DOCSLIB.ORG

Formal Description of Web Services for Expressive Matchmaking

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Formal Description of Web Services for Expressive Matchmaking Zur Erlangung des akademischen Grades eines Doktors der Wirtschaftswissenschaften (Dr. rer. pol.) von der Fakult¨at f¨ur Wirtschaftswissenschaften der Universit¨at Karlsruhe (TH) genehmigte Dissertation. Formal Description of Web Services for Expressive Matchmaking Dipl.-Inform. Sudhir Agarwal Tag der m¨undlichen Pr¨ufung: 04.05.2007 Referent: Prof. Dr. Rudi Studer Koreferent: Prof. Dr. Christof Weinhardt Pr¨ufer: Prof. Dr. Andreas Oberweis 2007 Karlsruhe ii Abstract Web services are programmable interfaces that can be invoked via standardized Web communication protocols, such as HTTP, in order to embed the functionality they offer in a software system. The necessity of outsourcing parts of business processes together with the wide acceptance of the Service Oriented Architecture (SOA) will lead to a large number of Web services. Such a big market of Web services demands automatic methods for finding suitable Web services. Existing industrial standards for finding Web services are keyword based and hence require a lot of manual effort to select Web services with desired functionality and desired interface. Industrial standard UDDI allow only keyword based search that considers a fixed set of properties and methods known from research in the field of Semantic Web Services base on interoperable descriptions of Web service interfaces. However, in real business scenarios criteria for searching suitable Web services are much richer than just the types of input and output paremeters. In this work, our aim is to develop automatic methods for finding Web services based on the functional and non-functional properties of Web services. To be able to develop automatic reasoning algorithms about the functional and non-functional properties of Web services, they must be described formally. So, we first develop a formalism that allows to describe the involved resources, the credentials of the involved actors and the dynamic behavior of web services including access control policies in a unifying way. We identify that in order to support a broader spectrum of use cases, two types of matchmaking approaches are needed, namely goal based and equivalence based. Goal based matchmaking deals with specifying constraints on desired Web services and then finding the Web services that satisfy the constraints, whereas equivalence based match- making supports the use cases in which an existing Web service should be replaced by another Web service without changing the overall behaviour of the system it is incorpo- rated in. In this work, we develop an expressive formalism to specify desired constraints on a Web service and develop a sound and complete algorithm to check whether a Web service description fulfills a goal. Furthermore, we develop the notion of equivalence of Web service functionalities and show how two web service descriptions can be checked for equivalence. Finally, we present the implementation of our system and conclude by discussing some problems that may be of future research interest. iii iv Contents I Introduction 1 1 Motivation 3 1.1 Introduction to Web Services ......................... 3 1.1.1 Web Service Description Language (WSDL) ............. 5 1.1.2 Simple Object Access Protocol (SOAP) ............... 8 1.1.3 Using Web Services .......................... 8 1.2 B2B Integration and EAI with Web Services ................. 9 1.3 Web Service Markets .............................. 11 1.4 UDDI, Current Standard for Web Service Discovery ............ 14 1.4.1 Limitations of UDDI .......................... 17 1.5 Limitations of Existing Semantic Matchmaking Approaches ........ 18 1.6 Outline ..................................... 19 2 Requirements 23 2.1 Requirements for Matchmaking ........................ 24 2.1.1 Constraints on Resources ....................... 25 2.1.2 Security and Trust Constraints .................... 26 2.1.3 Constraints on Behavior ........................ 26 2.1.4 Combination of different Types of Constraints ............ 27 2.2 Requirements for Semantic Description of Web Services .......... 28 2.2.1 Requirements for Resource Descriptions ............... 29 2.2.2 Requirements for Credentials ..................... 30 2.2.3 Requirements for Behavior Description ................ 30 2.3 Conclusion ................................... 34 II Formal Description of Web Services 35 3 Modeling Resources 39 3.1 Introduction to Description Logics ...................... 39 3.2 Basic Description Logic ALC ......................... 40 3.2.1 Description logics with Concrete Domains .............. 42 v Contents 3.3 More Expressive Description Logic SHOIN (D) .............. 43 3.3.1 The Family of OWL Languages .................... 45 3.4 Query Languages ................................ 46 3.5 Rule Languages for OWL ........................... 50 3.6 Modeling Resources .............................. 51 4 Modeling Credentials 53 4.1 Role Based Access Control ........................... 