1. 1 Building Intelligent Systems Marie desJardins Tim Finin Anupam Joshi Yun Peng Yelena Yesha September 2004 tell register tell register

2. UMBC an Honors University in Maryland 2 Overview Faculty: Finin, Yesha, Joshi, Peng, desJardins Faculty: Finin, Yesha, Joshi, Peng, desJardins Students: ~12 PhD, ~12 MS, ~5 undergrad Students: ~12 PhD, ~12 MS, ~5 undergrad Labs: Labs: Ebiquity: agents and the semantic web for open, heterogeneous, distributed systems Ebiquity: agents and the semantic web for open, heterogeneous, distributed systems MAPLE: MultiAgent Planning and LEarning MAPLE: MultiAgent Planning and LEarning Funding Funding Current: DARPA (DAML), NSF (three ITRs, …), Intelligence community, NASA, NIST, Industry, … Current: DARPA (DAML), NSF (three ITRs, …), Intelligence community, NASA, NIST, Industry, … Recent: DARPA (CoABS, GENOA II) Recent: DARPA (CoABS, GENOA II)

3. UMBC an Honors University in Maryland 3 ebiquity Group Intelligent Information Systems Networking & Systems Security AIDB semantic web mobility pervasive computing trust privacy assurance web services user modeling wireless data management service discovery and composition multiagent systems machine learning policy KR

4. UMBC an Honors University in Maryland 4 ebiquity Group Intelligent Information Systems Networking & Systems Security AIDB semantic web mobility pervasive computing trust privacy assurance web services user modeling wireless data management service discovery and composition multiagent systems machine learning policy KR Building intelligent systems in open, heterogeneous, dynamic, distributed environments

5. UMBC an Honors University in Maryland 5 Some Relevant Project Areas (1) Agents and the semantic web (2) Policies for controlling autonomous agents (3) Context aware pervasive computing (4) TIVO for mobile computing (5) Trust in information systems (6) Large scale semantic web systems

6. UMBC an Honors University in Maryland 6 (1) The Celebrity Couple SemanticWebSemanticWebSoftwareAgentsSoftwareAgents In 2002, Geek Gossip gushed “The semantic web will provide content for internet agents, and agents will make the semantic web “come alive”. Looks like a match made in Heaven!”

7. UMBC an Honors University in Maryland 7 (1) Trading Agents We’ve built an agent-based environment inspired by TAC, the Trading Agent Competition We’ve built an agent-based environment inspired by TAC, the Trading Agent Competition TAC is a forum for dynamic trading agent research with games run in the last five years TAC is a forum for dynamic trading agent research with games run in the last five years TAC Classic involves a travel procurement, with agents buying and selling goods for clients and scored on the cost and clients’ preferences for trips assembled. TAC Classic involves a travel procurement, with agents buying and selling goods for clients and scored on the cost and clients’ preferences for trips assembled. TAC is organized around a central auction server TAC is organized around a central auction server Our goal was to open up the system, allowing peer-to- peer communication among agents as well various kinds of mediator, auction, discovery, service provider agents … and to see how well the semantic web works as the common knowledge infrastructure. Our goal was to open up the system, allowing peer-to- peer communication among agents as well various kinds of mediator, auction, discovery, service provider agents … and to see how well the semantic web works as the common knowledge infrastructure.

8. UMBC an Honors University in Maryland 8 TAGA: Travel Agent Game in Agentcities http://taga.umbc.edu/ Technologies FIPA (JADE, April Agent Platform) Semantic Web (RDF, OWL) Web (SOAP,WSDL,DAML-S) Internet (Java Web Start ) Features Open Market Framework Auction Services OWL message content OWL Ontologies Global Agent Community Motivation Market dynamics Auction theory (TAC) Semantic web Agent collaboration (FIPA & Agentcities) Travel Agents Auction Service Agent Customer Agent Bulletin Board Agent Market Oversight Agent Request Direct Buy Report Direct Buy Transactions Bid CFP Report Auction Transactions Report Travel Package Report Contract Proposal Web Service Agents Ontologies http://taga.umbc.edu/ontologies/ travel.owl – travel concepts travel.owl – travel concepts fipaowl.owl – FIPA content lang. fipaowl.owl – FIPA content lang. auction.owl – auction services auction.owl – auction services tagaql.owl – query language tagaql.owl – query language FIPA platform infrastructure services, including directory facilitators enhanced to use OWL-S for service discovery Owl for representation and reasoning Owl for service descriptions Owl for negotiation Owl as a content language Owl for publishing communicative acts Owl for contract enforcement Owl for modeling trust Owl for authorization policies Owl for protocol description

