Description Logics
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D3.3 Initial Technical & Logical Architecture and Future Work
D3.3 Initial Technical & Logical Architecture and future work recommendations ECP 2008 DILI 558001 Europeana v1.0 D3.3 Initial Technical & Logical Architecture and future work recommendations Deliverable number D3.3 Dissemination level Public Delivery date 30 July 2010 Status Final Makx Dekkers, Stefan Gradmann, Jan Author(s) Molendijk eContentplus This project is funded under the eContentplus programme1, a multiannual Community programme to make digital content in Europe more accessible, usable and exploitable. 1 OJ L 79, 24.3.2005, p. 1. 1/14 D3.3 Initial Technical & Logical Architecture and future work recommendations 1. Introduction This deliverable has two tasks: To characterise the technical and logical architecture of Europeana as a system in its 1.0 state (that is to say by the time of the ‘Rhine’ release To outline the future work recommendations that can reasonably be made at that moment. This also provides a straightforward and logical structure to the document: characterisation comes first followed by the recommendations for future work. 2. Technical and Logical Architecture From a high-level architectural point of view, Europeana.eu is best characterized as a search engine and a database. It loads metadata delivered by providers and aggregators into a database, and uses that database to allow users to search for cultural heritage objects, and to find links to those objects. Various methods of searching and browsing the objects are offered, including a simple and an advanced search form, a timeline, and an openSearch API. It is also important to describe what Europeana.eu does not do, even though people sometimes expect it to. -
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 Knowledge Discovery Approach
Semantic XML Tagging of Domain-Specific Text Archives: A Knowledge Discovery Approach Dissertation zur Erlangung des akademisches Grades Doktoringenieur (Dr.-Ing.) angenommen durch die Fakult¨at fur¨ Informatik der Otto-von-Guericke-Universit¨at Magdeburg von Diplom-Kaufmann Peter Karsten Winkler, geboren am 1. Oktober 1971 in Berlin Gutachterinnen und Gutachter: Prof. Dr. Myra Spiliopoulou Prof. Dr. Gunter Saake Prof. Dr. Stefan Conrad Ort und Datum des Promotionskolloquiums: Magdeburg, 22. Januar 2009 Karsten Winkler. Semantic XML Tagging of Domain-Specific Text Archives: A Knowl- edge Discovery Approach. Dissertation, Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany, January 2009. Contents List of Figures v List of Tables vii List of Algorithms xi Abstract xiii Zusammenfassung xv Acknowledgments xvii 1 Introduction 1 1.1 TheAbundanceofText ............................ 1 1.2 Defining Semantic XML Markup . 3 1.3 BenefitsofSemanticXMLMarkup . 9 1.4 ResearchQuestions ............................... 12 1.5 ResearchMethodology ............................. 14 1.6 Outline...................................... 16 2 Literature Review 19 2.1 Storage, Retrieval, and Analysis of Textual Data . ....... 19 2.1.1 Knowledge Discovery in Textual Databases . 19 2.1.2 Information Storage and Retrieval . 23 2.1.3 InformationExtraction. 25 2.2 Discovering Concepts in Textual Data . 26 2.2.1 Topic Discovery in Text Documents . 27 2.2.2 Extracting Relational Tuples from Text . 31 2.2.3 Learning Taxonomies, Thesauri, and Ontologies . 35 2.3 Semantic Annotation of Text Documents . 39 2.3.1 Manual Semantic Text Annotation . 40 2.3.2 Semi-Automated Semantic Text Annotation . 43 2.3.3 Automated Semantic Text Annotation . 47 2.4 Schema Discovery in Marked-Up Text Documents . -
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. -
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. -
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. -
Description Logics Emerge from Ivory Towers Deborah L
Description Logics Emerge from Ivory Towers Deborah L. McGuinness Stanford University, Stanford, CA, 94305 [email protected] Abstract: Description logic (DL) has existed as a field for a few decades yet somewhat recently have appeared to transform from an area of academic interest to an area of broad interest. This paper provides a brief historical perspective of description logic developments that have impacted their usability beyond just in universities and research labs and provides one perspective on the topic. Description logics (previously called terminological logics and KL-ONE-like systems) started with a motivation of providing a formal foundation for semantic networks. The first implemented DL system – KL-ONE – grew out of Brachman’s thesis [Brachman, 1977]. This work was influenced by the work on frame systems but was focused on providing a foundation for building term meanings in a semantically meaningful and unambiguous manner. It rejected the notion of maintaining an ever growing (seemingly adhoc) vocabulary of link and node names seen in semantic networks and instead embraced the notion of a fixed set of domain-independent “epistemological primitives” that could be used to construct complex, structured object descriptions. It included constructs such as “defines-an-attribute-of” as a built-in construct and expected terms like “has-employee” to be higher-level terms built up from the epistemological primitives. Higher level terms such as “has-employee” and “has-part-time-employee” could be related automatically based on term definitions instead of requiring a user to place links between them. In its original incarnation, this led to maintaining the motivation of semantic networks of providing broad expressive capabilities (since people wanted to be able to represent natural language applications) coupled with the motivation of providing a foundation of building blocks that could be used in a principled and well-defined manner. -
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). -
Bibliography [Abiteboul and Kanellakis, 1989] Serge Abiteboul and Paris Kanellakis
507 Bibliography [Abiteboul and Kanellakis, 1989] Serge Abiteboul and Paris Kanellakis. Object identity as a query language primitive. In Proc. of the ACM SIGMOD Int. Conf. on Management of Data, pages 159–173, 1989. [Abiteboul et al., 1995] Serge Abiteboul, Richard Hull, and Victor Vianu. Foundations of Databases. Addison Wesley Publ. Co., Reading, Massachussetts, 1995. [Abiteboul et al., 1997] Serge Abiteboul, Dallan Quass, Jason McHugh, Jennifer Widom, and Janet L. Wiener. The Lorel query language for semistructured data. Int. J. on Digital Libraries, 1(1):68–88, 1997. [Abiteboul et al., 2000] Serge Abiteboul, Peter Buneman, and Dan Suciu. Data on the Web: from Relations to Semistructured Data and XML. Morgan Kaufmann, Los Altos, 2000. [Abiteboul, 1997] Serge Abiteboul. Querying semi-structured data. In Proc. of the 6th Int. Conf. on Database Theory (ICDT’97), pages 1–18, 1997. [Abrahams et al., 1996] Merryll K. Abrahams, Deborah L. McGuinness, Rich Thomason, Lori Alperin Resnick, Peter F. Patel-Schneider, Violetta Cavalli-Sforza, and Cristina Conati. NeoClassic tutorial: Version 1.0. Technical report, Artificial Intelligence Prin- ciples Research Department, AT&T Labs Research and University of Pittsburgh, 1996. Available as http://www.bell-labs.com/project/classic/papers/NeoTut/NeoTut. html. [Abrett and Burstein, 1987] Glen Abrett and Mark H. Burstein. The KREME knowledge editing environment. Int. J. of Man-Machine Studies, 27(2):103–126, 1987. [Abrial, 1974] J. R. Abrial. Data semantics. In J. W. Klimbie and K. L. Koffeman, editors, Data Base Management, pages 1–59. North-Holland Publ. Co., Amsterdam, 1974. [Achilles et al., 1991] E. Achilles, B. -
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. -
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 .