53 4.2 Authorization based Access Control with SPKI/SDSI ........... 56 4.3 Modeling Credentials .............................. 61 4.3.1 Scenario ................................. 64 4.3.2 Specification of Certification Policies ................. 64 4.3.3 Users .................................. 67 4.4 Conclusion ................................... 69 5 Modeling Behavior 71 5.1 Introduction to π-calculus ........................... 71 5.1.1 Relation to Other Formalisms ..................... 71 5.1.2 Syntax .................................. 74 5.1.3 Semantics ................................ 77 5.1.4 The Polyadic π-calculus ........................ 78 5.2 Modeling Behavior ............................... 81 5.2.1 Message Types for Communication Operations ........... 82 5.2.2 Introducing Local Operations to Model Updates .......... 83 5.2.3 Integrating Access Control Policies as Conditions .......... 84 5.3 Related Work .................................. 87 5.3.1 OWL-S ................................. 87 5.3.2 WSMO ................................. 91 5.3.3 WSDL-S ................................. 96 5.3.4 BPEL4WS ............................... 97 5.3.5 Access Control Related Work ..................... 97 5.4 Conclusion ................................... 99 III Semantic Matchmaking of Web Services 101 6 Goal Specification 105 6.1 Modal Logics for Processes ..........................106 6.2 Early Logics ...................................107 6.3 Introduction to mu-calculi ...........................108 6.3.1 Syntax and Semantics of µ-calculus ..................110 vi Contents 6.3.2 Examples of Some Common Patterns ................111 6.4 Specification of Hybrid Formulas with Logic B ...............112 6.5 Related Work ..................................114 6.6 Conclusion ...................................117 7 Goal based Matchmaking 119 7.1 Introduction to Model Checking ........................119 7.2 Matchmaking Algorithm ............................121 7.2.1 Complexity ...............................127 7.3 Related Work ..................................127 7.3.1 OWL-S Matchmaker ..........................127 7.3.2 WSMO Discovery ...........................129 7.3.3 Other Matchmaking Approaches ...................130 7.4 Conclusion ...................................130 8 Simulation based Matchmaking 133 8.1 Simulation of Resources ............................133 8.2 Comparing Credentials of Agents .......................136 8.3 Comparing Behavior of Agents ........................137 8.3.1 Trace Equivalence: A First Attempt .................137 8.3.2 Strong Bisimilarity ...........................138 8.3.3 Weak Bisimilarity ...........................139 8.4 Algorithm ....................................142 8.5 Conclusion ...................................145 IV Implementation and Applications 147 9 Implementation 151 9.1 KASWS Web Service Management API ...................151 9.1.1 Generating Domain APIs from Ontology ...............152 9.1.2 Modeling the Knowledge Base with Description Logics .......156 9.2 KASWS Reasoning API ............................158 9.3 Bootstrapping ..................................160 9.4 User Interface ..................................162 10 Applications 165 10.1 Knowledge Integration in SemIPort ......................165 10.1.1 Sample Scenario ............................166 10.1.2 Integration of Heterogeneous Information Sources ..........167 10.2 Modeling Auctions in SESAM .........................169 vii Contents 10.2.1 Introduction ..............................170 10.2.2 Reference Model for Market Mechanisms ..............170 10.2.3 Formal Description of Reference Model for Market Mechanisms . 173 10.3 Further Potential Applications ........................175 10.3.1 Verification of Business Processes ...................175 10.3.2 Reasoning about Market Mechanisms ................175 10.3.3 Model Driven Architecture in Software Development ........176 10.4 Conclusion ...................................176 11 Conclusion and Outlook 177 11.1 Contribution ..................................177 11.2 Open Problems Concerning Our Work ....................180 11.2.1 Annotating Web Services with Logic B ................180 11.2.2 Semi-Automatic Support for Describing Web Services and Constraints181 11.2.3 Efficient Matchmaking .........................181 11.3 Further Research Ideas .............................182 11.3.1 Automatic Composition ........................182 11.3.2 Preference Based Ranking .......................183 viii List of Figures 1.1 Using Web Services in an Application .................... 8 1.2 B2B Integration
Load more

Recommended publications
  • A Logical Framework for Modularity of Ontologies∗
