Reasoning in Description Logics Using Resolution and Deductive Databases
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Schema.Org As a Description Logic
Schema.org as a Description Logic Andre Hernich1, Carsten Lutz2, Ana Ozaki1 and Frank Wolter1 1University of Liverpool, UK 2University of Bremen, Germany fandre.hernich, anaozaki, [email protected] [email protected] Abstract Patel-Schneider [2014] develops an ontology language for Schema.org with a formal syntax and semantics that, apart Schema.org is an initiative by the major search en- from some details, can be regarded as a fragment of OWL DL. gine providers Bing, Google, Yahoo!, and Yandex that provides a collection of ontologies which web- In this paper, we abstract slightly further and view the masters can use to mark up their pages. Schema.org Schema.org ontology language as a DL, in line with the for- comes without a formal language definition and malization by Patel-Schneider. Thus, what Schema.org calls a without a clear semantics. We formalize the lan- type becomes a concept name and a property becomes a role guage of Schema.org as a Description Logic (DL) name. The main characteristics of the resulting ‘Schema.org and study the complexity of querying data using DL’ are that (i) the language is very restricted, allowing only (unions of) conjunctive queries in the presence of inclusions between concept and role names, domain and range ontologies formulated in this DL (from several per- restrictions, nominals, and datatypes; (ii) ranges and domains spectives). While querying is intractable in general, of roles can be restricted to disjunctions of concept names (pos- we identify various cases in which it is tractable and sibly mixed with datatypes in range restrictions) and nominals where queries are even rewritable into FO queries are used in ‘one-of enumerations’ which also constitute a form or datalog programs. -
A Framework for Comparing the Use of a Linguistic Ontology in an Application
A Framework for Comparing the use of a Linguistic Ontology in an Application Julieanne van Zyl1 and Dan Corbett2 Abstract. A framework recently developed for understanding and “a body of knowledge about the world (or a domain) that a) is classifying ontology applications provides opportunities to review a repository of primitive symbols used in meaning the state of the art, and to provide guidelines for application b) organises these symbols in a tangled subsumption developers from different communities [1]. The framework hierarchy; and identifies four main categories of ontology applications: neutral c) further interconnects these symbols using a rich system of authoring, ontology as specification, common access to semantic and discourse-pragmatic relations defined among information, and ontology-based search. Specific scenarios are the concepts” [2]. described for each category and a number of features have been identified to highlight the similarities and differences between There has been some work on comparing ontology applications, them. In this paper we identify additional scenarios, describing the which refer to “the application that makes use of or benefits from use of a linguistic ontology in an application. We populate the the ontology (possibly directly)” [1]. A framework [3] was scenarios with a number of prominent research and industrial developed for comparing various projects in ontology design, applications from diverse communities such as knowledge-based which considered the differences and similarities in the way the machine translation, medical databases, heterogenous information projects treat some basic knowledge representation aspects. The systems integration, multilingual text generation, Web-based projects compared include three natural language ontologies, two applications, and information retrieval. -
Ontologies and Description Logics
Symbolic and structural models for image understanding Part II: Ontologies and description logics Jamal Atif - Isabelle Bloch - Céline Hudelot IJCAI 2016 1 / 72 J. Atif, I. Bloch, C. Hudelot Image Understanding Outline 1 What is an ontology ? 2 Ontologies for image understanding: overview 3 Description Logics 4 Description Logics for image understanding 5 Conclusion What is an ontology ? What is an ontology ? Example from F. Gandon, WIMMICS Team, INRIA What is the last document that you have read? Documents 3 / 72 J. Atif, I. Bloch, C. Hudelot Image Understanding What is an ontology ? Ontologies: Definition Ontology ethymology: ontos (being, that which is) + logos (science, study, theory) Philosophy Study of the nature of being, becoming and reality. Study of the basic categories of being and their relations. Computer Science Formal representation of a domain of discourse. Explicit specification of a conceptualization [Gruber 95]. Ref: [Guarino 09] 4 / 72 J. Atif, I. Bloch, C. Hudelot Image Understanding What is an ontology ? Ontologies: Definition ontology Formal, explicit (and shared) specification of a conceptualization [Gruber 95, Studer 98] Formal, explicit specification: a formal language is used to refer to the elements of the conceptualization, e.g. description logics Conceptualization: Objects, concepts and other entities and their relationships Concept Relation Denoted by: Denoted by: a name a name a meaning (intensional definition) an intension a set of denoted objects (extensional an extension definition) 5 / 72 J. Atif, I. Bloch, C. Hudelot Image Understanding What is an ontology ? The different types of ontologies According to their expressivity Source : [Uschold 04] 6 / 72 J. Atif, I. Bloch, C. -
