGuinness JCIS June 11, 2005 http://www.w3.org/2004/Talks/0412-RDF-functions/slide4-0.html

  5. Semantic Web Statements • The Semantic Web is made up of individual statements • The subject and predicate are Uniform Resource Identifiers (URIs) – the object can be a URI or an optionally typed literal value subject object predicate worksFor #Stanford #Deborah collaboratesWith worksFor #NCAR #Peter #McGuinness Assoc worksFor surname “Fox” surname “McGuinness” McGuinness JCIS June 11, 2005

  6. Ontology Spectrum Thesauri “narrower term” relation Frames (properties) Formal is-a General Logical constraints Catalog/ ID Informal is-a Formal instance Disjointness, Inverse, part-of… Terms/ glossary Value Restrs. Ontology languages such as DAML+OIL, OWL can be used to encode the spectrum Originally from AAAI 1999- Ontologies Panel – updated by McGuinness McGuinness JCIS June 11, 2005

  7. General Nature of Descriptions class a WINE superclass a LIQUID a POTABLE grape: chardonnay, ... [>= 1] sugar-content: dry, sweet, off-dry color: red, white, rose price: a PRICE winery: a WINERY grape dictates color (modulo skin) harvest time and sugar are related general categories number/card restrictions structured components Roles/ properties value restrictions interconnections between parts McGuinness JCIS June 11, 2005

  8. DAML/OWL Language • Web Languages • XML • RDF/S • Extends vocabulary of XML and RDF/S • Rich ontology representation language • Language features chosen for efficient implementations DAML-ONT DAML+OIL OWL OIL Formal Foundations Description Logics Frame Systems FACT, CLASSIC, DLP, … McGuinness JCIS June 11, 2005

  9. Selected Technical Benefits • Integrating Multiple Data Sources • Semantic Drill Down / Focused Perusal • Statements about Statements • Inference • Translation • Smart (Focused) Search • Smarter Search … Configuration • Proof McGuinness JCIS June 11, 2005

  10. 1: Integrating Multiple Data Sources • The Semantic Web lets us merge statements from different sources • The RDF Graph Model allows programs to use data uniformly regardless of the source • Figuring out where to find such data is a motivator for Semantic Web Services #US #USD currency name telephoneCode “United States” “1” Different line & text colors represent different data sources McGuinness JCIS June 11, 2005

  11. 2: Drill Down /Focused Perusal • The Semantic Web uses Uniform Resource Identifiers (URIs) to name things • These can typically be resolved to get more information about the resource • This essentially creates a web of data analogous to the web of text created by the World Wide Web • Ontologies are represented using the same structure as content • We can resolve class and property URIs to learn about the ontology …#Deborah …#California Internet locatedIn worksFor ...#Stanford ...#McGuinness Assoc type type …#University ...#Company McGuinness JCIS June 11, 2005

  12. 3: Statements about Statements • The Semantic Web allows us to make statements about statements • Timestamps • Provenance / Lineage • Authoritativeness / Probability / Uncertainty • Security classification • … • This is an unsung virtue of the Semantic Web #Estimate #US type population year 2003 290342554 McGuinness JCIS June 11, 2005 From CIA World Factbook

  13. 4: Inference • The formal foundations of the Semantic Web allow us to infer additional (implicit) statements that are not explicitly made • Unambiguous semantics allow question answerers to infer that objects are the same, objects are related, objects have certain restrictions, … • SWRL allows us to make additional inferences beyond those provided by the ontology sibling #Joe #Louise hasBrother sibling daughterOf hasUncle hasMother hasChild #Deborah McGuinness JCIS June 11, 2005

  14. 5: Translation • While encouraging sharing, the Semantic Web allows multiple URIs to refer to the same thing • There are multiple levels of mapping • Classes • Properties • Instances • Ontologies • OWL supports equivalence and specialization; SWRL allows more complex mappings #car type ont1:country ont1:Car fips:UK #car type ont2:country ont2:Vehicle iso:GB McGuinness JCIS June 11, 2005