    A Logical Framework for Modularity of Ontologies∗ Bernardo Cuenca Grau, Ian Horrocks, Yevgeny Kazakov and Ulrike Sattler The University of Manchester School of Computer Science Manchester, M13 9PL, UK {bcg, horrocks, ykazakov, sattler }@cs.man.ac.uk Abstract ontology itself by possibly reusing the meaning of external symbols. Hence by merging an ontology with external on- Modularity is a key requirement for collaborative tologies we import the meaning of its external symbols to de- ontology engineering and for distributed ontology fine the meaning of its local symbols. reuse on the Web. Modern ontology languages, To make this idea work, we need to impose certain con- such as OWL, are logic-based, and thus a useful straints on the usage of the external signature: in particular, notion of modularity needs to take the semantics of merging ontologies should be “safe” in the sense that they ontologies and their implications into account. We do not produce unexpected results such as new inconsisten- propose a logic-based notion of modularity that al- cies or subsumptions between imported symbols. To achieve lows the modeler to specify the external signature this kind of safety, we use the notion of conservative exten- of their ontology, whose symbols are assumed to sions to define modularity of ontologies, and then prove that be defined in some other ontology. We define two a property of some ontologies, called locality, can be used restrictions on the usage of the external signature, to achieve modularity. More precisely, we define two no- a syntactic and a slightly less restrictive, seman- tions of locality for SHIQ TBoxes: (i) a tractable syntac- tic one, each of which is decidable and guarantees tic one which can be used to provide guidance in ontology a certain kind of “black-box” behavior, which en- editing tools, and (ii) a more general semantic one which can ables the controlled merging of ontologies.
    [Show full text]
  • Deciding Semantic Matching of Stateless Services∗
    Deciding Semantic Matching of Stateless Services∗ Duncan Hull†, Evgeny Zolin†, Andrey Bovykin‡, Ian Horrocks†, Ulrike Sattler†, and Robert Stevens† School of Computer Science, Department of Computer Science, † ‡ University of Manchester, UK University of Liverpool, UK Abstract specifying automated reasoning algorithms for such stateful service descriptions is basically impossible in the presence We present a novel approach to describe and reason about stateless information processing services. It can of any expressive ontology (Baader et al. 2005). Stateless- be seen as an extension of standard descriptions which ness implies that we do not need to formulate pre- and post- makes explicit the relationship between inputs and out- conditions since our services do not change the world. puts and takes into account OWL ontologies to fix the The question we are interested in here is how to help the meaning of the terms used in a service description. This biologist to find a service he or she is looking for, i.e., a allows us to define a notion of matching between ser- service that works with inputs and outputs the biologist can vices which yields high precision and recall for service provide/accept, and that provides the required functionality. location. We explain why matching is decidable, and The growing number of publicly available biomedical web provide biomedical example services to illustrate the utility of our approach. services, 3000 as of February 2006, required better match- ing techniques to locate services. Thus, we are concerned with the question of how to describe a service request Q Introduction and service advertisements Si such that the notion of a ser- Understanding the data generated from genome sequenc- vice S matching the request Q can be defined in a “useful” ing projects like the Human Genome Project is recognised way.
    [Show full text]
  • Ontology-Based Methods for Analyzing Life Science Data
    Habilitation a` Diriger des Recherches pr´esent´ee par Olivier Dameron Ontology-based methods for analyzing life science data Soutenue publiquement le 11 janvier 2016 devant le jury compos´ede Anita Burgun Professeur, Universit´eRen´eDescartes Paris Examinatrice Marie-Dominique Devignes Charg´eede recherches CNRS, LORIA Nancy Examinatrice Michel Dumontier Associate professor, Stanford University USA Rapporteur Christine Froidevaux Professeur, Universit´eParis Sud Rapporteure Fabien Gandon Directeur de recherches, Inria Sophia-Antipolis Rapporteur Anne Siegel Directrice de recherches CNRS, IRISA Rennes Examinatrice Alexandre Termier Professeur, Universit´ede Rennes 1 Examinateur 2 Contents 1 Introduction 9 1.1 Context ......................................... 10 1.2 Challenges . 11 1.3 Summary of the contributions . 14 1.4 Organization of the manuscript . 18 2 Reasoning based on hierarchies 21 2.1 Principle......................................... 21 2.1.1 RDF for describing data . 21 2.1.2 RDFS for describing types . 24 2.1.3 RDFS entailments . 26 2.1.4 Typical uses of RDFS entailments in life science . 26 2.1.5 Synthesis . 30 2.2 Case study: integrating diseases and pathways . 31 2.2.1 Context . 31 2.2.2 Objective . 32 2.2.3 Linking pathways and diseases using GO, KO and SNOMED-CT . 32 2.2.4 Querying associated diseases and pathways . 33 2.3 Methodology: Web services composition . 39 2.3.1 Context . 39 2.3.2 Objective . 40 2.3.3 Semantic compatibility of services parameters . 40 2.3.4 Algorithm for pairing services parameters . 40 2.4 Application: ontology-based query expansion with GO2PUB . 43 2.4.1 Context . 43 2.4.2 Objective .