Knowledge Representation in Bicategories of Relations
Knowledge Representation in Bicategories of Relations Evan Patterson Department of Statistics, Stanford University Abstract We introduce the relational ontology log, or relational olog, a knowledge representation system based on the category of sets and relations. It is inspired by Spivak and Kent’s olog, a recent categorical framework for knowledge representation. Relational ologs interpolate between ologs and description logic, the dominant formalism for knowledge representation today. In this paper, we investigate relational ologs both for their own sake and to gain insight into the relationship between the algebraic and logical approaches to knowledge representation. On a practical level, we show by example that relational ologs have a friendly and intuitive—yet fully precise—graphical syntax, derived from the string diagrams of monoidal categories. We explain several other useful features of relational ologs not possessed by most description logics, such as a type system and a rich, flexible notion of instance data. In a more theoretical vein, we draw on categorical logic to show how relational ologs can be translated to and from logical theories in a fragment of first-order logic. Although we make extensive use of categorical language, this paper is designed to be self-contained and has considerable expository content. The only prerequisites are knowledge of first-order logic and the rudiments of category theory. 1. Introduction arXiv:1706.00526v2 [cs.AI] 1 Nov 2017 The representation of human knowledge in computable form is among the oldest and most fundamental problems of artificial intelligence. Several recent trends are stimulating continued research in the field of knowledge representation (KR). -
A Translation Approach to Portable Ontology Specifications
Knowledge Systems Laboratory September 1992 Technical Report KSL 92-71 Revised April 1993 A Translation Approach to Portable Ontology Specifications by Thomas R. Gruber Appeared in Knowledge Acquisition, 5(2):199-220, 1993. KNOWLEDGE SYSTEMS LABORATORY Computer Science Department Stanford University Stanford, California 94305 A Translation Approach to Portable Ontology Specifications Thomas R. Gruber Knowledge System Laboratory Stanford University 701 Welch Road, Building C Palo Alto, CA 94304 [email protected] Abstract To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse — definitions of classes, relations, functions, and other objects — is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how -
THE DATA COMPLEXITY of DESCRIPTION LOGIC ONTOLOGIES in Recent Years, the Use of Ontologies to Access Instance Data Has Become In
THE DATA COMPLEXITY OF DESCRIPTION LOGIC ONTOLOGIES CARSTEN LUTZ AND FRANK WOLTER University of Bremen, Germany e-mail address: [email protected] University of Liverpool e-mail address: [email protected] ABSTRACT. We analyze the data complexity of ontology-mediated querying where the ontologies are formulated in a description logic (DL) of the ALC family and queries are conjunctive queries, positive existential queries, or acyclic conjunctive queries. Our approach is non-uniform in the sense that we aim to understand the complexity of each single ontology instead of for all ontologies for- mulated in a certain language. While doing so, we quantify over the queries and are interested, for example, in the question whether all queries can be evaluated in polynomial time w.r.t. a given on- tology. Our results include a PTIME/CONP-dichotomy for ontologies of depth one in the description logic ALCFI, the same dichotomy for ALC- and ALCI-ontologies of unrestricted depth, and the non-existence of such a dichotomy for ALCF-ontologies. For the latter DL, we additionally show that it is undecidable whether a given ontology admits PTIME query evaluation. We also consider the connection between PTIME query evaluation and rewritability into (monadic) Datalog. 1. INTRODUCTION In recent years, the use of ontologies to access instance data has become increasingly popular [PLC+08, KZ14, BO15]. The general idea is that an ontology provides domain knowledge and an enriched vocabulary for querying, thus serving as an interface between the query and the data, and enabling the derivation of additional facts. In this emerging area, called ontology-mediated querying, it is a central research goal to identify ontology languages for which query evaluation scales to large amounts of instance data. -
