Towards Linked Ontologies and Data on the Semantic Web


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  • Let’s have a closer look how this is done
  • Let’s see an example. This slide shows Bob’s schedule ontology. Bob’s has a private activity that he is unwilling to share with the public. Let’s see what might happen when his ontology is queried, such as by other people’s software agent, for his availability. While Bob knows he has a date at 3pm by himself, he won’t reveal its details to everyone. When being asked if he is busy, his ontology, in fact, the software for answering queries about ontology, may answer “Yes”. However, when being asked about if he has a date, the software may answer “no” or “unknown” to protect Bob’s privacy. In this work, we assume the answer is unknown. This would not let a reasoner software to lie in protecting privacy. Similarly, when being asked if Bob’ has dinner from 3pm to 5pm, the software may also answer “unknown”, since this piece of information is not in the ontology. We call this incomplete knowledge. Please note that since both hidden knowledge and incomplete knowledge are answered with unknown, a querying agent will not be able to distinguish which case is what. We may use this fact to protect privacy from harmful querying.
  • Any question so far?
  • Now let’s see how could we make not only ontologies, but also structured data available on the web that can be linked to each.
  • We will discuss three problems in dealing with structured data.
  • Li has presented this on Oct 3, 2008.
  • This shows… There a couple more interesting use cases for the semantic history extension, please see the demo site for details.
  • I have also involved in a several other projects that turn data into SMW format, or to export SMW data into another forms…
  • Goal: to have “little semantics” usable by everyone
  • Towards Linked Ontologies and Data on the Semantic Web

    1. 1. Towards Linked Ontologies and Data on the Semantic Web Jie Bao , Rensselaer Polytechnic Institute, Oct 1st, 2009
    2. 2. Outline <ul><li>Personal Background & Research Overview </li></ul><ul><li>Linked Ontologies: a modular ontology approach </li></ul><ul><li>Linked Data: a semantic wiki based approach </li></ul><ul><li>Ongoing/Future Work </li></ul>
    3. 3. Personal Background
    4. 4. Research Overview /48
    5. 5. Research Overview They are all about the power of linking…
    6. 6. Research Overview Linked Ontologies Linked Data
    7. 7. <ul><li>Part I </li></ul><ul><li>Linked Ontologies </li></ul>
    8. 8. Roadmap Semantic Web
    9. 9. Why not owl:imports? owl:imports
    10. 10. Why not owl:imports? owl:imports (Alice, 2001) (Bob, 2009) Analogy: Paper Writing in the OWL fashion Recent development in Web… In this paper, we extend the algorithm A proposed by (Alice,2001) … Recent development in algorithms … In this paper, we present two algorithms A and B to …
    11. 11. Why not owl:imports? (Bob, 2009) Analogy: Paper Writing in the OWL fashion In this paper, we extend the algorithm A proposed by In this paper, we present two algorithms A and B to … Recent development in algorithms … Recent development in Web…
    12. 12. P-DL: Importing akin to Citation P-DL imports (Alice, 2001) (Bob, 2009) Paper(Bob2009) {Bob2009} ⊑  propose.{AlgC} {AlgC} ⊑  extend.{ AlgA } {Bob2009} ⊑  cite.RecentDev Paper(Alice2001) {Alice2001} ⊑  propose.{ AlgA } {Alice2001} ⊑  propose.{AlgB} {Alice2001} ⊑  cite.RecentDev [ISWC 2006; ASWC 2006; AAAI 2007, AAAI 2008] P-DL = Package-based Description Logics
    13. 13. P-DL: Semantics People Animals O 1 O 2 Only “relevant” local domains are connected Expressions are interpreted in local domains (e.g., neg)