9. UMBC an Honors University in Maryland 9 What we learned OWL is a good KR language for a reasonably sophisticated MAS OWL is a good KR language for a reasonably sophisticated MAS Integrates well with FIPA standards Integrates well with FIPA standards OWL made it easy to mix content from different ontologies unambiguously OWL made it easy to mix content from different ontologies unambiguously Supporting partial understanding & extensibility Supporting partial understanding & extensibility The use of OWL supported web integration The use of OWL supported web integration Using information published on web pages and integrating with web services via WSDL and SOAP Using information published on web pages and integrating with web services via WSDL and SOAP OWL has limitations: no rules, no default reasoning, graph semantics, … OWL has limitations: no rules, no default reasoning, graph semantics, … Some of which are being addressed Some of which are being addressed

10. UMBC an Honors University in Maryland 10 (2) It’s policies all the way down 1 A robot may not injure a human being, or, through inaction, allow a human being to come to harm. 2 A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. 3 A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. - Handbook of Robotics, 56th Edition, 2058 A.D.

11. UMBC an Honors University in Maryland 11 (2) It’s policies all the way down In Asimov’s world, the robots didn’t always strictly follow their policies Unlike traditional “hard coded” rules like DB access control & OS file permissions Autonomous agents need policies as “norms of behavior” to be followed to be good citizens So, it’s natural to worry about … How agents governed by multiple policies can resolve conflicts among them How to deal with failure to follow policies – sanctions, reputation, etc. Whether policy engineering will be any easier than software engineering 1 A robot may not injure a human being, or, through inaction, allow a human being to come to harm. 2 A robot must obey the orders given it by hu- man beings except where such orders would conflict with the First Law. 3 A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. - Handbook of Robotics, 56th Edition, 2058 A.D.

12. UMBC an Honors University in Maryland 12 Our Approach Policies are useful at virtually all levels Policies are useful at virtually all levels OS, networking, data management, applications OS, networking, data management, applications Declarative policies guide the behavior of entities in open, distributed environments Declarative policies guide the behavior of entities in open, distributed environments Positive & negative authorizations & obligations Positive & negative authorizations & obligations Focused on domain actions Focused on domain actions Policies are based on attributes of the action (and its actor and target) and the general context – not just on their identity of the actor Policies are based on attributes of the action (and its actor and target) and the general context – not just on their identity of the actor

13. UMBC an Honors University in Maryland 13 Rei Policy Language Developed several versions of Rei, a policy specification language, encoded in (1) Prolog, (2) RDFS, (3) OWL Developed several versions of Rei, a policy specification language, encoded in (1) Prolog, (2) RDFS, (3) OWL Used to model different kinds of policies Used to model different kinds of policies Authorization for services Authorization for services Privacy in pervasive computing and the web Privacy in pervasive computing and the web Conversations between agents Conversations between agents Team formation, collaboration & maintenance Team formation, collaboration & maintenance The OWL grounding enables policies that reason over SW descriptions of actions, agents, targets and context The OWL grounding enables policies that reason over SW descriptions of actions, agents, targets and context

14. UMBC an Honors University in Maryland 14 Rei Policy Language Rei is a declarative policy language for describing policies over actions Rei is a declarative policy language for describing policies over actions Reasons over domain dependent information Reasons over domain dependent information Currently represented in OWL + logical variables Currently represented in OWL + logical variables Based on deontic concepts Based on deontic concepts Permission, Prohibition, Obligation, Dispensation Permission, Prohibition, Obligation, Dispensation Models speech acts Models speech acts Delegation, Revocation, Request, Cancel Delegation, Revocation, Request, Cancel Meta policies Meta policies Priority, modality preference Priority, modality preference Policy engineering tools Policy engineering tools Reasoner, IDE for Rei policies in Eclipse Reasoner, IDE for Rei policies in Eclipse

15. UMBC an Honors University in Maryland 15 Applications – past, present & future Coordinating access in supply chain management system Coordinating access in supply chain management system Authorization policies in a pervasive computing environment Authorization policies in a pervasive computing environment Policies for team formation, collaboration, information flow in multi-agent systems Policies for team formation, collaboration, information flow in multi-agent systems Security in semantic web services Security in semantic web services Privacy and trust on the Internet Privacy and trust on the Internet Privacy in pervasive computing environments Privacy in pervasive computing environments 1999 2002 2003 … 2004 …

16. UMBC an Honors University in Maryland 16 What we learned Declarative policies can be used to model security, trust and privacy constraints Declarative policies can be used to model security, trust and privacy constraints Reasonably expressive policy languages can be encoded on OWL Reasonably expressive policy languages can be encoded on OWL This enables policies to depend on attributes and context information available on the semantic web This enables policies to depend on attributes and context information available on the semantic web Policies are applicable at almost every level of the stack, from systems and networking to multiagent applications. Policies are applicable at almost every level of the stack, from systems and networking to multiagent applications.

