Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben

2019 | Buch

Classical Algorithms for Reasoning and Explanation in Description Logics Kapitel lesen Erstes Kapitel lesen

Reasoning Web. Explainable Artificial Intelligence

15th International Summer School 2019, Bolzano, Italy, September 20–24, 2019, Tutorial Lectures

herausgegeben von: Markus Krötzsch, Daria Stepanova

Verlag: Springer International Publishing

Buchreihe : Lecture Notes in Computer Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Einloggen, um Zugang zu erhalten
insite
SUCHEN

Über dieses Buch

This volume contains lecture notes of the 15th Reasoning Web Summer School (RW 2019), held in Bolzano, Italy, in September 2019.
The research areas of Semantic Web, Linked Data, and Knowledge Graphs have recently received a lot of attention in academia and industry. Since its inception in 2001, the Semantic Web has aimed at enriching the existing Web with meta-data and processing methods, so as to provide Web-based systems with intelligent capabilities such as context awareness and decision support. The Semantic Web vision has been driving many community efforts which have invested a lot of resources in developing vocabularies and ontologies for annotating their resources semantically. Besides ontologies, rules have long been a central part of the Semantic Web framework and are available as one of its fundamental representation tools, with logic serving as a unifying foundation. Linked Data is a related research area which studies how one can make RDF data available on the Web and interconnect it with other data with the aim of increasing its value for everybody. Knowledge Graphs have been shown useful not only for Web search (as demonstrated by Google, Bing, etc.) but also in many application domains.

