Exploring Semantic Hierarchies to Improve Resolution Theorem Proving on Ontologies
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Automated Theorem Proving Introduction
Automated Theorem Proving Scott Sanner, Guest Lecture Topics in Automated Reasoning Thursday, Jan. 19, 2006 Introduction • Def. Automated Theorem Proving: Proof of mathematical theorems by a computer program. • Depending on underlying logic, task varies from trivial to impossible: – Simple description logic: Poly-time – Propositional logic: NP-Complete (3-SAT) – First-order logic w/ arithmetic: Impossible 1 Applications • Proofs of Mathematical Conjectures – Graph theory: Four color theorem – Boolean algebra: Robbins conjecture • Hardware and Software Verification – Verification: Arithmetic circuits – Program correctness: Invariants, safety • Query Answering – Build domain-specific knowledge bases, use theorem proving to answer queries Basic Task Structure • Given: – Set of axioms (KB encoded as axioms) – Conjecture (assumptions + consequence) • Inference: – Search through space of valid inferences • Output: – Proof (if found, a sequence of steps deriving conjecture consequence from axioms and assumptions) 2 Many Logics / Many Theorem Proving Techniques Focus on theorem proving for logics with a model-theoretic semantics (TBD) • Logics: – Propositional, and first-order logic – Modal, temporal, and description logic • Theorem Proving Techniques: – Resolution, tableaux, sequent, inverse – Best technique depends on logic and app. Example of Propositional Logic Sequent Proof • Given: • Direct Proof: – Axioms: (I) A |- A None (¬R) – Conjecture: |- ¬A, A ∨ A ∨ ¬A ? ( R2) A A ? |- A∨¬A, A (PR) • Inference: |- A, A∨¬A (∨R1) – Gentzen |- A∨¬A, A∨¬A Sequent (CR) ∨ Calculus |- A ¬A 3 Example of First-order Logic Resolution Proof • Given: • CNF: ¬Man(x) ∨ Mortal(x) – Axioms: Man(Socrates) ∀ ⇒ Man(Socrates) x Man(x) Mortal(x) ¬Mortal(y) [Neg. conj.] Man(Socrates) – Conjecture: • Proof: ∃y Mortal(y) ? 1. ¬Mortal(y) [Neg. conj.] 2. ¬Man(x) ∨ Mortal(x) [Given] • Inference: 3. -
An Ontological Approach for Querying Distributed Heterogeneous Information Systems Under Critical Operational Environments
An Ontological Approach for Querying Distributed Heterogeneous Information Systems Under Critical Operational Environments Atif Khan, John Doucette, and Robin Cohen David R. Cheriton School of Computer Science, University of Waterloo {a78khan,j3doucet,rcohen}@uwaterloo.ca Abstract. In this paper, we propose an information exchange frame- work suited for operating in time-critical environments where diverse heterogeneous sources may be involved. We utilize a request-response type of communication in order to eliminate the need for local collection of distributed information. We then introduce an ontological knowledge representation with a semantic reasoner that operates on an aggregated view of the knowledge sources to generate a response and a semantic proof. A prototype of the system is demonstrated in two example emer- gency health care scenarios, where there is low tolerance for inaccuracy and the use of diverse sources is required, not all of which may be known to the individual generating the request. The system is contrasted with conventional machine learning approaches and existing work in seman- tic data mining (used to infer knowledge in order to provide responses), and is shown to oer theoretical and practical advantages over these conventional techniques. 1 Introduction As electronic information systems become mainstream, society's dependence upon them for knowledge acquisition and distribution has increased. Over the years, the sophistication of these systems has evolved, making them capable of not only storing large amounts of information in diverse formats, but also of reasoning about complex decisions. The increase in technological capabilities has revolutionized the syntactic interoperability of modern information systems, allowing for a heterogeneous mix of systems to exchange a wide spectrum of data in many dierent formats. -
Logic and Proof Computer Science Tripos Part IB