17. UMBC an Honors University in Maryland 17 (3) A Love Triangle? Semantic Web Software Agents Pervasive Computing Even matches made in Heaven don’t always work out as planned.

18. UMBC an Honors University in Maryland 18 (3) Pervasive Computing “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it ” – Mark Weiser Think: writing, central heating, electric lighting, water services, … Not: taking your laptop to the beach, or immersing yourself into a virtual reality

19. UMBC an Honors University in Maryland 19

20. UMBC an Honors University in Maryland 20

21. UMBC an Honors University in Maryland 21 Pervasive environments for the Military

22. UMBC an Honors University in Maryland 22 This is a challenging environment While devices are getting smaller, cheaper and more powerful, they still have severe limitations. Battery, memory, computation, connection, bandwidth Battery, memory, computation, connection, bandwidth Each as limited sensors and perspective Each as limited sensors and perspective The environment is inherently dynamic with serendipitous connections and unknown entities The environment is inherently dynamic with serendipitous connections and unknown entities This makes security and trust important This makes security and trust important MANETS (mobile ad hoc networks) underlie pervasive infrastructures like Bluetooth MANETS (mobile ad hoc networks) underlie pervasive infrastructures like Bluetooth It’s autonomous agents all the way down It’s autonomous agents all the way down Security and privacy is a special concern Security and privacy is a special concern Warfighters and agents must control how information about them is collected and used Warfighters and agents must control how information about them is collected and used

23. UMBC an Honors University in Maryland 23 Representing and reasoning about context CoBrA: a broker centric agent architecture for supporting pervasive context-aware systems Using SW ontologies for context modeling and reasoning about devices, space, time, people, preferences, meetings, etc. Using SW ontologies for context modeling and reasoning about devices, space, time, people, preferences, meetings, etc. Using logical inference to interpret context and to detect and resolve inconsistent knowledge Using logical inference to interpret context and to detect and resolve inconsistent knowledge Allowing users to define policies controlling how information about them is used and shared Allowing users to define policies controlling how information about them is used and shared

24. UMBC an Honors University in Maryland 24 A Bird’s Eye View of CoBrA

25. UMBC an Honors University in Maryland 25 SOUPA Ontology provides common vocabulary

26. UMBC an Honors University in Maryland 26 A Simple Spatial Model of UMBC

27. UMBC an Honors University in Maryland 27 Where’s Harry?

28. UMBC an Honors University in Maryland 28 Detecting Inconsistencies

29. UMBC an Honors University in Maryland 29 Powerful AI tools DL reasoning over privacy policies DL reasoning over privacy policies Abductive reasoning Abductive reasoning Cobra uses a simple form of abductive reasoning to keep track of assumptions underlying it’s conclusions Cobra uses a simple form of abductive reasoning to keep track of assumptions underlying it’s conclusions e.g.: A person’s at X if his cell phone’s at X assuming it’s not lost This allows Cobra to find consistent and plausible explanations for the data it sees. This allows Cobra to find consistent and plausible explanations for the data it sees. Argumentation Argumentation Agents can question or challenge each others beliefs, engaging in simple argumentation dialogs Agents can question or challenge each others beliefs, engaging in simple argumentation dialogs e.g.: giving the assumptions and provenance underlying a belief An agent may choose not to adopt another’s belief based on the provenance of the data An agent may choose not to adopt another’s belief based on the provenance of the data

30. UMBC an Honors University in Maryland 30 Privacy Protection in CoBrA Users define policies to permit or prohibit the sharing of their information Users define policies to permit or prohibit the sharing of their information Policies are provided by personal agents or published on web pages Policies are provided by personal agents or published on web pages and use the SOUPA ontologies as well as other SW assertions (e.g., FOAF, schedules) and use the SOUPA ontologies as well as other SW assertions (e.g., FOAF, schedules) The context broker follows user defined policies when sharing information, unless contravened by higher policies The context broker follows user defined policies when sharing information, unless contravened by higher policies

31. UMBC an Honors University in Maryland 31 The SOUPA Policy Ontology

32. UMBC an Honors University in Maryland 32 Policy Reasoning Use Case The speaker doesn’t want others to know the specific room that he’s in, but is willing for others to know he’s on campus The speaker doesn’t want others to know the specific room that he’s in, but is willing for others to know he’s on campus He defines the following privacy policy He defines the following privacy policy Share my location with a granularity >= “State” Share my location with a granularity >= “State” The broker The broker isLocated(US) => Yes! isLocated(US) => Yes! isLocated(Maryland) => Yes! isLocated(Maryland) => Yes! isLocated(UMBC) => Uncertain.. isLocated(UMBC) => Uncertain.. isLocated(ITE-RM210) => Uncertain.. isLocated(ITE-RM210) => Uncertain..