  15. 6: Smart (Focused) Search • The Semantic Web associates 1 or more classes with each object • We can use ontologies to enhance search by: • Query expansion • Sense disambiguation • Type with restrictions • …. McGuinness JCIS June 11, 2005

  16. McGuinness JCIS June 11, 2005

  17. 7: Smarter Search / Configuration McGuinness JCIS June 11, 2005

  18. KSL Wine AgentSemantic Web Integration Example • Uses emerging web standards to enable smart web applications • Given a meal description • Deborah’s Specialty • Describe matching wines • White, Dry, Full bodied… • Retrieve some specific options from web • Forman Chardonnay from DLM’s cellar, ThreeSteps from wine.com, …. • Info: http://www.ksl.stanford.edu/people/dlm/webont/wineAgent/ McGuinness JCIS June 11, 2005

  19. KSL Wine Agent Semantic Web Integration Technology • OWL • for representing a domain ontology of foods, wines, their properties, and relationships between them • JTP theorem prover • for deriving appropriate pairings • DQL/OWL QL • for querying a knowledge base • Inference Web • for explaining and validating answers • (descriptions or instances) • Web Services • for interfacing with vendors • Connections to online web agents/information services • Utilities for conducting and caching the above transactions McGuinness JCIS June 11, 2005

  20. McGuinness JCIS June 11, 2005

  21. 8: Proof • The logical foundations of the Semantic Web allow us to construct proofs that can be used to improve transparency, understanding, and trust • Proof and Trust are on-going research areas for the Semantic Web: e.g., See PML and Inference Web #W3C #Acme hasMember hasEmployee #Bob “Employees of member companies can access W3C’s content” McGuinness JCIS June 11, 2005

  22. Scientific Digital Libraries Scientists should be able to access a global, distributed knowledge base of scientific data that: • appears to be integrated • appears to be locally available • is easy to search But… data is obtained by multiple instruments, using various protocols, in differing (possibly unfamiliar) vocabularies, using (sometimes unstated) assumptions, with inconsistent (or non-existent) meta-data. It may be inconsistent, incomplete, evolving, and distributed McGuinness JCIS June 11, 2005

  23. Future Science Digital Libraries • Repositories of data with markup and provenance that enables… • sharing data AND tools with distributed colleagues • understanding assumptions, constraints, and enough information to determine applicability and reuse • research data and experiment composition and dependence • consistency and validation checking and more… • Current and future repositories are poised to change the nature of how science is done by supporting interoperability and sharing at new levels. • Projects like the Virtual Solar Terrestrial Observatory, GEON, etc. use semantic web technology to enable next generation digital scientific libraries McGuinness JCIS June 11, 2005

  24. Conclusion • Semantic Web Languages and Tools are ready for use (OWL, OWL-S, Cerebra, Sandpiper, …) • Predecessor technology (description logics etc.) have been in use for decades • Current ontologies and tools being used in science: • Gene Ontology (GO) • NCI and UMLS • SWEET (Semantic Web for Earth and Environmental Terminology ) • Immune Epitope DataBase • GEON • Virtual Solar Terrestrial Observatory • … • The time is NOW to work together towards next generation semantically-enabled interoperable systems McGuinness JCIS June 11, 2005

  25. Resources • Selected Papers: • McGuinness. Ontologies come of age, 2003 • Das, Wei, McGuinness, Industrial Strength Ontology Evolution Environments, 2002. • Kendall, Dutra, McGuinness. Towards a Commercial Strength Ontology Development Environment, 2002. • McGuinness Description Logics Emerge from Ivory Towers, 2001. • McGuinness. Ontologies and Online Commerce, 2001. • McGuinness. Conceptual Modeling for Distributed Ontology Environments, 2000. • McGuinness, Fikes, Rice, Wilder. An Environment for Merging and Testing Large Ontologies, 2000. • Brachman, Borgida, McGuinness, Patel-Schneider. Knowledge Representation meets Reality, 1999. • McGuinness. Ontological Issues for Knowledge-Enhanced Search, 1998. • Selected Tutorials: • -Smith, Welty, McGuinness. OWL Web Ontology Language Guide, 2004. • Noy, McGuinness. Ontology Development 101: A Guide to Creating your First Ontology. 2001. • Brachman, McGuinness, Resnick, Borgida. How and When to Use a KL-ONE-like System, 1991. • Languages, Environments, Software: • OWL - http://www.w3.org/TR/owl-features/ , http://www.w3.org/TR/owl-guide/ • Inference Web - http://www.ksl.stanford.edu/software/iw/ • Wine Agent - http://www.ksl.stanford.edu/people/dlm/webont/wineAgent/ • - Chimaera - http://www.ksl.stanford.edu/software/chimaera/ • FindUR - http://www.research.att.com/people/~dlm/findur/ • - TAP – http://tap.stanford.edu/ • OWL-QL - http://www.ksl.stanford.edu/projects/owl-ql/ • Cerebra (formerly Network Inference) – http://www.cerebra.com • Sandpiper Software – http://www.sandsoft.com • Virtual Solar Terrestrial Observatory - http://vsto.hao.ucar.edu/ McGuinness JCIS June 11, 2005