Logic and Proof Computer Science Tripos Part IB Lawrence C Paulson Computer Laboratory University of Cambridge [email protected] Copyright c 2015 by Lawrence C. Paulson Contents 1 Introduction and Learning Guide 1 2 Propositional Logic 2 3 Proof Systems for Propositional Logic 5 4 First-order Logic 8 5 Formal Reasoning in First-Order Logic 11 6 Clause Methods for Propositional Logic 13 7 Skolem Functions, Herbrand’s Theorem and Unification 17 8 First-Order Resolution and Prolog 21 9 Decision Procedures and SMT Solvers 24 10 Binary Decision Diagrams 27 11 Modal Logics 28 12 Tableaux-Based Methods 30 i 1 INTRODUCTION AND LEARNING GUIDE 1 1 Introduction and Learning Guide 2008 Paper 3 Q6: BDDs, DPLL, sequent calculus • 2008 Paper 4 Q5: proving or disproving first-order formulas, This course gives a brief introduction to logic, including • resolution the resolution method of theorem-proving and its relation 2009 Paper 6 Q7: modal logic (Lect. 11) to the programming language Prolog. Formal logic is used • for specifying and verifying computer systems and (some- 2009 Paper 6 Q8: resolution, tableau calculi • times) for representing knowledge in Artificial Intelligence 2007 Paper 5 Q9: propositional methods, resolution, modal programs. • logic The course should help you to understand the Prolog 2007 Paper 6 Q9: proving or disproving first-order formulas language, and its treatment of logic should be helpful for • 2006 Paper 5 Q9: proof and disproof in FOL and modal logic understanding other theoretical courses. It also describes • 2006 Paper 6 Q9: BDDs, Herbrand models, resolution a variety of techniques and data structures used in auto- • mated theorem provers. -
Semantic Resource Adaptation Based on Generic Ontology Models
Semantic Resource Adaptation Based on Generic Ontology Models Bujar Raufi, Florije Ismaili, Jaumin Ajdari, Mexhid Ferati and Xhemal Zenuni Faculty of Contemporary Sciences and Technologies, South East European University, Ilindenska 335, Tetovo, Macedonia Keywords: Semantic Web, Linked Open Data, User Adaptive Software Systems, Adaptive Web-based Systems. Abstract: In recent years, a substantial shift from the web of documents to the paradigm of web of data is witnessed. This is seen from the proliferation of massive semantic repositories such as Linked Open Data. Recommending and adapting resources over these repositories however, still represents a research challenge in the sense of relevant data aggregation, link maintenance, provenance and inference. In this paper, we introduce a model of adaptation based on user activities for performing concept adaptation on large repositories built upon extended generic ontology model for Adaptive Web-Based Systems. The architecture of the model is consisted of user data extraction, user knowledgebase, ontology retrieval and application and a semantic reasoner. All these modules are envisioned to perform specific tasks in order to deliver efficient and relevant resources to the users based on their browsing preferences. Specific challenges in relation to the proposed model are also discussed. 1 INTRODUCTION edge. While PIR is mostly query based, AH is mainly browsing oriented. The expansion of the web in the recent years, espe- An ongoing challenge is the semantic retrieval cially with the proliferation of new paradigms such and adaptation of resources over a huge repositories as semantic web, linked semantic data and the so- such as Linked Open Data (LOD) as well as the fa- cial web phenomena has rendered it a place where cilitation of the collaborative knowledge construction information is not simply posted, searched, browsed by assisting in discovering new things (Bizer, 2009). -
INPUT CONTRIBUTION Group Name:* MAS WG Title:* Semantic Web Best Practices
Semantic Web best practices INPUT CONTRIBUTION Group Name:* MAS WG Title:* Semantic Web best practices. Semantic Web Guidelines for domain knowledge interoperability to build the Semantic Web of Things Source:* Eurecom, Amelie Gyrard, Christian Bonnet Contact: Amelie Gyrard, [email protected], Christian Bonnet, [email protected] Date:* 2014-04-07 Abstract:* This contribution proposes to describe the semantic web best practices, semantic web tools, and existing domain ontologies for uses cases (smart home and health). Agenda Item:* Tbd Work item(s): MAS Document(s) Study of Existing Abstraction & Semantic Capability Enablement Impacted* Technologies for consideration by oneM2M. Intended purpose of Decision document:* Discussion