33. UMBC an Honors University in Maryland 33 What we learned FIPA and OWL were good for integrating disparate components, even on cell phones! FIPA and OWL were good for integrating disparate components, even on cell phones! OWL made it easy to mix content from different ontologies unambiguously OWL made it easy to mix content from different ontologies unambiguously OWL made it easy to take advantage of information published in XML on the web OWL made it easy to take advantage of information published in XML on the web e.g., foaf information, privacy policy e.g., foaf information, privacy policy Powerful AI tools add value Powerful AI tools add value DL reasoning, abduction, argumentation DL reasoning, abduction, argumentation

34. UMBC an Honors University in Maryland 34 (4) TIVO for Mobile Computing A mobile computing vision and a problem Devices “broadcast” information and service descriptions via short-range RF (802.11, Bluetooth, UWB, etc.) Devices “broadcast” information and service descriptions via short-range RF (802.11, Bluetooth, UWB, etc.) As people and their devices move, they can access this data, but only while it’s in range As people and their devices move, they can access this data, but only while it’s in range The data may be out of range when it’s needed The data may be out of range when it’s needed Devices must anticipate their information need so they can cache data when it’s available Devices must anticipate their information need so they can cache data when it’s available Based on user model, preferences, schedule, context, trust, … Based on user model, preferences, schedule, context, trust, … Compute a dynamic utility function to create a “semantic” cache replacement algorithm Compute a dynamic utility function to create a “semantic” cache replacement algorithm

35. UMBC an Honors University in Maryland 35 MoGATU’s distributed belief model MoGATU is a data management module for MANETs MoGATU is a data management module for MANETs Devices send queries to peers Devices send queries to peers Ask its vicinity for reputation of untrusted peers that responded -- trust a device if trusted before or if enough trusted peers trust it Ask its vicinity for reputation of untrusted peers that responded -- trust a device if trusted before or if enough trusted peers trust it Use answers from (recommended to be) trusted peers to determine answer Use answers from (recommended to be) trusted peers to determine answer Update reputation/trust level for all responding devices Update reputation/trust level for all responding devices Trust level increases for devices giving what becomes final answer Trust level increases for devices giving what becomes final answer Trust level decreases for devices giving “wrong” answer Trust level decreases for devices giving “wrong” answer Each devices builds a ring of trust… Each devices builds a ring of trust…

36. UMBC an Honors University in Maryland 36 A: Where is Bob? C: I know where Bob is. D: I know where Bob is. B: I know where Bob is.

37. UMBC an Honors University in Maryland 37 A: D, where is Bob? A: C, where is Bob? A: B, where is Bob?

38. UMBC an Honors University in Maryland 38 C: A, Bob is at work. D: A, Bob is home. B: A, Bob is home.

39. UMBC an Honors University in Maryland 39 A: B: Bob at home, C: Bob at work, D: Bob at home A: I have enough trust in D. What about B and C?

40. UMBC an Honors University in Maryland 40 A: Do you trust C? C: I always do. D: I don’t. B: I am not sure. E: I don’t. F: I do. A: I don’t care what C says. I don’t know enough about B, but I trust D, E, and F. Together, they don’t trust C, so won’t I.

41. UMBC an Honors University in Maryland 41 A: Do you trust B? C: I never do. D: I am not sure. B: I do. E: I do. F: I am not sure. A: I don’t care what B says. I don’t trust C, but I trust D, E, and F. Together, they trust B a little, so will I.

42. UMBC an Honors University in Maryland 42 A: I trust B and D, both say Bob is home… A: Increase trust in D. A: Decrease trust in C. A: Increase trust in B. A: Bob is home!

