Automated Semantic Forgetting for Expressive Description Logics
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Metadata for Semantic and Social Applications
etadata is a key aspect of our evolving infrastructure for information management, social computing, and scientific collaboration. DC-2008M will focus on metadata challenges, solutions, and innovation in initiatives and activities underlying semantic and social applications. Metadata is part of the fabric of social computing, which includes the use of wikis, blogs, and tagging for collaboration and participation. Metadata also underlies the development of semantic applications, and the Semantic Web — the representation and integration of multimedia knowledge structures on the basis of semantic models. These two trends flow together in applications such as Wikipedia, where authors collectively create structured information that can be extracted and used to enhance access to and use of information sources. Recent discussion has focused on how existing bibliographic standards can be expressed as Semantic Metadata for Web vocabularies to facilitate the ingration of library and cultural heritage data with other types of data. Harnessing the efforts of content providers and end-users to link, tag, edit, and describe their Semantic and information in interoperable ways (”participatory metadata”) is a key step towards providing knowledge environments that are scalable, self-correcting, and evolvable. Social Applications DC-2008 will explore conceptual and practical issues in the development and deployment of semantic and social applications to meet the needs of specific communities of practice. Edited by Jane Greenberg and Wolfgang Klas DC-2008 -
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. -
From the Semantic Web to Social Machines
ARTICLE IN PRESS ARTINT:2455 JID:ARTINT AID:2455 /REV [m3G; v 1.23; Prn:25/11/2009; 12:36] P.1 (1-6) Artificial Intelligence ••• (••••) •••–••• Contents lists available at ScienceDirect Artificial Intelligence www.elsevier.com/locate/artint From the Semantic Web to social machines: A research challenge for AI on the World Wide Web ∗ Jim Hendler a, , Tim Berners-Lee b a Tetherless World Constellation, RPI, United States b Computer Science and AI Laboratory, MIT, United States article info abstract Article history: The advent of social computing on the Web has led to a new generation of Web Received 24 September 2009 applications that are powerful and world-changing. However, we argue that we are just Received in revised form 1 October 2009 at the beginning of this age of “social machines” and that their continued evolution and Accepted 1 October 2009 growth requires the cooperation of Web and AI researchers. In this paper, we show how Available online xxxx the growing Semantic Web provides necessary support for these technologies, outline the challenges we see in bringing the technology to the next level, and propose some starting places for the research. © 2009 Elsevier B.V. All rights reserved. Much has been written about the profound impact that the World Wide Web has had on society. Yet it is primarily in the past few years, as more interactive “read/write” technologies (e.g. Wikis, blogs and photo/video sharing) and social network- ing sites have proliferated, that the truly profound nature of this change is being felt. From the very beginning, however, the Web was designed to create a network of humans changing society empowered using this shared infrastructure. -
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). -
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. -
A New Generation Digital Content Service for Cultural Heritage Institutions
A New Generation Digital Content Service for Cultural Heritage Institutions Pierfrancesco Bellini, Ivan Bruno, Daniele Cenni, Paolo Nesi, Michela Paolucci, and Marco Serena Distributed Systems and Internet Technology Lab, DISIT, Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze, Italy [email protected] http://www.disit.dsi.unifi.it Abstract. The evolution of semantic technology and related impact on internet services and solutions, such as social media, mobile technologies, etc., have de- termined a strong evolution in digital content services. Traditional content based online services are leaving the space to a new generation of solutions. In this paper, the experience of one of those new generation digital content service is presented, namely ECLAP (European Collected Library of Artistic Perform- ance, http://www.eclap.eu). It has been partially founded by the European Commission and includes/aggregates more than 35 international institutions. ECLAP provides services and tools for content management and user network- ing. They are based on a set of newly researched technologies and features in the area of semantic computing technologies capable of mining and establishing relationships among content elements, concepts and users. On this regard, ECLAP is a place in which these new solutions are made available for inter- ested institutions. Keywords: best practice network, semantic computing, recommendations, automated content management, content aggregation, social media. 1 Introduction Traditional library services in which the users can access to content by searching and browsing on-line catalogues obtaining lists of references and sporadically digital items (documents, images, etc.) are part of our history. With the introduction of web2.0/3.0, and thus of data mining and semantic computing, including social media and mobile technologies most of the digital libraries and museum services became rapidly obsolete and were constrained to rapidly change. -