    [Show full text]
  • Description Logics
    Description Logics Franz Baader1, Ian Horrocks2, and Ulrike Sattler2 1 Institut f¨urTheoretische Informatik, TU Dresden, Germany [email protected] 2 Department of Computer Science, University of Manchester, UK {horrocks,sattler}@cs.man.ac.uk Summary. In this chapter, we explain what description logics are and why they make good ontology languages. In particular, we introduce the description logic SHIQ, which has formed the basis of several well-known ontology languages, in- cluding OWL. We argue that, without the last decade of basic research in description logics, this family of knowledge representation languages could not have played such an important rˆolein this context. Description logic reasoning can be used both during the design phase, in order to improve the quality of ontologies, and in the deployment phase, in order to exploit the rich structure of ontologies and ontology based information. We discuss the extensions to SHIQ that are required for languages such as OWL and, finally, we sketch how novel reasoning services can support building DL knowledge bases. 1 Introduction The aim of this section is to give a brief introduction to description logics, and to argue why they are well-suited as ontology languages. In the remainder of the chapter we will put some flesh on this skeleton by providing more technical details with respect to the theory of description logics, and their relationship to state of the art ontology languages. More detail on these and other matters related to description logics can be found in [6]. Ontologies There have been many attempts to define what constitutes an ontology, per- haps the best known (at least amongst computer scientists) being due to Gruber: “an ontology is an explicit specification of a conceptualisation” [47].3 In this context, a conceptualisation means an abstract model of some aspect of the world, taking the form of a definition of the properties of important 3 This was later elaborated to “a formal specification of a shared conceptualisation” [21].
    [Show full text]
  • Proceedings of the 11Th International Workshop on Ontology
    Proceedings of the 11th International Workshop on Ontology Matching (OM-2016) Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Michelle Cheatham, Oktie Hassanzadeh, Ryutaro Ichise To cite this version: Pavel Shvaiko, Jérôme Euzenat, Ernesto Jiménez-Ruiz, Michelle Cheatham, Oktie Hassanzadeh, et al.. Proceedings of the 11th International Workshop on Ontology Matching (OM-2016). Ontology matching workshop, Kobe, Japan. No commercial editor., pp.1-252, 2016. hal-01421835 HAL Id: hal-01421835 https://hal.inria.fr/hal-01421835 Submitted on 15 Jul 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Ontology Matching OM-2016 Proceedings of the ISWC Workshop Introduction Ontology matching1 is a key interoperability enabler for the semantic web, as well as a useful tactic in some classical data integration tasks dealing with the semantic hetero- geneity problem. It takes ontologies as input and determines as output an alignment, that is, a set of correspondences between the semantically related entities of those on- tologies. These correspondences can be used for various tasks, such as ontology merg- ing, data translation, query answering or navigation on the web of data. Thus, matching ontologies enables the knowledge and data expressed in the matched ontologies to in- teroperate.
