Special Issue "Conceptual Structures 2019"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 May 2020).

Special Issue Editors

Prof. Dr. Dominik Endres
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Guest Editor
Department of Psychology, Philipps Universität Marburg, 35036 Marburg, Germany
Interests: machine learning; neuroscience; computational psychology; order theory
Dr. Mehwish Alam
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Guest Editor
FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, 76344 Karlsruhe, Germany
Interests: semantic web; data mining; artificial intelligence
Dr. Diana Florina Sotropa
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Guest Editor
Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca 400084, Romania
Interests: web programing; web technologies and communication; web design and optimization; formal concept analysis

Special Issue Information

Dear Colleagues,

This Special Issue, which grew out of the International Conferences on Conceptual Structures (ICCS 2019), focuses on the formal analysis and representation of conceptual knowledge, at the crossroads of artificial intelligence, human cognition, computational linguistics, and related areas of computer science and cognitive science. Recently, graph-based knowledge representation and reasoning (KRR) paradigms are getting more and more attention. With the rise of quasi-autonomous AI, graph-based representations provide a vehicle for making machine cognition explicit to its human users. Conversely, graphical and graph-based models can provide a rigorous way of expressing intuitive notions in computable frameworks. The aim of the ICCS 2019 conference is to build upon its long-standing expertise in graph-based KRR and focus on providing modeling, formal, and application results of graph-based systems.

The Special Issue welcomes contributions from a modeling, application, and theoretical viewpoint:

  • Modeling results will investigate concrete real world needs for graph-based representation, for example (but not exclusively) how human cognition can be mapped onto and facilitated by graphical representations, how certain use cases are of interest to the graph community, how using graphs can bring added (business) value, what kind of graph representation is needed for a given case, etc.;
  • Papers reporting on application experience will be expected to demonstrate the benefits of the graph-based proposed solutions in the context of the use case studied. Where appropriate, the graph-based solutions are compared to other possible solutions;
  • Technical results will include fundamental graph theory based results for novel structures for representation, extensions of existing structures for added expressivity, conciseness, optimization algorithms for reasoning, reasoning explanation, etc.

Prof. Dr. Dominik Endres
Dr. Mehwish Alam
Dr. Diana Florina Sotropa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Graph-based models and tools for human reasoning
  • Existential and conceptual graphs
  • Formal concept analysis
  • Philosophical, neural, and didactic investigations of conceptual, graphical representations
  • Knowledge architecture and management
  • Human and machine reasoning under inconsistency
  • Human and machine knowledge representation and uncertainty
  • Contextual logic
  • Constraint satisfaction
  • Decision making and argumentation
  • Ontologies
  • Semantic Web, Web of Data, Web 2.0
  • Social network analysis
  • Conceptual knowledge acquisition
  • Data and text mining
  • Conceptual structures in natural language processing and linguistics
  • Metaphoric, cultural or semiotic considerations
  • Resource allocation and agreement technologies

Published Papers (2 papers)

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Research

Open AccessArticle
Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL
Information 2020, 11(7), 361; https://doi.org/10.3390/info11070361 - 12 Jul 2020
Abstract
Linked Open Data (LOD) refers to freely available data on the World Wide Web that are typically represented using the Resource Description Framework (RDF) and standards built on it. LOD is an invaluable resource of information due to its richness and openness, which [...] Read more.
Linked Open Data (LOD) refers to freely available data on the World Wide Web that are typically represented using the Resource Description Framework (RDF) and standards built on it. LOD is an invaluable resource of information due to its richness and openness, which create new opportunities for many areas of application. In this paper, we address the exploitation of LOD by utilizing SPARQL queries in order to extract social networks among entities. This enables the application of de-facto techniques from Social Network Analysis (SNA) to study social relations and interactions among entities, providing deep insights into their latent social structure. Full article
(This article belongs to the Special Issue Conceptual Structures 2019)
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Open AccessArticle
Null Models for Formal Contexts
Information 2020, 11(3), 135; https://doi.org/10.3390/info11030135 - 28 Feb 2020
Abstract
Null model generation for formal contexts is an important task in the realm of formal concept analysis. These random models are in particular useful for, but not limited to, comparing the performance of algorithms. Nonetheless, a thorough investigation of how to generate null [...] Read more.
Null model generation for formal contexts is an important task in the realm of formal concept analysis. These random models are in particular useful for, but not limited to, comparing the performance of algorithms. Nonetheless, a thorough investigation of how to generate null models for formal contexts is absent. Thus we suggest a novel approach using Dirichlet distributions. We recollect and analyze the classical coin-toss model, recapitulate some of its shortcomings and examine its stochastic properties. Building upon this we propose a model which is capable of generating random formal contexts as well as null models for a given input context. Through an experimental evaluation we show that our approach is a significant improvement with respect to the variety of contexts generated. Furthermore, we demonstrate the applicability of our null models with respect to real world datasets. Full article
(This article belongs to the Special Issue Conceptual Structures 2019)
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