  26. EXTRAS McGuinness JCIS June 11, 2005

  27. OWL McGuinness JCIS June 11, 2005

  28. OWL Sublanguages • OWL Lite supports users primarily needing a classification hierarchy and simple constraint features. (For example, while it supports cardinality constraints, it only permits cardinality values of 0 or 1. It should be simpler to provide tool support for OWL Lite than its more expressive relatives, and provides a quick migration path for thesauri and other taxonomies.) • OWL DL supports users who need maximum expressiveness while their reasoning systems maintain computational completeness (all conclusions are guaranteed to be computed) and decidability (all computations will finish in finite time). OWL DL includes all OWL language constructs, but they can be used only under certain restrictions (for example, while a class may be a subclass of many classes, a class cannot be an instance of another class). OWL DL is named for its correspondence with description logics. • OWL Full supports users who want maximum expressiveness and the syntactic freedom of RDF with no computational guarantees. For example, in OWL Full a class can be treated simultaneously as a collection of individuals and as an individual in its own right. OWL Full allows an ontology to augment the meaning of the pre-defined (RDF or OWL) vocabulary. It is unlikely that any complete and efficient reasoner will be able to support every feature of OWL Full. McGuinness JCIS June 11, 2005

  29. OWL Lite Features • RDF Schema Features • Class, rdfs:subClassOf , Individual • rdf:Property, rdfs:subPropertyOf • rdfs:domain , rdfs:range • Equality and Inequality • equivalentClass , equivalentProperty , sameAs • differentFrom • AllDifferent , distinctMembers • Restricted Cardinality • minCardinality, maxCardinality (restricted to 0 or 1) • cardinality (restricted to 0 or 1) • Property Characteristics • inverseOf , TransitiveProperty , SymmetricProperty • FunctionalProperty(unique) , InverseFunctionalProperty • allValuesFrom, someValuesFrom (universal and existential local range restrictions) • Datatypes • Following the decisions of RDF Core. • Header Information • imports , Dublin Core Metadata , versionInfo McGuinness JCIS June 11, 2005

  30. OWL Features • Class Axioms • oneOf (enumerated classes) • disjointWith • equivalentClass applied to class expressions • rdfs:subClassOf applied to class expressions • Boolean Combinations of Class Expressions • unionOf • intersectionOf • complementOf • Arbitrary Cardinality • minCardinality • maxCardinality • cardinality • Filler Information • hasValue Descriptions can include specific value information McGuinness JCIS June 11, 2005

  31. OWL Lite and OWL • Overview: http://www.w3.org/TR/owl-features/ • Guide: http://www.w3.org/TR/owl-guide/ • Reference: http://www.w3.org/TR/owl-ref/ • Semantics and Abstract Syntax: http://www.w3.org/TR/owl-absyn/ McGuinness JCIS June 11, 2005

  32. Virtual Solar Terrestrial Observatories McGuinness JCIS June 11, 2005

  33. Background 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 McGuinness JCIS June 11, 2005

  34. Virtual Observatories 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” tools for evolution and maintenance. McGuinness JCIS June 11, 2005

  35. 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 in first year McGuinness JCIS June 11, 2005

