KI - Künstliche Intelligenz

German Journal on Artificial Intelligence - Organ des Fachbereichs "Künstliche Intelligenz" der Gesellschaft für Informatik e.V.

ISSN: 0933-1875 (Print) 1610-1987 (Online)

Description

The Scientific journal "KI – Künstliche Intelligenz" is the official journal of the division for artificial intelligence within the "Gesellschaft für Informatik e.V." (GI) – the German Informatics Society – with contributions from throughout the field of artificial intelligence. The journal presents all relevant aspects of artificial intelligence – the fundamentals and tools, their use and adaptation for scientific purposes, and applications which are implemented using AI methods – and thus provides the reader with the latest developments in and well-founded background information on all relevant aspects of artificial intelligence. For all members of the AI community the journal provides quick access to current topics in the field and promotes vital interdisciplinary interchange.

Preview:

Semantic Web

With more and more data available on the web, the use of semantic technologies is the key to making this knowledge accessible to machines. Thirteen years after the last special issue on the Semantic Web, it is time to review again the advances and state-of-the-art in this area. Several new standards for specifying data and schema information in a machine-processable way have emerged: the basic language to describe data, the resource description framework (RDF) with its schema extension RDFS has been revised in 2014; SPARQL became a widely used query language (SPARQL 1.1 standardised in 2013) and now also supports updates, retrieval of entailed query answers, and federation; the Web Ontology Language OWL has been revised and extended to OWL 2; and the rule interchange format RIF (standardized in 2013) allows for expressing rules in a common format. The recent Linked Open Data movement is based on these standards and makes a vast amount of interlinked resources available in the web and for use in semantically enriched applications. What was still a far-away future when the first Semantic Web special issue appeared became reality: the big search engines now use semantic markup (via RDFa, microdata, or microformats) to improve their search results. With their joint schema.org initiative Google, Microsoft, and Yahoo! provide a unified schema for the structured description of web page content. Topics of interest include, but are not limited to:
– Management of semantics and data on the Web, including linked data.
– Languages, tools, and methodologies for representing and managing semantics and data on the Web.
– Database, information retrieval, information extraction, natural language processing and artificial intelligence techniques for the Semantic Web.
– Searching and querying the Semantic Web.
– Knowledge representation and reasoning on the Web.
– Cleaning, quality assurance, and provenance of Semantic Web data, services, and processes.
– Semantic Web data analysis.
– Ontology-based data access and integration/exchange on the Web.
– User Interfaces and interaction with semantics and data on the Web.
– Information visualization of Semantic Web data.
– Ontology engineering and ontology patterns for the Web.
– Ontology modularity, mapping, merging, and alignment for the Web.
– Trust, privacy, and security on the Semantic Web.

The KI Journal, published and indexed by Springer, supports a variety of formats including technical articles, project descriptions, surveys, dissertation abstracts, conference reports, and book reviews. Interested authors are asked to contact the guest editorsat their earliest convenience:

Juniorprof. Dr. Birte Glimm
Institute of Artificial Intelligence
University of Ulm
89069 Ulm
birte.glimm@uni-ulm.de

Prof. Dr. Heiner Stuckenschmidt
Data- and Web Science Research Group
University of Mannheim
68159 Mannheim
heiner@informatik.uni-mannheim.de

Challenges for Reasoning Under Uncertainty, Inconsistency, Vagueness, and Preferences

Managing uncertainty, inconsistency, vagueness, and preferences has been extensively explored in Artificial Intelligence. During the recent years, especially with the emerging of smart services and devices, technologies for managing uncertainty, inconsistency, vagueness, and preferences to tackle the problems of dynamic, real-world scenarios have started to play a key role also in other areas, such as information systems and the (Social or Semantic) Web. These application areas have sparked another wave of strong interest into formalisms and logics for dealing with uncertainty, inconsistency, vagueness, and preferences. Important examples are fuzzy and probabilistic approaches for description logics, or rule systems for handling vagueness and uncertainty in the Semantic Web, or formalisms for handling user preferences in the context of ontological knowledge in the Social Semantic Web. While scalability of these approaches is an important issue to be addressed, also the need for combining various of these approaches with each other or more classical ways of reasoning have become obvious (hybrid reasoning under uncertainty).

The aim of the special issue is to collect overview articles on important state-of-the-art formalisms and methodologies, as well as articles on emerging trends for the future.

Prof. Gabriele Kern-Isberner
TU Dortmund
Fakultät für Informatik
gabriele.kern-isberner@cs.uni-dortmund.de

Prof. Thomas Lukasiewicz
Professor of Computer Science
University of Oxford
thomas.lukasiewicz@cs.ox.ac.uk


Landmark-Based Navigation in Cognitive Systems

The importance of landmarks in human navigation has long been recognized in multiple fields. These include areas involved in the understanding, modelling and supporting wayfinding, spatial knowledge acquisition, and place recognition. From the Psychological, Linguistic and Cognitive Neuroscience viewpoint, the perceived landmarkness of discrete objects vary among individuals. Thus, the key challenge lies in identifying those properties, which remain relevant across a wide range of individual differences, experiences, and behavioural patterns. From the Computer Science, Artificial Intelligence and Cognitive Modelling perspective, formalising these relations in a manner successfully matching the landmark’s relevance for humans has proven difficult. Most recently, the increasing volume and accessibility of semantically rich geospatial data has opened new avenues for further progress in this area. The continuing collaboration between these fields is exemplified by the regular conference series on spatial information theory and geospatial science as well as multiple on-going interdisciplinary research projects.
In spite of that, technologies used to support human navigation struggle to incorporate the type of landmark information relevant for the human user. The gap between the human’s and the computer’s understanding of what constitutes a landmark remains one of the major challenges in the development of spatial systems intuitive in use as well as in modelling navigational behaviour similar to this of a human.
This special issue integrates theoretical, experimental and computational contributions from disciplines involved in the study of landmark-based navigation in cognitive systems. The aim of the issue is to identify new areas for potential interdisciplinary collaboration and we invite applications focusing on, but not limited to, the following topics:
– Automatic, semi-automatic, and crowd-sourced detection of landmarks.
– Modelling of landmark-based navigation.
– Landmark knowledge acquisition and use.
– Communication of landmark-ness.
– Landmark-based approaches for indoor navigation.
– Human-computer interaction with landmark-based systems.
– Ubiquitous computing applications of the landmark concept.

The KI Journal, which is published and indexed by Springer, supports the following lists of formats: Technical contributions, research projects, discussions, dissertation abstracts, conference reports, software, and book reviews. If you are interested in contributing to this special issue, please contact one of the guest editors:
Prof. Dr. Angela Schwering
Dr. Jakub Krukar
Vanessa Joy Anacta

Institute for Geoinformatics
University of Muenster
D-48149 Muenster

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