Complexity of the Description Logic ALCM
Proceedings, Fifteenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2016) Complexity of the Description Logic ALCM Monica´ Martinez and Edelweis Rohrer Paula Severi Instituto de Computacion,´ Facultad de Ingenier´ıa, Department of Computer Science, Universidad de la Republica,´ Uruguay University of Leicester, England Abstract in ALCM can be used for a knowledge base in ALC. Details of our ExpTime algorithm for ALCM along with In this paper we show that the problem of deciding the consis- proofs of correctness and the complexity result can be found tency of a knowledge base in the Description Logic ALCM is ExpTime-complete. The M stands for meta-modelling as in (Martinez, Rohrer, and Severi 2015). defined by Motz, Rohrer and Severi. To show our main result, we define an ExpTime Tableau algorithm as an extension of 2 A Flexible Meta-modelling Approach an algorithm for ALC by Nguyen and Szalas. A knowledge base in ALCM contains an Mbox besides of a Tbox and an Abox. An Mbox is a set of equalities of the form 1 Introduction a =m A where a is an individual and A is a concept (Motz, The main motivation of the present work is to study the com- Rohrer, and Severi 2015). Figure 1 shows an example of two plexity of meta-modelling as defined in (Motz, Rohrer, and ontologies separated by a horizontal line, where concepts are Severi 2014; 2015). No study of complexity has been done denoted by large ovals and individuals by bullets. The two so far for this approach and we would like to analyse if it ontologies conceptualize the same entities at different lev- increases the complexity of a given description logic. -
Hybrid Logic As Extension of Modal and Temporal Logics
Revista de Humanidades de Valparaíso Humanities Journal of Valparaiso Año 7, 2019, 1er semestre, No 13, págs. 34-67 No 13 (2019): 34-67 eISSN 0719-4242 Hybrid Logic as Extension of Modal and Temporal Logics La lógica híbrida como extensión de las lógicas modal y temporal Daniel Álvarez-Domínguez Universidad de Salamanca, España [email protected] DOI: https://doi.org/10.22370/rhv2019iss13pp34-67 Recibido: 16/07/2019. Aceptado: 14/08/2019 Abstract Developed by Arthur Prior, Temporal Logic allows to represent temporal information on a logical system using modal (temporal) operators such as P, F, H or G, whose intuitive meaning is “it was sometime in the Past...”, “it will be sometime in the Future...”, “it Has always been in the past...” and “it will always Going to be in the future...” respectively. Val- uation of formulae built from these operators are carried out on Kripke semantics, so Modal Logic and Temporal Logic are consequently related. In fact, Temporal Logic is an extension of Modal one. Even when both logics mechanisms are able to formalize modal-temporal information with some accuracy, they suffer from a lack of expressiveness which Hybrid Logic can solve. Indeed, one of the problems of Modal Logic consists in its incapacity of naming specific points inside a model. As Temporal Logic is based on it, it cannot make such a thing neither. But First-Order Logic does can by means of constants and equality relation. Hybrid Logic, which results from combining Modal Logic and First-Order Logic, may solve this shortcoming. The main aim of this paper is to explain how Hybrid Logic emanates from Modal and Temporal ones in order to show what it adds to both logics with regard to information representation, why it is more expressive than them and what relation it maintains with the First-Order Correspondence Language. -
Logic Functions Realized Using Clockless Gates for Rapid Single-Flux-Quantum Circuits
R2-4 SASIMI 2021 Proceedings Logic Functions Realized Using Clockless Gates for Rapid Single-Flux-Quantum Circuits Takahiro Kawaguchi Kazuyoshi Takagi Naofumi Takagi Graduate School of Informatics Graduate School of Engineering Graduate School of Informatics Kyoto University Mie University Kyoto University Kyoto, 606-8501, Japan Mie, 514-8507, Japan Kyoto, 606-8501, Japan [email protected] [email protected] [email protected] Abstract— Superconducting rapid single-flux- and the area occupied by a clock distribution network are quantum (RSFQ) circuit is a promising candidate reduced. The reduction of the pipeline depth leads to a for circuit technology in the post-Moore era. RSFQ fewer DFFs for path-balancing and a smaller circuit area. digital circuits operate with pulse logic and are A clockless AND gate and a clockless NIMPLY gate was usually composed of clocked gates. We have proposed proposed in [10]. A NIMPLY (not-imply) gate is an AND clockless gates, which can reduce the hardware gate with one inverted input, which implements x∧¬y.A amount of a circuit drastically. In this paper, we confluence buffer [1], which merges a pulse from its inputs first discuss logic functions realizable using clockless to its output, can be used as a clockless OR gate. gates. We show that all the 0-preserving functions A clockless gate realizes only a 0-preserving logic func- can be realized using the existing clockless gates, tion, which outputs 0 from the input value of all 0s, be- i.e., AND, OR, and NIMPLY (not-imply) gates. -