43. UMBC an Honors University in Maryland 43 What we learned OWL was a good language for capturing user profiles and the simple BDI models we needed OWL was a good language for capturing user profiles and the simple BDI models we needed Any of several simple trust models increase the accuracy of information Any of several simple trust models increase the accuracy of information Designing a good trust model depends on the MANET assumptions Designing a good trust model depends on the MANET assumptions As well as the level of cooperation and honesty As well as the level of cooperation and honesty Trading reputation information boosts the performance of the algorithms Trading reputation information boosts the performance of the algorithms

44. 44 Knowledge Discovery in the Semantic Web (4) (4) SEMDIS NSF award ITR-IIS-0325464 U. Georgia, Sheth, Arpinar, Kochut, Miller NSF award ITR-IIS-0325172 UMBC, Joshi, Yesha, Finin June 2004 Objective Design, prototype and evaluate a system supporting the discovery, indexing and querying of complex semantic relationships in the Semantic Web. The system maintains and utilizes trust and provenance information to enhance the relationship discovery. Approach Knowledge representation systems reason over sem- antic web content discovered on the web which is re- duced to triples that can be efficiently stored and pro- cessed in relational databases. Trust models and heuristics guide the formation of conclusions Broader impacts Techniques and prototypes developed can be applied to a range of problems, including discovering new connections and relations in scientific information and homeland security. SWETO is large ontology covering several test-bed domains. It is pop-ulated with 800K instances and 1.M relations extracted from heterogeneous Web sources. SWETO was developed using Semagix Freedom system. An experimental algorithm has been developed to integrate and rank discovered relationships. Referencefoaf:Agent rdf:Statement selects JustificationTrust Belief Association contains foaf:Document rdf:Resource foaf:page DocumentRelation xsd:real [0,1] AssociationConnective confidence connective source A “web of belief” model and associated ontology is used to represent, integrate, and evaluate conclusions drawn from the large volume of heterogeneous assertions found in the data. http://lsdis.cs.uga.edu/Projects/SemDis http://semdis.umbc.edu/ A. Joshi L. Ding H. Chen P. Kolari F. Perich Y. Yesha J. Golbeck J. Hendler Kagal sink hub source island FininA. Joshi Ding Chen Kagal Perich Golbeck’s Trust Network DBLP Network FOAF Network A. Sheth M. P. Singh Y. Peng 6 1 5 1 28 T. Finin mapTo knows

45. UMBC an Honors University in Maryland 45 Need a slide or two here….

46. 46 Research support was provided by NSF, award NSF-ITR-IIS-0326460, PI Tim Finin, UMBC. (5) SPIRE Semantic Prototypes in Research Ecoinfomatics Approach We are building prototype tools and applications that demonstrate how semantic web technology supports infor- mation discovery, integration and sharing in scientific com- munities. The National Biological Information Infrastructure (NBII) and Invasive Species Forecasting System (ISFS) pro- vide requirements and serve as testbeds for our prototypes. Significant Results SWOOGLE - a search engine for the semantic web. MoaM (Meal of a Meal) - Given a species list, infer a food web. Photostuff - annotate regions of a picture with OWL. SWOOP - the first ontology editor written specifically for OWL. Ontologies for ecological interaction, and observation data. Food web visualization and analysis tools that are driven by OWL ontologies and instance data. CRISIS CAT - an RDF based catalog of Invasive Species resources in California. Coordination with USGS, NASA, EPA, GBIF, and the Intergovernmental, Interagency Cooperation on Ecoinformatics. Broader Impacts Enable knowledge from one community to be effectively used by another. Harness the power of the citizen scientist. (The majority of invasives are discovered by amateurs.) Integrate research and education in the classroom. Research Team UMBC ebiquity (Finin)UC Davis ICE (Quinn) UMBC GEST Center (Sachs)RMBL PEaCE (Martinez) UMD MINDSWAP (Hendler)NASA GSFC (Schnase) The RMBL team expresses food webs in OWL using an ontology for eco- logical interaction they have constructed in coordination with other ecolo- gists. The OWL model drives the simulation and visualization. Spatial distribution of exotic plants at the Cerro Grande fire site. The statistical techniques used to generate these maps do not take trophic data as input. Yet. Swoogle is a crawler based search and retrieval system for semantic web doc- uments (SWDs) in RDF and OWL. It discovers SWDs and computes their metadata and relations, and stores them in an IR system. Users can search for ontologies or instance data, and hits are ranked according to our Ontology Rank algorithm. Invasive species do more economic damage to the U.S. every year that all other natural disasters combined. Above: plants, animals, and a virus. An ontology (found via Swoogle) is loaded into Photostuff to mark up regions of a field photograph. The NBII California Information Node (CAIN), maintained by UC Davis, is a jumping off point to broader NBII deployment. Coming Soon ELVIS – an end to end application that starts with a location and produces a model of its food web. The Pond Project - a junior high school classroom activity to monitor the health of local ecosystems. Enhanced tools. Spire is a distributed, interdisciplinary research project exploring how semantic web technology supports information discov- ery, integration, and sharing in scientific communities. We are building prototype tools and applications for inclusion in the National Biological Information Infrastructure (NBII), with a focus on the early detection and warning of invasive species. Meal of a Meal (after Friend of a Friend). We know Fish 1 eats Plant 1. We then infer that Fish 1 may also eat the taxonomic siblings of Plant 1: Plants 2 and 3. Similarly, we infer that the taxonomic siblings of Fish 1 - Fishes 2 and 3 - may eat Plant 1. UMBC AN HONORS UNIVERSITY IN MARYLAND