Information Other <specify> Decision requested or This is an informative paper proposed by the French Eurecom institute as a recommendation:* guideline to MAS contributors on Semantic web best practices, as it was suggested during MAS#9.3 call. Amélie Gyrard is a new member in oneM2M (via ETSI PT1). oneM2M IPR STATEMENT Participation in, or attendance at, any activity of oneM2M, constitutes acceptance of and agreement to be bound by all provisions of IPR policy of the admitting Partner Type 1 and permission that all communications and statements, oral or written, or other information disclosed or presented, and any translation or derivative thereof, may without compensation, and to the extent such participant or attendee may legally and freely grant such copyright rights, be distributed, published, and posted on oneM2M’s web site, in whole or in part, on a non- exclusive basis by oneM2M or oneM2M Partners Type 1 or their licensees or assignees, or as oneM2M SC directs. -
Enabling Scalable Semantic Reasoning for Mobile Services
IGI PUBLISHING ITJ 5113 701Int’l E. JournalChocolate on Avenue, Semantic Hershey Web & PA Information 17033-1240, Systems, USA 5(2), 91-116, April-June 2009 91 Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.igi-global.com This paper appears in the publication, International Journal on Semantic Web & Information Systems, Volume 5, Issue 2 edited by Amit Sheth © 2009, IGI Global Enabling Scalable Semantic Reasoning for Mobile Services Luke Albert Steller, Monash University, Australia Shonali Krishnaswamy, Monash University, Australia Mohamed Methat Gaber, Monash University, Australia ABSTRACT With the emergence of high-end smart phones/PDAs there is a growing opportunity to enrich mobile/pervasive services with semantic reasoning. This article presents novel strategies for optimising semantic reasoning for re- alising semantic applications and services on mobile devices. We have developed the mTableaux algorithm which optimises the reasoning process to facilitate service selection. We present comparative experimental results which show that mTableaux improves the performance and scalability of semantic reasoning for mobile devices. [Article copies are available for purchase from InfoSci-on-Demand.com] Keywords: Optimised Semantic Reasoning; Pervasive Service Discovery; Scalable Semantic Match- ing INTRODUCTION edge is the resource intensive nature of reason- ing. Currently available semantic reasoners are The semantic web offers new opportunities to suitable for deployment on high-end desktop represent knowledge based on meaning rather or service based infrastructure. However, with than syntax. Semantically described knowledge the emergence of high-end smart phones / can be used to infer new knowledge by reasoners PDAs the mobile environment is increasingly in an automated fashion. -
CTHIOT-1010.Pdf
Making Sense of Data with Machine Reasoning Samer Salam Principal Engineer, Cisco CHTIOT-1010 Cisco Spark Questions? Use Cisco Spark to communicate with the speaker after the session How 1. Find this session in the Cisco Live Mobile App 2. Click “Join the Discussion” 3. Install Spark or go directly to the space 4. Enter messages/questions in the space Cisco Spark spaces will be cs.co/ciscolivebot#CHTIOT-1010 available until July 3, 2017. © 2017 Cisco and/or its affiliates. All rights reserved. Cisco Public Agenda • Introduction • Climbing the Data Chain: Machine Reasoning Primer • Use-Cases • Conclusion Introduction Introduction Challenges in Network & IT Operations Over 50% of network outages from human factors Orchestration ERRORS Virtualization driving an explosion in data to be managed Controller / NMS / EMS Service activation too slow with humans in the loop Smart “things” connecting to network providing myriad of new data Paradigm shift: manage system at operator desired level of abstraction CLI API GUI Easy Button CHTIOT-1010 © 2017 Cisco and/or its affiliates. All rights reserved. Cisco Public 6 SDN, Meet IoT Network Business APP Applications … Applications Machine Reasoning Machine Learning Device Data Abstraction Base Service Controller APIs Application Communication & Models Functions Layer Services Laptop TP Manager Viewpoint Switch IoT SDN Viewpoint SDN Network Router Things Devices IP Phone Printer Call Manager Firewall Web Server Server Farm WLC Telepresence CHTIOT-1010 © 2017 Cisco and/or its affiliates. All rights reserved. Cisco Public 7 Making Sense of Data Data Chain for Machine Reasoning Getting Machine Answers Intelligence Building the Knowledge Enriching with Context Network Data + Understanding the Data Endpoint Meta-Data + Collecting the Application Data Meta-Data [DIKW Pyramid] CHTIOT-1010 © 2017 Cisco and/or its affiliates. -