Inhaltsverzeichnis

Frontmatter
Classical Algorithms for Reasoning and Explanation in Description Logics
Abstract
Description Logics (DLs) are a family of languages designed to represent conceptual knowledge in a formal way as a set of ontological axioms. DLs provide a formal foundation of the ontology language OWL, which is a W3C standardized language to represent information in Web applications. The main computational problem in DLs is finding relevant consequences of the information stored in ontologies, e.g., to answer user queries. Unlike related techniques based on keyword search or machine learning, the notion of a consequence is well-defined using a formal logic-based semantics. This course provides an in-depth description and analysis of the main reasoning and explanation methods for ontologies: tableau procedures and axiom pinpointing algorithms.
Birte Glimm, Yevgeny Kazakov
Explanation-Friendly Query Answering Under Uncertainty
Abstract
Many tasks often regarded as requiring some form of intelligence to perform can be seen as instances of query answering over a semantically rich knowledge base. In this context, two of the main problems that arise are: (i) uncertainty, including both inherent uncertainty (such as events involving the weather) and uncertainty arising from lack of sufficient knowledge; and (ii) inconsistency, which involves dealing with conflicting knowledge. These unavoidable characteristics of real world knowledge often yield complex models of reasoning; assuming these models are mostly used by humans as decision-support systems, meaningful explainability of their results is a critical feature. These lecture notes are divided into two parts, one for each of these basic issues. In Part 1, we present basic probabilistic graphical models and discuss how they can be incorporated into powerful ontological languages; in Part 2, we discuss both classical inconsistency-tolerant semantics for ontological query answering based on the concept of repair and other semantics that aim towards more flexible yet principled ways to handle inconsistency. Finally, in both parts we ponder the issue of deriving different kinds of explanations that can be attached to query results.
Maria Vanina Martinez, Gerardo I. Simari
Provenance in Databases: Principles and Applications
Abstract
Data provenance is extra information computed during query evaluation over databases, which provides additional context about query results. Several formal frameworks for data provenance have been proposed, in particular based on provenance semirings. The provenance of a query can be computed in these frameworks for a variety of query languages. Provenance has applications in various settings, such as probabilistic databases, view maintenance, or explanation of query results. Though the theory of provenance semirings has mostly been developed in the setting of relational databases, it can also apply to other data representations, such as XML, graph, and triple-store databases.
Pierre Senellart
Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases
Abstract
Entity-centric knowledge bases are large collections of facts about entities of public interest, such as countries, politicians, or movies. They find applications in search engines, chatbots, and semantic data mining systems. In this paper, we first discuss the knowledge representation that has emerged as a pragmatic consensus in the research community of entity-centric knowledge bases. Then, we describe how these knowledge bases can be mined for logical rules. Finally, we discuss how entities can be represented alternatively as vectors in a vector space, by help of neural networks.
Fabian M. Suchanek, Jonathan Lajus, Armand Boschin, Gerhard Weikum
Explaining Data with Formal Concept Analysis
Abstract
We give a brief introduction into Formal Concept Analysis, an approach to explaining data by means of lattice theory.
Bernhard Ganter, Sebastian Rudolph, Gerd Stumme
Logic-Based Learning of Answer Set Programs
Abstract
Learning interpretable models from data is stated as one of the main challenges of AI. The goal of logic-based learning is to compute interpretable (logic) programs that explain labelled examples in the context of given background knowledge. This tutorial introduces recent advances of logic-based learning, specifically learning non-monotonic logic programs under the answer set semantics. We introduce several learning frameworks and algorithms, which allow for learning highly expressive programs, containing rules representing non-determinism, choice, exceptions, constraints and preferences. Throughout the tutorial, we put a strong emphasis on the expressive power of the learning systems and frameworks, explaining why some systems are incapable of learning particular classes of programs.
Mark Law, Alessandra Russo, Krysia Broda
Constraint Learning: An Appetizer
Abstract
Constraints are ubiquitous in artificial intelligence and operations research. They appear in logical problems like propositional satisfiability, in discrete problems like constraint satisfaction, and in full-fledged mathematical optimization tasks. Constraint learning enters the picture when the structure or the parameters of the constraint satisfaction/optimization problem to be solved are (partially) unknown and must be inferred from data. The required supervision may come from offline sources or gathered by interacting with human domain experts and decision makers. With these lecture notes, we offer a brief but self-contained introduction to the core concepts of constraint learning, while sampling from the diverse spectrum of constraint learning methods, covering classic strategies and more recent advances. We will also discuss links to other areas of AI and machine learning, including concept learning, learning from queries, structured-output prediction, (statistical) relational learning, preference elicitation, and inverse optimization.
Stefano Teso
A Modest Markov Automata Tutorial
Abstract
Distributed computing systems provide many important services. To explain and understand why and how well they work, it is common practice to build, maintain, and analyse models of the systems’ behaviours. Markov models are frequently used to study operational phenomena of such systems. They are often represented with discrete state spaces, and come in various flavours, overarched by Markov automata. As such, Markov automata provide the ingredients that enable the study of a wide range of quantitative properties related to risk, cost, performance, and strategy. This tutorial paper gives an introduction to the formalism of Markov automata, to practical modelling of Markov automata in the Modest language, and to their analysis with the Modest Toolset. As case studies, we optimise an attack on Bitcoin, and evaluate the performance of a small but complex resource-sharing computing system.
Arnd Hartmanns, Holger Hermanns
Explainable AI Planning (XAIP): Overview and the Case of Contrastive Explanation (Extended Abstract)
Abstract
Model-based approaches to AI are well suited to explainability in principle, given the explicit nature of their world knowledge and of the reasoning performed to take decisions. AI Planning in particular is relevant in this context as a generic approach to action-decision problems. Indeed, explainable AI Planning (XAIP) has received interest since more than a decade, and has been taking up speed recently along with the general trend to explainable AI. In the lecture, we provide an overview, categorizing and illustrating the different kinds of explanation relevant in AI Planning; and we outline recent works on one particular kind of XAIP, contrastive explanation. This extended abstract gives a brief summary of the lecture, with some literature pointers. We emphasize that completeness is neither claimed nor intended; the abstract may serve as a brief primer with literature entry points.
Jörg Hoffmann, Daniele Magazzeni
Backmatter
Metadaten
Titel
Reasoning Web. Explainable Artificial Intelligence
herausgegeben von
Markus Krötzsch
Daria Stepanova
Copyright-Jahr
2019
Electronic ISBN
978-3-030-31423-1
Print ISBN
978-3-030-31422-4
DOI
https://doi.org/10.1007/978-3-030-31423-1

Premium Partner

    Bildnachweise