    14. 14. Modular ontology languages: Comparison Distributed DL E-Connection P-DL What can you import? Nothing Concept name, Nominal name Concept , Role, and Nominal name What can you do with the imported names syntactically? (bridge rules between concepts and between roles) Use it in the range of a (link) role Free use, except that imported roles can not be used in role inclusions What is the result semantically? Decidability with BR between concepts. Decidability Decidability , Transitive Reusability, Preservation of Unsatisfiability
    15. 15. Roadmap Semantic Web
    16. 16. Reasoning with P-DL <ul><li>Major Considerations </li></ul>[WI 2006]
    17. 17. P-DL Federated Reasoning Dog Dog ⊑ Animal P 2 P 1 What is a “Dog”? “ Dog” is a type of “Animal”
    18. 18. P-DL Federated Reasoning messages
    19. 19. Roadmap Semantic Web
    20. 20. Privacy Matters <ul><li>A reasoner may pose queries to another reasoner (of a remote ontology) </li></ul><ul><li>However, not everything is public. </li></ul>[WI 2007]
    21. 21. Private Knowledge Locally visible : Has date Q: Has date? A : Unknown Bob’ schedule ontology Q: Busy? A : Yes Q: Has dinner? A : Unknown indistinguishable
    22. 22. Privacy-Preserving Reasoners More informative More general Dummy Reasoner “ Combina-tion Safe” Reasoner Naive Reasoner Gives information Always answer “U” ? Always answer faithfully Safety scope All ontologies Ontologies that have no hidden knowledge
    23. 23. Privacy-Preserving Reasoners <ul><li>Reasoner I: iff a statement is hidden, answer “unknown”. </li></ul><ul><li>Reasoner II: iff a statement may be used in inferring a hidden statement, answer “unknown”. </li></ul><ul><li>Reasoner III: it will NOT give more knowledge (except the public knowledge) about the signature of the hidden KB. </li></ul><ul><ul><li>Using the notion of “Locality” in DL. </li></ul></ul>(See [WI 2007] for formal definitions) More informative More general
    24. 24. <ul><li>Part II </li></ul><ul><li>Linked Data </li></ul><ul><li>- A semantic wiki based approach </li></ul>
    25. 25.
    26. 26. Roadmap Semantic Web
    27. 27. Why Semantic Wiki <ul><li>Linked data ready </li></ul><ul><li>Support both structured and unstructured data. </li></ul><ul><li>Simple syntax, Low learning curve </li></ul><ul><li>Inherent collaboration support </li></ul><ul><li>Browser-based, Cross-platform </li></ul>Semantic Wiki
    28. 28. Example: Semantic History <ul><li>Turn wiki revision history into semantic data </li></ul><ul><li>Demo site: </li></ul>
    29. 29. Semantic History (v1) <ul><li>Expose history in </li></ul><ul><li>RDF using PML </li></ul><ul><li>Query data and its </li></ul><ul><li>provenance metadata </li></ul> RDF/XML data displayed in Tabulator
    30. 30. Semantic History (v2) [SDOW 2009]
    31. 31. <ul><li>{{ SH_Triple |…}} </li></ul><ul><li>{{ SH_Add |…}} </li></ul><ul><li>{{ SH_Delete |…}} </li></ul>User makes edits SMW The Semantic History (SH) extension captures edit actions SH Templates (customizable) Revision history SH Templates generates semantic descriptions of revision history Triple Representation of history User Triplified revision history are added to and managed by the SMW Applications use the triplified revision history for various purposes /48
    32. 32. Semantic History: Example (Provenance tracking) Who has changed the first name of James Hendler?
    33. 33. Semantic History (v2): Example (Statistics and Visualization)
    34. 34. Other Data Generation Services A few other SMW extensions I have worked on:
    35. 35. Roadmap Semantic Web
    36. 36. Concept Modeling Person Name Role Alias Affiliation Project Name Member Issue ID Project Assignee Jim Hendler Professor hasRole Person rdfs:subClassOf Alice John hasUncle Alice Bob John isParentOf isBrotherOf RDF Modeling Relational Modeling Rule Modeling