    [Show full text]
  • Justification Oriented Proofs In
    Justification Oriented Proofs in OWL Matthew Horridge, Bijan Parsia, and Ulrike Sattler School of Computer Science, The University of Manchester Abstract. Justifications — that is, minimal entailing subsets of an on- tology — are currently the dominant form of explanation provided by ontology engineering environments, especially those focused on the Web Ontology Language (OWL). Despite this, there are naturally occurring justifications that can be very difficult to understand. In essence, justifi- cations are merely the premises of a proof and, as such, do not articulate the (often non-obvious) reasoning which connect those premises with the conclusion. This paper presents justification oriented proofs as a poten- tial solution to this problem. 1 Introduction and Motivation Modern ontology development environments such as Prot´eg´e-4, the NeOn Toolkit, Swoop, and Top Braid Composer, allow users to request explanations for entail- ments (inferences) that they encounter when editing or browsing ontologies. In- deed, the provision of explanation generating functionality is generally seen as being a vital component in such tools. Over the last few years, justifications have become the dominant form of explanation in these tools. This paper examines justifications as a kind of explanation and highlights some problems with them. It then presents justification lemmatisation as a non-standard reasoning service, which can be used to augment a justification with intermediate inference steps, and gives rise to a structure known as a justification oriented proof. Ultimately, a justification oriented proof could be used as an input into some presentation de- vice to help a person step though a justification that is otherwise too difficult for them to understand.
    [Show full text]
  • Curriculum Vitae
    Curriculum vitae Thomas Andreas Meyer 25 August 2019 Address Room 312, Computer Science Building University of Cape Town University Avenue, Rondebosch Tel: +27 12 650 5519 e-mail: [email protected] WWW: http://cs.uct.ac.za/~tmeyer Biographical information Date of birth: 14 January 1964, Randburg, South Africa. Marital status: Married to Louise Leenen. We have two children. Citizenship: Australian and South African. Qualifications 1. PhD (Computer Science), University of South Africa, Pretoria, South Africa, 1999. 2. MSc (Computer Science), Rand Afrikaans University (now the University of Johan- nesburg), Johannesburg, South Africa, 1986. 3. BSc Honours (Computer Science) with distinction, Rand Afrikaans University (now the University of Johannesburg), Johannesburg, South Africa, 1985. 4. BSc (Computer Science, Mathematical Statistics), Rand Afrikaans University (now the University of Johannesburg), Johannesburg, South Africa, 1984. 1 Employment and positions held 1. July 2015 to date: Professor, Department of Computer Science, University of Cape Town, Cape Town, South Africa. 2. July 2015 to June 2020: Director, Centre for Artificial Intelligence Research (CAIR), CSIR, Pretoria, South Africa. 3. July 2015 to June 2020: UCT-CSIR Chair in Artificial Intelligence, University of Cape Town, Cape Town, South Africa. 4. August 2011 to June 2015: Chief Scientist, CSIR Meraka Institute, Pretoria, South Africa. 5. May 2011 to June 2015: Director of the UKZN/CSIR Meraka Centre for Artificial Intelligence Research, CSIR and University of KwaZulu-Natal, South Africa. 6. August 2007 to July 2011: Principal Researcher, CSIR Meraka Institute, Pretoria, South Africa. 7. August 2007 to June 2015: Research Group Leader of the Knowledge Representation and Reasoning group (KRR) at the CSIR Meraka Institute, Pretoria, South Africa.
    [Show full text]
  • AAAI-16 / IAAI-16 / EAAI-16 Program
    Thirtieth AAAI Conference on Artificial Intelligence Twenty-Eighth Conference on Innovative Applications of Artificial Intelligence The Sixth Symposium on Educational Advances in AI AAAI-16 / IAAI-16 / EAAI-16 Program February 12–17, 2016 Phoenix Convention Center / Hyatt Regency Phoenix Phoenix, Arizona, USA Sponsored by the Association for the Advancement of Artificial Intelligence Cosponsored by AI Journal, the National Science Foundation, Baidu, IBM Research, Infosys, Lionbridge, Microsoft Research, Disney Research, University of Southern California/Information Sciences Institute, Yahoo Labs!, ACM/SIGAI, CRA Computing Community Consortium (CCC), and David E. Smith MORNING AFTERNOON EVENING (AFTER 5:00 PM) Friday, February 12 Tutorial Forum Tutorial Forum Student Welcome