  36. Inference Web and Explanation McGuinness JCIS June 11, 2005

  37. Inference Web Framework for explaining reasoning tasks by storing, exchanging, combining, annotating, filtering, segmenting, comparing, and rendering proofs and proof fragments provided by multiple distributed reasoners. • OWL-based Proof Markup Language (PML) specification as an interlingua for proof interchange • IWExplainer for generating and presenting interactive explanations from PML proofs providing multiple dialogues and abstraction options • IWBrowser for displaying (distributed) PML proofs • IWBase distributed repository of proof-related meta-data such as inference engines/rules/languages/sources • Integrated with theorem provers, text analyzers, web services, … http://iw.stanford.edu McGuinness JCIS June 11, 2005

  38. SW Questions & Answers Users can explore extracted entities and relationships, create new hypothesis, ask questions, browse answers and get explanations for answers. A context for explaining the answer A question An answer An abstracted explanation (this graphical interface done by Batelle supported by KSL) McGuinness JCIS June 11, 2005

  39. Browsing Proofs The proof associated with an answer can be browsed in multiple formats. Menu to switch between Graphical/HTML Proof Styles Proof Rendered in Graphical Style Provenance Information associated with a selected NodeSet McGuinness JCIS June 11, 2005

  40. Selected Semantic Web Tools McGuinness JCIS June 11, 2005

  41. Protégé • Open source ontology editor from Stanford Medical Informatics • Large user community • Good GUI interface for subject-matter experts • Extra features • SWRL support • PROMPT versioning • http://protege.stanford.edu McGuinness JCIS June 11, 2005

  42. Cerebra • Commercial OWL DL tools • Cerebra Construct • Ontology engineering and external source mapping within a familiar MS Visio framework • Cerebra Server • Commercial-grade inference platform, providing industry-standard query, high-performance inference and management capabilities with emphasis on scalability, availability, robustness and 100% correctness. Based on initial work from University of Manchester • CEREBRA Repository • Collaborative object repository for metadata, vocabulary, security and policy management • http://www.cerebra.com McGuinness JCIS June 11, 2005

  43. Medius / Sandpiper • Visual Ontology Modeler • UML-based modeling tool • Add-in to Rational Rose • Produces RDF, OWL, DAML, UML, … • Medius Knowledge Brokering Suite • OMG Ontology Definition Metamodel (ODM) • http://www.sandsoft.com McGuinness JCIS June 11, 2005

  44. SWOOP • Hypermedia-based open source ontology editor • Includes an interface to the Pellet OWL DL reasoner • http://www.mindswap.org/2004/SWOOP/ McGuinness JCIS June 11, 2005

  45. Pellet • Open source Java OWL DL reasoner • API supports • Species validation (OWL Lite/DL/Full) • Consistency checking • Classification • Entailment • Query • http://www.mindswap.org/2003/pellet/ McGuinness JCIS June 11, 2005

  46. SNOBASE • Ontology management system from IBM • Ontology Directory • Query capability • JOBC API • http://www.alphaworks.ibm.com/tech/snobase McGuinness JCIS June 11, 2005

  47. Jena • Open source API from HP Labs UK • Most popular Java API • Parser • Serializer • Extra features • Persistence (RDBMS) • Query (RDQL) • Reasoning • Rule Engine • http://www.hpl.hp.com/semweb/ McGuinness JCIS June 11, 2005

  48. SweetRules • Open source rule framework • Executes SWRL and RuleML using a variety of rule engines • CommonRules • XSB Prolog • JESS • Jena 2 • Translates between various rule formats • http://sweetrules.projects.semwebcentral.org McGuinness JCIS June 11, 2005

  49. SemWebCentral • Open source software development site dedicated to the Semantic Web • 79+ projects • 257+ developers • Select projects by workflow or other attributes • http://semwebcentral.org McGuinness JCIS June 11, 2005

  50. Other Tool Resources • Dave Beckett’s RDF Resource Guide • http://www.ilrt.bris.ac.uk/discovery/rdf/resources/ • Michael Denny’s Survey of Ontology Tools • http://www.xml.com/pub/a/2004/07/14/onto.html McGuinness JCIS June 11, 2005

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