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. -
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]. -
Description Logics—Basics, Applications, and More
Description Logics|Basics, Applications, and More Ian Horrocks Information Management Group University of Manchester, UK Ulrike Sattler Teaching and Research Area for Theoretical Computer Science RWTH Aachen, Germany RWTH Aachen 1 Germany Overview of the Tutorial • History and Basics: Syntax, Semantics, ABoxes, Tboxes, Inference Problems and their interrelationship, and Relationship with other (logical) formalisms • Applications of DLs: ER-diagrams with i.com demo, ontologies, etc. including system demonstration • Reasoning Procedures: simple tableaux and why they work • Reasoning Procedures II: more complex tableaux, non-standard inference prob- lems • Complexity issues • Implementing/Optimising DL systems RWTH Aachen 2 Germany Description Logics • family of logic-based knowledge representation formalisms well-suited for the representation of and reasoning about ➠ terminological knowledge ➠ configurations ➠ ontologies ➠ database schemata { schema design, evolution, and query optimisation { source integration in heterogeneous databases/data warehouses { conceptual modelling of multidimensional aggregation ➠ : : : • descendents of semantics networks, frame-based systems, and KL-ONE • aka terminological KR systems, concept languages, etc. RWTH Aachen 3 Germany Architecture of a Standard DL System Knowledge Base I N Terminology F E I R Father = Man u 9 has child.>... N E T Human = Mammal. u Biped . N E Description C R E F Logic A Concrete Situation S C Y E John:Human u Father S John has child Bill . T . E M RWTH Aachen 4 Germany Introduction -
Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives
information Article Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives Roberta Calegari 1,* , Giovanni Ciatto 2 , Enrico Denti 3 and Andrea Omicini 2 1 Alma AI—Alma Mater Research Institute for Human-Centered Artificial Intelligence, Alma Mater Studiorum–Università di Bologna, 40121 Bologna, Italy 2 Dipartimento di Informatica–Scienza e Ingegneria (DISI), Alma Mater Studiorum–Università di Bologna, 47522 Cesena, Italy; [email protected] (G.C.); [email protected] (A.O.) 3 Dipartimento di Informatica–Scienza e Ingegneria (DISI), Alma Mater Studiorum–Università di Bologna, 40136 Bologna, Italy; [email protected] * Correspondence: [email protected] Received: 25 February 2020; Accepted: 18 March 2020; Published: 22 March 2020 Abstract: Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future. -
Enterprise Data Classification Using Semantic Web Technologies
Enterprise Data Classification Using Semantic Web Technologies David Ben-David1, Tamar Domany2, and Abigail Tarem2 1 Technion – Israel Institute of Technology, Haifa 32000, Israel [email protected] 2 IBM Research – Haifa, University Campus, Haifa 31905, Israel {tamar,abigailt}@il.ibm.com Abstract. Organizations today collect and store large amounts of data in various formats and locations. However they are sometimes required to locate all instances of a certain type of data. Good data classification allows marking enterprise data in a way that enables quick and efficient retrieval of information when needed. We introduce a generic, automatic classification method that exploits Semantic Web technologies to assist in several phases in the classification process; defining the classification requirements, performing the classification and representing the results. Using Semantic Web technologies enables flexible and extensible con- figuration, centralized management and uniform results. This approach creates general and maintainable classifications, and enables applying se- mantic queries, rule languages and inference on the results. Keywords: Semantic Techniques, RDF, Classification, modeling. 1 Introduction Organizations today collect and store large amounts of data in various formats and locations. The data is then consumed in many places, sometimes copied or cached several times, causing valuable and sensitive business information to be scattered across many enterprise data stores. When an organization is required to meet certain legal or regulatory requirements, for instance to comply with regulations or perform discovery during civil litigation, it becomes necessary to find all the places where the required data is located. For example, if in order to comply with a privacy regulation an organization is required to mask all Social Security Numbers (SSN) when delivering personal information to unauthorized entities, all the occurrences of SSN must be found.