    37. 37. Rules: Logic Programs RightHanded(x):-Person(x), not LeftHanded(x). {{LP Rule |body= 1::Person; 1:not:LeftHanded |head= RightHanded }} Example : every person is by default right-handed, unless that person is known to be left-handed: See details at: [ACITA 2009]
    38. 38. Rules: Integrity constraints <ul><li>IC describes errors in a dataset </li></ul>Example : notOK :- Person(x), not HasGender (x,y) See details at:
    39. 39. Roadmap Semantic Web
    40. 40. Semantic Wiki: Application Workbench [ASWC 2009]
    41. 41. Case Study: CNL Wiki <ul><li>A Wiki-based ontology editor </li></ul><ul><li>Supports ontology representation in Controlled Natural Languages (CNL). </li></ul>http:// [CNL 2009]
    42. 42. Case Study: CNL Wiki Class(Rabbit partial intersectionOf(animal restriction(eat someValuesFrom(FreshVegetable))) OWL: “Rabbit is Animal and eats some fresh vegetable” Us wiki templates to create OWL meta-model extensions for SMW Form-based editing interface associated with templates
    43. 43. Case Study: CNL Wiki
    44. 44. Case Study: RPI Map /48
    45. 45. <ul><li>Ongoing/Future Work </li></ul>
    46. 46. Some Other Recent Research Activities <ul><li>TAMI & AIR (2009) </li></ul><ul><ul><li>Policy testbed and AIR formulation(With Li Ding and Ankesh Khandelwal) </li></ul></ul><ul><li>OWL Working Group (2008-2009) </li></ul><ul><ul><li>Authored rdf:plainLiteral and OWL Quick Reference Guide </li></ul></ul><ul><li>Integrity Constraint and Closed World Reasoning (2008-2009) </li></ul><ul><ul><li>IC language for Semantic Web(with Jiao Tao) </li></ul></ul><ul><li>INDUS (2003-2006) </li></ul><ul><ul><li>Semantic data integration in the bioinformatics domain </li></ul></ul>
    47. 47. Some Ongoing/Planned Work <ul><li>Linked Data </li></ul><ul><ul><li>Turn government data into wiki data /RDF </li></ul></ul><ul><ul><li>Semantic search with RDFa / SMW </li></ul></ul><ul><li>Policy and Trust </li></ul><ul><ul><li>Privacy-preserving query with SMW </li></ul></ul><ul><ul><li>Information accountability and access control in SMW </li></ul></ul><ul><ul><li>Trust computation based on semantic history </li></ul></ul><ul><li>Web-scale Computing </li></ul><ul><ul><li>Scalable “is-a” reasoner </li></ul></ul><ul><ul><li>OWL-RL reasoner </li></ul></ul>
    48. 48. Long-term Goal Email Picture Phone Call Shopping music video Web Server Farm
    49. 49. Conclusion <ul><li>“ A little semantics goes a long way” </li></ul><ul><li>a.k.a. Hendler Hypothesis </li></ul>
    50. 50. Acknowledgements <ul><li>Work at ISU: Vasant Honavar (Ph.D. advisor), Giora Slutzki, George Voutsadakis, Doina Caragea </li></ul><ul><li>Work at RPI: James Hendler (supervisor), Deborah L. McGuinness, Peter Fox, Li Ding, Zhenning Shangguan, Rui Huang, Jin Guang Zheng, Ankesh Khandelwal </li></ul><ul><li>Funding support from NSF, NIH, USDA, Army Research Laboratory and DARPA. </li></ul>
    51. 51. References (modular ontologies) <ul><li>Bao, J., Voutsadakis, G., Slutzki, G. Honavar, V. Package based Description Logics. Book Chapter in Ontology Modularization, pages 349-371. Berlin: Springer, 2009 </li></ul><ul><li>Bao, J., Voutsadakis. G., Slutzki, G. and Honavar, V. On the Decidability of Role Mappings Between Modular Ontologies. In AAAI 2008, pages 400-405. </li></ul><ul><li>Bao, J., Slutzki, G., and Honavar, V. Privacy-Preserving Reasoning on the Semantic Web . In Web Intelligence 2007. ( Slides) pages 791-797. </li></ul><ul><li>Bao, J., Slutzki, G., and Honavar, V. A Semantic Importing Approach to Knowledge Reuse from Multiple Ontologies . In AAAI 2007, pages 1304-1309. </li></ul><ul><li>Bao. J., Caragea, D., and Honavar, V. A Tableau-based Federated Reasoning Algorithm for Modular Ontologies , In Web Intelligence 2006, pages 404-410. </li></ul><ul><li>Bao. J., Caragea, D., and Honavar, V. On the Semantics of Linking and Importing in Modular Ontologies . In International Semantic Web Conference 2006, LNCS 4273, pages 72-86. </li></ul><ul><li>Bao. J., Caragea, D., and Honavar, V. Modular Ontologies - A Formal Investigation of Semantics and Expressivity . In Asian Semantic Web Conference 2006, LNCS 4185, pp. 616-631. </li></ul>