Semantic BIM Reasoner for the Verification of IFC Models M
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Archive Ouverte en Sciences de l'Information et de la Communication Semantic BIM Reasoner for the verification of IFC Models M. Fahad, Bruno Fies, Nicolas Bus To cite this version: M. Fahad, Bruno Fies, Nicolas Bus. Semantic BIM Reasoner for the verification of IFC Models. 12th European Conference on Product and Process Modelling (ECPPM 2018), Sep 2018, Copenhagen, Denmark. 10.1201/9780429506215. hal-02270827 HAL Id: hal-02270827 https://hal-cstb.archives-ouvertes.fr/hal-02270827 Submitted on 26 Aug 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. Paper published in: Proceedings of the 12th European conference on product and process modelling (ECPPM 2018), Copenhagen, Denmark, September 12-14, 2018, eWork and eBusiness in Architecture, Engineering and Construction - Jan Karlshoj, Raimar Scherer (Eds) Taylor & Francis Group, [ISBN 9781138584136] Semantic BIM Reasoner for the verification of IFC Models M. Fahad Experis IT, 1240 Route des Dolines, 06560 Valbonne, France N. Bus & B. Fies CSTB - Centre Scientifique et Technique du Bâtiment, 290 Route de Lucioles, 06560 Valbonne, France ABSTRACT: Recent years have witnessed the development of various techniques and tools for the building code-compliance of IFC models. -
Solving the Boolean Satisfiability Problem Using the Parallel Paradigm Jury Composition
Philosophæ doctor thesis Hoessen Benoît Solving the Boolean Satisfiability problem using the parallel paradigm Jury composition: PhD director Audemard Gilles Professor at Universit´ed'Artois PhD co-director Jabbour Sa¨ıd Assistant Professor at Universit´ed'Artois PhD co-director Piette C´edric Assistant Professor at Universit´ed'Artois Examiner Simon Laurent Professor at University of Bordeaux Examiner Dequen Gilles Professor at University of Picardie Jules Vernes Katsirelos George Charg´ede recherche at Institut national de la recherche agronomique, Toulouse Abstract This thesis presents different technique to solve the Boolean satisfiability problem using parallel and distributed architec- tures. In order to provide a complete explanation, a careful presentation of the CDCL algorithm is made, followed by the state of the art in this domain. Once presented, two proposi- tions are made. The first one is an improvement on a portfo- lio algorithm, allowing to exchange more data without loosing efficiency. The second is a complete library with its API al- lowing to easily create distributed SAT solver. Keywords: SAT, parallelism, distributed, solver, logic R´esum´e Cette th`ese pr´esente diff´erentes techniques permettant de r´esoudre le probl`eme de satisfaction de formule bool´eenes utilisant le parall´elismeet du calcul distribu´e. Dans le but de fournir une explication la plus compl`ete possible, une pr´esentation d´etaill´ee de l'algorithme CDCL est effectu´ee, suivi d'un ´etatde l'art. De ce point de d´epart,deux pistes sont explor´ees. La premi`ereest une am´eliorationd'un algorithme de type portfolio, permettant d'´echanger plus d'informations sans perte d’efficacit´e. -
Semantic Web & Relative Analysis of Inference Engines
International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 2 Issue 10, October - 2013 Semantic Web & Relative Analysis of Inference Engines Ravi L Verma 1, Prof. Arjun Rajput 2 1Information Technology, Technocrats Institute of Technology 2Computer Engineering, Technocrats Institute of Technology, Excellence Bhopal, MP Abstract - Semantic web is an enhancement to the reasoners to make logical conclusion based on the current web that will provide an easier path to find, framework. Use of inference engine in semantic web combine, share & reuse the information. It brings an allows the applications to investigate why a particular idea of having data to be