47. 47 Swoogle is a crawler based search & retrieval system for semantic web documents (SWDs) in RDF, Owl and DAML. It discovers SWDs and computes their metadata and relations, and stores them in an IR system. Contributors include Tim Finin, Anupam Joshi, Yun Peng, R. Scott Cost, Jim Mayfield, Joel Sachs, Pavan Reddivari, Vishal Doshi, Rong Pan, Li Ding, and Drew Ogle. Partial research support was provided by DARPA contract F30602-00-0591 and by NSF by awards NSF-ITR-IIS-0326460 and NSF-ITR-IDM-0219649. 20 May 2004. http://swoogle.umbc.edu/ Ontology discovery Ontology discovery Web interface Web interface DB SWD crawler SWD crawler We b Ontology Analyzer Ontology Analyzer Ontology Agents Ontology discovery Ontology discovery Google Apache/ Tomcat php, myAdmin mySQL Jena IR engine SIRE Web services Agent services cached files Focused Crawler APIs Swoogle uses two kinds of crawlers to discover semantic web documents and several analysis agents to compute metadata and relations among documents and ontologies. Metadata is stored in a relational DBMS. SWD Rank A SWD’s rank is a function of its type (SWO/SWI) and the rank and types of the documents to which it’s related. SWD Properties SWOs SWIs HTML documents Images CGI scripts Audio files Video files The web, like Gaul, is divided into three parts: the regular web (e.g. HTML), Seman- tic Web Ontologies (SWOs), and Semantic Web Instance files (SWIs) SWD = SWO + SWI Binary: R(D1,D2) IM: D1 owl:imports D2 IMstar: transitive closure of IM EX: D1 extends D2 by defining classes or properties subsumed by D2’s PV: owl:priorVersion & subproperties TM: D1 uses terms from D2 IN: D1 uses individual defined in D2 MAP: D1 maps some of its terms to D2’s SIM: D1 & D2 are similar EQ: D1 & D2 are identical EQV: D1 & D2 have the same triples Ternary: R(D1,D2,D3) MP3: D1 maps a term from D2 to D3 using owl:sameClass, etc. SWD Relations Language and level; encoding, number of triples, defined classes, defined properties, & defined individuals; type (SWO, SWI); form (RSS, FOAF, P3P, …); rank; weight; annotations; … Swoogle puts documents into a character n- gram based IR engine to compute document similarity and do retrieval from queries Swoogle has metadata on classes, properties and individuals from ~240,000 SWDs SWD IR Engine

48. UMBC an Honors University in Maryland 48

49. UMBC an Honors University in Maryland 49 Uncertainty in Ontology Engineering: A Bayesian Perspective Yun Peng, Zhongli Ding, Rong Pan Department of Computer Science and Electrical engineering University of Maryland Baltimore County ypeng@umbc.edu

50. UMBC an Honors University in Maryland 50 Uncertainty in ontology engineering –In representing/modeling the domain Degree of inclusion Overlap rather than inclusion Uncertain information often available –In reasoning How close a description D is to its most specific subsumer? Noisy data: leads to over generalization in subsumptions Uncertain input: –In mapping concepts from one ontology to another Similarity between concepts may not be described logically Mappings are hardly 1-to-1 Uncertainty becomes more prevalent in web environment –One ontology may import other ontologies –Competing ontologies for the same or overlapped domain Motivations

51. UMBC an Honors University in Maryland 51 BN1 –OWL-BN translation By a set of translation rules and procedures Maintain OWL semantics Ontology reasoning by probabilistic inference in BN Overview of The Approach onto1 P-onto1 Probabilistic ontological information onto2 P-onto2 BN2 Probabilistic annotation OWL-BN translation concept mapping –Ontology mapping A parsimonious set of links Capture similarity between concepts by joint distribution Mapping as evidential reasoning

52. UMBC an Honors University in Maryland 52 Translated BN will preserve –Semantics of the original ontology –Encoded probability distributions among relevant variables Encoding probabilities in OWL ontologies –Define new OWL classes for prior and conditional probabilities Structural translation: a set of rules –Class hierarchy: set theoretic approach Each OWL class translated to a BN node Arcs from super to subclass nodes –Logical relations (equivalence, disjoint, union, intersection...) Control nodes whose CPT realize logical relations –Properties (ongoing) OWL-BN Translation