    52. 52. References (semantic wikis) <ul><li>Bao, J. Ding, L., Huang, R., Smart, R., Braines, D., Jones, G. A Semantic Wiki based Light-Weight Web Application Model, In Proceedings of the 4th Asian Semantic Web Conference, pp. In Press, 2009 . </li></ul><ul><li>Bao, J., Ding, L., & McGuinness, D.L. Semantic History: Towards Modeling and Publishing Changes of Online Semantic Data, In The 2nd Social Data on the Web workshop (SDoW2009), In Process. </li></ul><ul><li>Bao, J., Ding, L., Smart, R., Braines, D., & Jones, G. Rule Modeling using Semantic MediaWiki, In 2nd Annual Conference of the International Technology Alliance, pp. In Process, 2009 </li></ul><ul><li>Bao, J., Smart, P., Braines, D., & Shadbolt, N. A Controlled Natural Language Interface for Semantic Media Wiki Using the Rabbit Language. In Workshop on Controlled Natural Language (CNL) 2009. </li></ul><ul><li>Bao, J. and Hendler, J., Ding., L. Knowledge representation and query in semantic mediawiki: A formal study. In Tetherless World Constellation (RPI) Technical Report, No TW-2008-42,, 2008. </li></ul><ul><li>Bao, J., Ding, L., McGuinness, D., Hendler, J. Towards Social Webtops Using Semantic Wiki, In International Semantic Web Conference (ISWC), Poster Track, 2008 </li></ul>/48
    53. 53. Backup
    54. 54. P-DL: Additional Properties Dog Dog⊑ Animal Pet Animal O 1 O 2 O 3 ⊑ ⊑
    55. 55. P-DL Federated Reasoning Bob2009 Alice2001 cite
    56. 56. Privacy-Preserving Reasoning <ul><ul><li>Protect hidden knowledge as if it is incomplete knowledge </li></ul></ul><ul><ul><ul><li>Both are answered “unknown” </li></ul></ul></ul><ul><ul><li>But when to answer “unknown”? </li></ul></ul><ul><ul><ul><li>People may combine “safe” answers to infer hidden knowledge </li></ul></ul></ul>
    57. 57. Privacy-Preserving Reasoning Queries Yes Unknown
    58. 58. Semantic MediaWiki (SMW) <ul><li>It is the most popular semantic wiki system extending MediaWiki </li></ul>Mediawiki: What you edit what you see
    59. 59. Semantic MediaWiki SMW: What you edit (Modeling Script) what you see To author knowledge typed link (property)
    60. 60. Semantic MediaWiki SMW: What you edit (Querying Script) what you see To retrieve knowledge
    61. 61. SMW Semantics and Complexity (Theory) See proofs in [TW-2008-42] Recall that L  NL  P  NP SMW RDF Modeling Language Translatable into positive logic programs NL-complete <ul><li>NP-complete; </li></ul><ul><li>P-complete for grounded graph [Bruijn and Heymans 2007] </li></ul>Query language <ul><li>Translatable into positive logic programs </li></ul><ul><li>P-complete; </li></ul><ul><li>In L without subqueries </li></ul>(SPARQL) P-complete [Perez et al 2006]
    62. 62. Formalize SMW Query (Theory) <ul><li>{{#ask: [[Category:A]][[p3::category:B]] or </li></ul><ul><li>[[ p.p1.p2 :: </li></ul><ul><li><q>[[Category:D]] or [[p1::<q>[[SomePage]]</q>]]</q> || !v || <q>[[Category:E]]</q> </li></ul><ul><li>]] </li></ul><ul><li>}} </li></ul><ul><li>_result(x) :- _tmp0(x). </li></ul><ul><li>_tmp0(x) :- A(x), p3(x,x0), x0=category:B . </li></ul><ul><li>_tmp0(x) :- p(x,x2), p1(x2,x3), p2(x3,x1), _tmp9(x1). </li></ul><ul><li>_tmp9(x1) :- _tmp12(x1). </li></ul><ul><li>_tmp12(x1) :- D(x1). </li></ul><ul><li>_tmp12(x1) :- p1(x1,x4), x4=SomePage. </li></ul><ul><li>_tmp9(x1) :- x1!=v. </li></ul><ul><li>_tmp9(x1) :- E(x1). </li></ul>
    63. 63. Case Study: RPI Map