Reception Workshops Workshops AAAI/SIGAI DC AAAI/SIGAI DC Saturday, February 13 Tutorial Forum Tutorial Forum Speed Dating Workshops Workshops Opening Reception AAAI/SIGAI DC AAAI/SIGAI DC EAAI EAAI Open House Invited Talks Open House Robotics Exhibits Robotics Exhibits Sunday, February 14 AAAI / IAAI Welcome / AAAI Awards Lunch with a Fellow Presidential Address: Dietterich IAAI Invited Talk: Rao AAAI Invited Talk: Krause AAAI / IAAI Technical Program AAAI / IAAI Technical Program AI Job Fair Classic Paper/Robotics Talks What’s Hot Talks / Robotics Talks Poster / Demo Session 1 EAAI EAAI Fellows Dinner Robotics/Vendor Exhibits Robotics/Vendor Exhibits Video Competition Viewing Video Competition Viewing Monday, February 15 Women’s Mentoring Breakfast AAAI Invited Talk:
    [Show full text]
  • Owl2, Rif, Sparql1.1)
    http://www.csd.abdn.ac.uk/ http://www.deri.ie The Role of Logics and Logic Programming in Semantic Web Standards (OWL2, RIF, SPARQL1.1) Axel Polleres Digital Enterprise Research Institute (DERI) National University of Ireland, Galway 1 Outline ESWC2010 Quick intro of RDF/OWL/Linked Open Data OWL2 Overview: from OWL 1 to OWL 2 Reasoning services in OWL 2 OWL2 Treactable Fragments (OWL2RL, OWL2EL, OWL2QL) OWL2 and RIF OWL2 and SPARQL1.1 Time allowed: Implementing SPARQL, OWL2RL, RIF on top of DLV – The GiaBATA system 2 Example: Finding experts/reviewers? ESWC2010 Tim Berners-Lee, Dan Connolly, Lalana Kagal, Yosi Scharf, Jim Hendler: N3Logic: A logical framework for the World Wide Web. Theory and Practice of Logic Programming (TPLP), Volume 8, p249-269 Who are the right reviewers? Who has the right expertise? Which reviewers are in conflict? Observation: Most of the necessary data already on the Web, as RDF! More and more of it follows the Linked Data principles, i.e.: 1. Use URIs as names for things 2. Use HTTP dereferenceable URIs so that people can look up those names. 3. When someone looks up a URI, provide useful information. 4. Include links to other URIs so that they can discover more things. 3 RDF on the Web ESWC2010 (i) directly by the publishers (ii) by e.g. transformations, D2R, RDFa exporters, etc. FOAF/RDF linked from a home page: personal data (foaf:name, foaf:phone, etc.), relationships foaf:knows, rdfs:seeAlso ) 4 RDF on the Web ESWC2010 (i) directly by the publishers (ii) by e.g.
    [Show full text]
  • Formalizing Common Sense Reasoning for Scalable Inconsistency-Robust Information Integration Using Direct Logictm Reasoning and the Actor Model
    Formalizing common sense reasoning for scalable inconsistency-robust information integration using Direct LogicTM Reasoning and the Actor Model Carl Hewitt. http://carlhewitt.info This paper is dedicated to Alonzo Church, Stanisław Jaśkowski, John McCarthy and Ludwig Wittgenstein. ABSTRACT People use common sense in their interactions with large software systems. This common sense needs to be formalized so that it can be used by computer systems. Unfortunately, previous formalizations have been inadequate. For example, because contemporary large software systems are pervasively inconsistent, it is not safe to reason about them using classical logic. Our goal is to develop a standard foundation for reasoning in large-scale Internet applications (including sense making for natural language) by addressing the following issues: inconsistency robustness, classical contrapositive inference bug, and direct argumentation. Inconsistency Robust Direct Logic is a minimal fix to Classical Logic without the rule of Classical Proof by Contradiction [i.e., (Ψ├ (¬))├¬Ψ], the addition of which transforms Inconsistency Robust Direct Logic into Classical Logic. Inconsistency Robust Direct Logic makes the following contributions over previous work: Direct Inference Direct Argumentation (argumentation directly expressed) Inconsistency-robust Natural Deduction that doesn’t require artifices such as indices (labels) on propositions or restrictions on reiteration Intuitive inferences hold including the following: . Propositional equivalences (except absorption) including Double Negation and De Morgan . -Elimination (Disjunctive Syllogism), i.e., ¬Φ, (ΦΨ)├T Ψ . Reasoning by disjunctive cases, i.e., (), (├T ), (├T Ω)├T Ω . Contrapositive for implication i.e., Ψ⇒ if and only if ¬⇒¬Ψ . Soundness, i.e., ├T ((├T) ⇒ ) . Inconsistency Robust Proof by Contradiction, i.e., ├T (Ψ⇒ (¬))⇒¬Ψ 1 A fundamental goal of Inconsistency Robust Direct Logic is to effectively reason about large amounts of pervasively inconsistent information using computer information systems.