53. UMBC an Honors University in Maryland 53 Structural Translation Set theoretic approach –Each OWL class is considered a set of objects/instances –Each class is defined as a node in BN –An arc in BN goes from a superset to a subset –Consistent with OWL semantics RDF Triples: (Human rdf:type owl:Class) (Human rdfs:subClassOf Animal) (Human rdfs:subClassOf Biped) Translated to BN

54. UMBC an Honors University in Maryland 54 Structural Translation Logical relations –Some can be encoded by CPT (e.g.. Man = Human∩Male) –Others can be realized by adding control nodes Man  Human Woman  Human Human = Man  Woman Man ∩ Woman =  auxiliary node: Human_1 Control nodes: Disjoint, Equivalent

55. UMBC an Honors University in Maryland 55 Constructing CPT Imported Probability information is not in the form of CPT Assign initial CPT to the translated structure by some default rules Iteratively modify CPT to fit imported probabilities while setting control nodes to true. –IPFP (Iterative Proportional Fitting Procedure) To find Q(x) that fit Q(E 1 ), … Q(E k ) to the given P(x) Q 0 (x) = P(x); then repeat Q i (x) = Q i-1 (x) Q(E j )/ Q i-1 (E j ) until converging Q  (x) is an I-projection of P (x) on Q(E 1 ), … Q(E k ) (minimizing Kullback-Leibler distance to P) –Modified IPFP for BN

56. UMBC an Honors University in Maryland 56 Example

57. UMBC an Honors University in Maryland 57 Formalize the notion of mapping Mapping involving multiple concepts Reasoning under ontology mapping Ontology Mapping

58. UMBC an Honors University in Maryland 58 Simplest case: Map concept E 1 in Onto 1 to E 2 in Onto 2 –How similar between E 1 and E 2 –How to impose belief (distribution) of E 1 to Onto 2 Cannot do it by simple Bayesian conditioning P(x| E 1 ) = Σ E 2 P(x| E 2 )P(E 2 | E 1 ) similarity(E 1, E 2 ) –Onto 1 and Onto 2 have different probability space (Q and P) Q(E 1 ) ≠ P(E 1 ) New distribution, given E 1 in Onto 1 : P * (x) ≠ΣP (x|E 1 )P(E 1 ) –similarity(E 1, E 2 ) also needs to be formalized Formalize The Notion of Mapping

59. UMBC an Honors University in Maryland 59 Jeffrey’s rule –Conditioning cross prob. spaces –P * (x) = Σ P (x|E 1 )Q(E 1 ) –P * is an I-projection of P (x) on Q(E 1 ) (minimizing Kullback- Leibler distance to P) –Update P to P * by applying Q(E 1 ) as soft evidence in BN similarity(E 1, E 2 ) –Represented as joint prob. R(E 1, E 2 ) in another space R –Can be obtained by learning or from user Define map(E 1, E 2 ) = Formalize The Notion of Mapping

60. UMBC an Honors University in Maryland 60 Reasoning With map(E 1, E 2 ) Q BN 1 E1E1 P BN 2 E2E2 R E 1 E 2 Applying Q(E 1 ) as soft evidence to update R to R* by Jeffrey’s rule Using similarity(E 1, E 2 ): R*(E 2 ) = R*(E 1, E 2 )/R*(E 1 ) Applying R*(E 2 ) as soft evidence to update P to P* by Jeffrey’s rule

61. UMBC an Honors University in Maryland 61 Multiple mappings –One node in BN1 can map to all nodes in BN2 –Most mappings with little similarity –Which of them can be removed without affecting the overall Similarity measure: –Jaccard-coefficient: sim(E 1, E 2 ) = P(E 1  E 2 )/R(E 1  E 2 ) –A generalization of subsumption –Remove those mappings with very small sim value Question: can we further remove other mappings –Utilizing knowledge in BN Mapping Reduction

62. UMBC an Honors University in Maryland 62 Current focuses: –OWL-BN translation: properties –Algorithms for ontology reasoning as probabilistic inference over translated BN –Ontology mapping: mapping reduction Prototyping and experiments Issues –Complexity –How to get these probabilities Current focuses

63. UMBC an Honors University in Maryland 63

64. UMBC an Honors University in Maryland 64 Marie’s slides go here

65. UMBC an Honors University in Maryland 65 backup

66. UMBC an Honors University in Maryland 66 Example: Security and Trust for Semantic Web Services Semantic web services are web services described using OWL-S Semantic web services are web services described using OWL-S Policy-based security infrastructure Policy-based security infrastructure Advantages of using policies: Advantages of using policies: Expressive -- can be over descriptions of requester, service & context Expressive -- can be over descriptions of requester, service & context Authorization: Rules for access control Authorization: Rules for access control Privacy: Rules for protecting information Privacy: Rules for protecting information Confidentiality: Cryptographic characteristics of information exchanged Confidentiality: Cryptographic characteristics of information exchanged Policies + Semantic Web Services

