Скачать презентацию Building Intelligent Systems tell Marie des Jardins Tim Скачать презентацию Building Intelligent Systems tell Marie des Jardins Tim

d72d66d1a93501a60ad78a2a64354602.ppt

  • Количество слайдов: 65

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

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

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

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

Some Relevant Project Areas (1) Agents and the semantic web (2) Policies for controlling 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 UMBC an Honors University in Maryland 5

(1) The Celebrity Couple Semantic Web Software Agents In 2002, Geek Gossip gushed “The (1) The Celebrity Couple Semantic Web Software Agents 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!” UMBC an Honors University in Maryland 6

(1) Trading Agents n We’ve built an agent-based environment inspired by TAC, the Trading (1) Trading Agents n We’ve built an agent-based environment inspired by TAC, the Trading Agent Competition n n 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 is organized around a central auction server Our goal was to open up the system, allowing peer-topeer 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. UMBC an Honors University in Maryland 7

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

What we learned n OWL is a good KR language for a reasonably sophisticated What we learned n OWL is a good KR language for a reasonably sophisticated MAS n n OWL made it easy to mix content from different ontologies unambiguously n n Supporting partial understanding & extensibility The use of OWL supported web integration n n Integrates well with FIPA standards 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, … n Some of which are being addressed UMBC an Honors University in Maryland 9

(2) It’s policies all the way down 1 A robot may not injure a (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, 56 th Edition, 2058 A. D. UMBC an Honors University in Maryland 10

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

Our Approach n Policies n are useful at virtually all levels OS, networking, data Our Approach n Policies n are useful at virtually all levels OS, networking, data management, applications n Declarative policies guide the behavior of entities in open, distributed environments n Positive & negative authorizations & obligations n Focused on domain actions n 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 UMBC an Honors University in Maryland 12

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

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

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

What we learned n n Declarative policies can be used to model security, trust What we learned n n Declarative policies can be used to model security, trust and privacy constraints Reasonably expressive policy languages can be encoded on OWL 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. UMBC an Honors University in Maryland 16

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

(3) Pervasive Computing “The most profound technologies are those that disappear. They weave themselves (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 UMBC an Honors University in Maryland 18

UMBC an Honors University in Maryland 19 UMBC an Honors University in Maryland 19

UMBC an Honors University in Maryland 20 UMBC an Honors University in Maryland 20

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

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

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

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

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

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

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

Detecting Inconsistencies UMBC an Honors University in Maryland 28 Detecting Inconsistencies UMBC an Honors University in Maryland 28

Powerful AI tools n DL reasoning over privacy policies n Abductive reasoning n Cobra Powerful AI tools n DL reasoning over privacy policies n Abductive reasoning n 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 n This allows Cobra to find consistent and plausible explanations for the data it sees. n Argumentation n Agents can question or challenge each others beliefs, engaging in simple argumentation dialogs e. g. : giving the assumptions and provenance underlying a belief n An agent may choose not to adopt another’s belief based on the provenance of the data UMBC an Honors University in Maryland 29

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

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

Policy Reasoning Use Case n The speaker doesn’t want others to know the specific Policy Reasoning Use Case n 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 n He defines the following privacy policy n Share n The my location with a granularity >= “State” broker n is. Located(US) => Yes! n is. Located(Maryland) => Yes! n is. Located(UMBC) => Uncertain. . n is. Located(ITE-RM 210) => Uncertain. . UMBC an Honors University in Maryland 32

What we learned n n FIPA and OWL were good for integrating disparate components, What we learned n n 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 take advantage of information published in XML on the web n e. g. , foaf information, privacy policy Powerful AI tools add value n DL reasoning, abduction, argumentation UMBC an Honors University in Maryland 33

(4) TIVO for Mobile Computing A mobile computing vision and a problem n Devices (4) TIVO for Mobile Computing A mobile computing vision and a problem n Devices “broadcast” information and service descriptions via short-range RF (802. 11, Bluetooth, UWB, etc. ) n As people and their devices move, they can access this data, but only while it’s in range n The data may be out of range when it’s needed n Devices must anticipate their information need so they can cache data when it’s available n Based on user model, preferences, schedule, context, trust, … n Compute a dynamic utility function to create a “semantic” cache replacement algorithm UMBC an Honors University in Maryland 34

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

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

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

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

A: B: Bob at home, C: Bob at work, D: Bob at home A: 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? UMBC an Honors University in Maryland 39

B: I am not sure. C: I always do. F: I do. E: I B: I am not sure. C: I always do. F: I do. E: I don’t. A: Do you trust C? D: I don’t. UMBC an Honors University in Maryland 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. 40

B: I do. C: I never do. F: I am not sure. E: I B: I do. C: I never do. F: I am not sure. E: I do. A: Do you trust B? D: I am not sure. UMBC an Honors University in Maryland 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. 41

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

What we learned n n n OWL was a good language for capturing user What we learned n n n 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 n Designing a good trust model depends on the MANET assumptions n As well as the level of cooperation and honesty Trading reputation information boosts the performance of the algorithms UMBC an Honors University in Maryland 43

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

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

Approach We are building prototype tools and applications that demonstrate how semantic web technology Approach We are building prototype tools and applications that demonstrate how semantic web technology supports information discovery, integration and sharing in scientific communities. The National Biological Information Infrastructure (NBII) and Invasive Species Forecasting System (ISFS) provide requirements and serve as testbeds for our prototypes. (5) SPIRE Invasive species do more economic damage to the U. S. every year that all other natural disasters combined. Above: plants, animals, and a virus. Semantic Prototypes in Research Ecoinfomatics Spire is a distributed, interdisciplinary research project exploring how semantic web technology supports information discovery, 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. The RMBL team expresses food webs in OWL using an ontology for ecological interaction they have constructed in coordination with other ecologists. The OWL model drives the simulation and visualization. Significant Results SWOOGLE - a search engine for the semantic web. Moa. M (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. • • • Swoogle is a crawler based search and retrieval system for semantic web documents (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. 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. 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. 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. UMBC AN HONORS UNIVERSITY IN MARYLAND 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. Research Team UMBC ebiquity (Finin) UMBC GEST Center (Sachs) UMD MINDSWAP (Hendler) UC Davis ICE (Quinn) RMBL PEa. CE (Martinez) NASA GSFC (Schnase) 46 Research support was provided by NSF, award NSF-ITR-IIS-0326460, PI Tim Finin, UMBC.