    [Show full text]
  • Table of Contents - Part II
    Table of Contents - Part II Semantic Web In-Use Track KOIOS: Utilizing Semantic Search for Easy-Access and Visualization of Structured Environmental Data 1 Veil Bicer, Thanh Tran, Andreas Abecker, and Radoslav Nedkov Wiki-Based Conceptual Modeling: An Experience with the Public Administration 17 Cristiano Casagni, Chiara Di Francescomarino, Mauro Dragoni, Licia Fiorentini, Luca Franci, Matteo Gerosa, Chiara Ghidini, Federica Rizzoli, Marco Rospocher, Anna Rovella, Luciano Serajini, Stefania Sparaco, and Alessandro Tabarroni Linking Semantic Desktop Data to the Web of Data 33 Laura Drägan, Renaud Delbru, Tudor Groza, Siegfried Handschuh, and Stefan Decker Linking Data across Universities: An Integrated Video Lectures Dataset 49 Miriam Fernandez, Mathieu d'Aquin, and Enrico Motta Mind Your Metadata: Exploiting Semantics for Configuration, Adaptation, and Provenance in Scientific Workflows 65 Yolanda Gil, Pedro Szekely, Sandra Villamizar, Thomas C. Harmon, Varun Ratnakar, Shubham Gupta, Maria Muslea, Fabio Silva, and Craig A. Knoblock Using Semantic Web Technologies to Build a Community-Driven Knowledge Curation Platform for the Skeletal Dysplasia Domain 81 Tudor Groza, Andreas Zankl, Yuan-Fang Li, and Jane Hunter Cyber Scientific Test Language 97 Peter Haglich, Robert Grimshaw, Steven Wilder, Marian Nodine, and Bryan Lyles How to "Make a Bridge to the New Town" Using OntoAccess 112 Matthias Hert, Giacomo Ghezzi, Michael Würsch, and Harald C. Gall The MetaLex Document Server: Legal Documents as Versioned Linked Data 128 Rinke Hoekstra Bibliografische
    [Show full text]
  • Program Guide
    The First AAAI/ACM Conference on AI, Ethics, and Society February 1 – 3, 2018 Hilton New Orleans Riverside New Orleans, Louisiana, 70130 USA Program Guide Organized by AAAI, ACM, and ACM SIGAI Sponsored by Berkeley Existential Risk Initiative, DeepMind Ethics & Society, Future of Life Institute, IBM Research AI, Pricewaterhouse Coopers, Tulane University AIES 2018 Conference Overview Thursday, February Friday, Saturday, 1st February 2nd February 3rd Tulane University Hilton Riverside Hilton Riverside 8:30-9:00 Opening 9:00-10:00 Invited talk, AI: Invited talk, AI Iyad Rahwan and jobs: and Edmond Richard Awad, MIT Freeman, Harvard 10:00-10:15 Coffee Break Coffee Break 10:15-11:15 AI session 1 AI session 3 11:15-12:15 AI session 2 AI session 4 12:15-2:00 Lunch Break Lunch Break 2:00-3:00 AI and law AI and session 1 philosophy session 1 3:00-4:00 AI and law AI and session 2 philosophy session 2 4:00-4:30 Coffee break Coffee Break 4:30-5:30 Invited talk, AI Invited talk, AI and law: and philosophy: Carol Rose, Patrick Lin, ACLU Cal Poly 5:30-6:30 6:00 – Panel 2: Invited talk: Panel 1: Prioritizing The Venerable What will Artificial Ethical Tenzin Intelligence bring? Considerations Priyadarshi, in AI: Who MIT Sets the Standards? 7:00 Reception Conference Closing reception Acknowledgments AAAI and ACM acknowledges and thanks the following individuals for their generous contributions of time and energy in the successful creation and planning of AIES 2018: Program Chairs: AI and jobs: Jason Furman (Harvard University) AI and law: Gary Marchant
    [Show full text]