67. UMBC an Honors University in Maryland 67 Example policies Authorization Authorization Policy 1: Stock service not accessible after market closes Policy 1: Stock service not accessible after market closes Policy 2: Only LAIT lab members who are Ph.D. students can use the LAIT lab laser printer Policy 2: Only LAIT lab members who are Ph.D. students can use the LAIT lab laser printer Privacy/Confidentiality Privacy/Confidentiality Policy 3: Do not disclose my my SSN Policy 3: Do not disclose my my SSN Policy 4: Do not disclose my home address or facts from which it could be easily discovered Policy 4: Do not disclose my home address or facts from which it could be easily discovered Policy 5: Do not use a service that doesn’t encrypt all input/output Policy 5: Do not use a service that doesn’t encrypt all input/output Policy 6: Use only those services that required an SSN if it is encrypted Policy 6: Use only those services that required an SSN if it is encrypted

68. UMBC an Honors University in Maryland 68 Example Mary is looking for a reservation service Mary is looking for a reservation service foaf description foaf description Confidentiality policy Confidentiality policy BravoAir is a reservation service BravoAir is a reservation service OWL-S description OWL-S description Authorization policy Authorization policy Only users belonging to the same project as John can access the service

69. UMBC an Honors University in Maryland 69 Mary Mary Smith Mary Smith <foaf:title>Ms</foaf:title><foaf:firstName>Mary</foaf:firstName><foaf:surname>Smith</foaf:surname> </foaf:Person></rdf:RDF>

70. UMBC an Honors University in Maryland 70 Bravo Policy <constraint:SimpleConstraint rdf:about="&bravo- policy;GetJohnProject" constraint:subject="&john;John" constraint:predicate="&foaf;currentProject" constraint:object="&bravo-policy;var2"/> <constraint:SimpleConstraint rdf:about="&bravo- policy;SameProjectAsJohn" constraint:subject="&bravo-policy;var1" constraint:predicate="&foaf;currentProject" constraint:object="&bravo-policy;var2"/> <constraint:And rdf:about="&bravo- policy;AndCondition1" constraint:first="&bravo-policy;GetJohnProject" constraint:second="&bravo- policy;SameProjectAsJohn"/> ………

71. UMBC an Honors University in Maryland 71 How it works Bravo Service OWL-S Desc URL to foaf desc + query request BravoAir Web service Mary Matchmaker + Reasoner

72. UMBC an Honors University in Maryland 72 How it works Mary’s query = Bravo Service ? YES Extract Bravo’s policy Does Mary meets Bravo’s policy ? Authorization enforcement complete Mary BravoAir Web service <constraint:SimpleConstraint rdf:about="&bravo-policy;SameProjectAsJohn" constraint:subject="&bravo-policy;var1" constraint:predicate="&foaf;currentProject" constraint:object="&bravo-policy;var2"/> Is the constraint true when var2 = http://www.somewebsite.com/SWS-Project.rdf var1 = http://www.cs.umbc.edu/~lkagal1/rei/examples/sws- sec/MaryProfile.rdf <constraint:SimpleConstraint rdf:about = "&bravo-policy;GetJohnProject” constraint:subject="&john;John" constraint:predicate="&foaf;currentProject" constraint:object="&bravo-policy;var2"/> var2 = http://www.somewebsite.com/SWS-Project.rdf

73. UMBC an Honors University in Maryland 73 Experimental results We are investigating the design of algorithms for these data management problems in MANETs via simulations for varying parameters We are investigating the design of algorithms for these data management problems in MANETs via simulations for varying parameters For example For example Answer accuracy vs. trust learning functions Answer accuracy vs. trust learning functions Answer accuracy vs. accuracy merging Functions Answer accuracy vs. accuracy merging Functions Distrust convergence vs. dishonesty level Distrust convergence vs. dishonesty level

74. UMBC an Honors University in Maryland 74 Answer Accuracy vs. Trust Learning Functions The effects of trust learning functions with initial optimistic trust in environments varying in dishonesty. The effects of trust learning functions with initial optimistic trust in environments varying in dishonesty. The results are shown for ∆ ++, ∆ --, ∆ s, ∆ f, ∆ f+, ∆ f-, and ∆ exp learning functions. The results are shown for ∆ ++, ∆ --, ∆ s, ∆ f, ∆ f+, ∆ f-, and ∆ exp learning functions. For more results, see http://ebiquity.org/ For more results, see http://ebiquity.org/