defined & linked in a way that solution has been reached, i.e. semantic applications can can be used for more efficient discovery, integration, provide proof of the derived conclusion. This paper is a automation & reuse across many applications.Today’s study work & survey, which demonstrates a comparison web is purely based on documents that are written in of different type of inference engines in context of HTML, used for coding a body of text with interactive semantic web. It will help to distinguish among different forms. Semantic web provides a common language for inference engines which may be helpful to judge the understanding how data related to the real world various proposed prototype systems with different views objects, allowing a person or machine to start with in on what an inference engine for the semantic web one database and then move through an unending set of should do. databases that are not connected by wires but by being about the same thing. -
Deduction and Resolution
Lecture 12: Deduction and Resolution 1 Hilbert’s System Hilbert discovered a very elegant deductive system that is both sound and com- plete. His system uses only the connectives {¬, →}, which we know are an adequate basis. It has also been shown that each of the three axioms given below are necessary for the system to be sound and complete. Hilbert’s System consists of the following: • Three axioms (all are strongly sound): – A1: (p → (q → p)) Axiom of simplification. – A2: ((p → (q → r)) → ((p → q) → (p → r))) Frege’s axiom. – A3: ((¬p → q) → ((¬p → ¬q) → p)) Reductio ad absurdum. • Inference Rules: p,p→q – Modus Ponens: q (Strongly Sound) ϕ – Substitution: ϕ[θ] , where θ is a substitution (p → ψ). (Sound) The Substitution Inference Rule is shorthand for all sequences of the form hϕ, ϕ[θ]i. It is sometimes called a schema for generating inference rules. Note that in general, it is not the case that ϕ |= ϕ[θ], for example p 6|= q. However, in assignment 2, we showed that if |= ϕ, then |= ϕ[θ]. This is why this rule is sound but not strongly sound. Theorem 1. Hilbert’s system is sound, complete, and tractable. Proof. We prove each property separately. • Soundness: Since all axioms are strongly sound, and the two inference rules are sound, the whole system is sound. • Completeness: The proof of this property is fairly involved and thus it is outside the scope of the class. 1 + • Tractability: Given Γ ∈ Form and ϕ1, . , ϕn, is ϕ1, . , ϕn ∈ Deductions(Γ)? Clearly, < ϕ1 > is in Γ only if it is an axiom. -
Inference (Proof) Procedures for FOL (Chapter 9)
Inference (Proof) Procedures for FOL (Chapter 9) Torsten Hahmann, CSC384, University of Toronto, Fall 2011 85 Proof Procedures • Interestingly, proof procedures work by simply manipulating formulas. They do not know or care anything about interpretations. • Nevertheless they respect the semantics of interpretations! • We will develop a proof procedure for first-order logic called resolution . • Resolution is the mechanism used by PROLOG • Also used by automated theorem provers such as Prover9 (together with other techniques) Torsten Hahmann, CSC384, University of Toronto, Fall 2011 86 Properties of Proof Procedures • Before presenting the details of resolution, we want to look at properties we would like to have in a (any) proof procedure. • We write KB ⊢ f to indicate that f can be proved from KB (the proof procedure used is implicit). Torsten Hahmann, CSC384, University of Toronto, Fall 2011 87 Properties of Proof Procedures • Soundness • KB ⊢ f → KB ⊨ f i.e all conclusions arrived at via the proof procedure are correct: they are logical consequences. • Completeness • KB ⊨ f → KB ⊢ f i.e. every logical consequence can be generated by the proof procedure. • Note proof procedures are computable, but they might have very high complexity in the worst case. So completeness is not necessarily achievable in practice. Torsten Hahmann, CSC384, University of Toronto, Fall 2011 88 Forward Chaining • First-order inference mechanism (or proof procedure) that generalizes Modus Ponens: • From C1 ∧ … ∧ Cn → Cn+1 with C1, …, Cn in KB, we can infer Cn+1 • We can chain this until we can infer some Ck such that each Ci is either in KB or is the result of Modus Ponens involving only prior Cm (m<i) • We then have that KB ⊢ Ck.