CGI scripts SWOs Video files HTML documents SWIs Web services Swoogle is a crawler CGI scripts SWOs Video files HTML documents SWIs Web services 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. SWD Properties Language and level; encoding, number of triples, defined classes, defined properties, & defined individuals; type (SWO, SWI); form (RSS, FOAF, P 3 P, …); rank; weight; annotations; … Web interface Ontology Analyzer Jena Ontology Agents APIs Apache/ Tomcat my. SQL Images Agent services php, my. Admin IR engine SIRE SWD = SWO + SWI Focused Crawler SWD crawler DB Audio files Web 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) Jena Ontology discovery Ontology Google discovery cached files SWD Relations Binary: R(D 1, D 2) • IM: D 1 owl: imports D 2 • IMstar: transitive closure of IM • EX: D 1 extends D 2 by defining classes or properties subsumed by D 2’s • PV: owl: prior. Version & subproperties • TM: D 1 uses terms from D 2 • IN: D 1 uses individual defined in D 2 • MAP: D 1 maps some of its terms to D 2’s • SIM: D 1 & D 2 are similar • EQ: D 1 & D 2 are identical • EQV: D 1 & D 2 have the same triples Ternary: R(D 1, D 2, D 3) • MP 3: D 1 maps a term from D 2 to D 3 using owl: same. Class, etc. 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. http: //swoogle. umbc. edu/ Swoogle has metadata on classes, properties and individuals from ~240, 000 SWDs 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 IR Engine Swoogle puts documents into a character ngram based IR engine to compute document similarity and do retrieval from queries 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 F 30602 -00 -0591 and by NSF by awards NSF-ITR-IIS-0326460 and NSF-ITR-IDM-0219649. 20 May 2004. 47

UMBC an Honors University in Maryland 48 UMBC an Honors University in Maryland 48

Uncertainty in Ontology Engineering: A Bayesian Perspective Yun Peng, Zhongli Ding, Rong Pan Department 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 [email protected] edu UMBC an Honors University in Maryland 49

Motivations • Uncertainty in ontology engineering – In representing/modeling the domain • Degree of Motivations • 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 UMBC an Honors University in Maryland 50

Overview of The Approach onto 2 onto 1 Probabilistic ontological information P-onto 2 P-onto Overview of The Approach onto 2 onto 1 Probabilistic ontological information P-onto 2 P-onto 1 Probabilistic annotation BN 1 BN 2 OWL-BN translation – OWL-BN translation concept mapping • By a set of translation rules and procedures • Maintain OWL semantics • Ontology reasoning by probabilistic inference in BN UMBC an Honors University in Maryland Probabilistic ontological information – Ontology mapping • A parsimonious set of links • Capture similarity between concepts by joint distribution • Mapping as evidential reasoning 51

OWL-BN Translation • Translated BN will preserve – Semantics of the original ontology – OWL-BN Translation • 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) UMBC an Honors University in Maryland 52

Structural Translation • Set theoretic approach – – Each OWL class is considered a 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: sub. Class. Of Animal) (Human rdfs: sub. Class. Of Biped) Translated to BN UMBC an Honors University in Maryland 53

Structural Translation • Logical relations – Some can be encoded by CPT (e. g. 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 = Man Woman Man ∩ Woman = auxiliary node: Human_1 Control nodes: Disjoint, Equivalent UMBC an Honors University in Maryland 54

Constructing CPT • Imported Probability information is not in the form of CPT • 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(Ek) to the given P(x) • Q 0(x) = P(x); then repeat Qi(x) = Qi-1(x) Q(Ej)/ Qi-1(Ej) until converging • Q (x) is an I-projection of P (x) on Q(E 1), … Q(Ek) (minimizing Kullback-Leibler distance to P) – Modified IPFP for BN UMBC an Honors University in Maryland 55

Example UMBC an Honors University in Maryland 56 Example UMBC an Honors University in Maryland 56

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

Formalize The Notion of Mapping • Simplest case: Map concept E 1 in Onto Formalize The Notion of Mapping • 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) = ΣE 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 UMBC an Honors University in Maryland 58

Formalize The Notion of Mapping • Jeffrey’s rule – Conditioning cross prob. spaces – Formalize The Notion of Mapping • Jeffrey’s rule – Conditioning cross prob. spaces – P*(x) =ΣP (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) = UMBC an Honors University in Maryland 59

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

Mapping Reduction • Multiple mappings – One node in BN 1 can map to Mapping Reduction • Multiple mappings – One node in BN 1 can map to all nodes in BN 2 – 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 UMBC an Honors University in Maryland 61

Current focuses • Current focuses: – OWL-BN translation: properties – Algorithms for ontology reasoning Current focuses • 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 UMBC an Honors University in Maryland 62

UMBC an Honors University in Maryland 63 UMBC an Honors University in Maryland 63

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

backup UMBC an Honors University in Maryland 65 backup UMBC an Honors University in Maryland 65