Layering Genetic Circuits to Build a Single Cell, Bacterial Half Adder
bioRxiv preprint doi: https://doi.org/10.1101/019257; this version posted May 30, 2015. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Layering genetic circuits to build a single cell, bacterial half adder 2 Adison Wong1,2,†, Huijuan Wang1, Chueh Loo Poh1* and Richard I Kitney2* 3 4 1School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore 637459, SG 5 2Department of Bioengineering, Imperial College London, London SW7 2AZ, UK 6 7 * To whom correspondence should be addressed: Chueh Loo Poh, Division of Bioengineering, School of 8 Chemical and Biomedical Engineering, Nanyang Technological University, Singapore 637459, SG. Tel: +65 9 6514 1088; Email: [email protected] 10 11 * Correspondence may also be addressed to: Richard Ian Kitney, Centre for Synthetic Biology and Innovation, 12 and Department of Bioengineering, Imperial College London, London SW7 2AZ, UK. Tel: +44 (0)20 7594 6226; 13 Email: [email protected] 14 15 †Present Address: NUS Synthetic Biology for Clinical and Technological Innovation, National University of 16 Singapore, Singapore 117456, SG 17 18 ABSTRACT 19 Gene regulation in biological systems is impacted by the cellular and genetic context-dependent 20 effects of the biological parts which comprise the circuit. Here, we have sought to elucidate the 21 limitations of engineering biology from an architectural point of view, with the aim of compiling a set of 22 engineering solutions for overcoming failure modes during the development of complex, synthetic 23 genetic circuits. -
Formalizing Knowledge by Ontologies: OWL and KIF
Formalizing Knowledge by Ontologies: OWL and KIF Marchetti Andrea, Ronzano Francesco, Tesconi Maurizio, Minutoli Salvatore CNR, IIT Department, Via Moruzzi 1, I-56124, Pisa, Italy (andrea.marchetti, francesco.ronzano, maurizio.tesconi, salvatore.minutoli)@iit.cnr.it 2 I Abstract. During the last years, the activities of knowledge formaliza- tion and sharing useful to allow for semantically enabled management of information have been attracting growing attention, expecially in dis- tributed environments like the Web. In this report, after a general introduction about the basis of knowledge abstraction and its formalization through ontologies, we briefly present a list of relevant formal languages used to represent knowledge: CycL, F- Logic, LOOM, KIF, Ontolingua, RDF(S) and OWL. Then we focus our attention on the Web Ontology Language (OWL) and the Knowledge Interchange Format (KIF). OWL is the main language used to describe and share ontologies over the Web: there are three OWL sublanguages with a growing degree of expressiveness. We describe its structure as well as the way it is used in order to reasons over asserted knowledge. Moreover we briefly present three relevant OWL ontology editors: Prot´eg´e, SWOOP and Ontotrack and two important OWL reasoners: Pellet and FACT++. KIF is mainly a standard to describe knowledge among different com- puter systems so as to facilitate its exchange. We describe the main elements of KIF syntax; we also consider Sigma, an environment for cre- ating, testing, modifying, and performing inference with KIF ontologies. We comment some meaningful example of both OWL and KIF ontologies and, in conclusion, we compare their main expresive features. -
Layering Genetic Circuits to Build a Single Cell, Bacterial Half Adder
bioRxiv preprint doi: https://doi.org/10.1101/019257; this version posted May 12, 2015. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Layering genetic circuits to build a single cell, bacterial half adder 2 Adison Wong1,2,†, Huijuan Wang1, Chueh Loo Poh1* and Richard I Kitney2* 3 4 1School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore 637459, SG 5 2Department of Bioengineering, Imperial College London, London SW7 2AZ, UK 6 7 * To whom correspondence should be addressed: Chueh Loo Poh, Division of Bioengineering, School of 8 Chemical and Biomedical Engineering, Nanyang Technological University, Singapore 637459, SG. Tel: +65 9 6514 1088; Email: [email protected] 10 11 * Correspondence may also be addressed to: Richard Ian Kitney, Centre for Synthetic Biology and Innovation, 12 and Department of Bioengineering, Imperial College London, London SW7 2AZ, UK. Tel: +44 (0)20 7594 6226; 13 Email: [email protected] 14 15 †Present Address: NUS Synthetic Biology for Clinical and Technological Innovation, National University of 16 Singapore, Singapore 117456, SG 17 18 ABSTRACT 19 Gene regulation in biological systems is impacted by the cellular and genetic context-dependent 20 effects of the biological parts which comprise the circuit. Here, we have sought to elucidate the 21 limitations of engineering biology from an architectural point of view, with the aim of compiling a set of 22 engineering solutions for overcoming failure modes during the development of complex, synthetic 23 genetic circuits.