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Documents authored by Janowicz, Krzysztof

Document
DOI: 10.4230/LIPIcs.COSIT.2024.9
Probing the Information Theoretical Roots of Spatial Dependence Measures

Authors: Zhangyu Wang, Krzysztof Janowicz, Gengchen Mai, and Ivan Majic

Published in: LIPIcs, Volume 315, 16th International Conference on Spatial Information Theory (COSIT 2024)

Abstract
Intuitively, there is a relation between measures of spatial dependence and information theoretical measures of entropy. For instance, we can provide an intuition of why spatial data is special by stating that, on average, spatial data samples contain less than expected information. Similarly, spatial data, e.g., remotely sensed imagery, that is easy to compress is also likely to show significant spatial autocorrelation. Formulating our (highly specific) core concepts of spatial information theory in the widely used language of information theory opens new perspectives on their differences and similarities and also fosters cross-disciplinary collaboration, e.g., with the broader AI/ML communities. Interestingly, however, this intuitive relation is challenging to formalize and generalize, leading prior work to rely mostly on experimental results, e.g., for describing landscape patterns. In this work, we will explore the information theoretical roots of spatial autocorrelation, more specifically Moran’s I, through the lens of self-information (also known as surprisal) and provide both formal proofs and experiments.
Cite as

Zhangyu Wang, Krzysztof Janowicz, Gengchen Mai, and Ivan Majic. Probing the Information Theoretical Roots of Spatial Dependence Measures. In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 9:1-9:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{wang_et_al:LIPIcs.COSIT.2024.9,
  author =	{Wang, Zhangyu and Janowicz, Krzysztof and Mai, Gengchen and Majic, Ivan},
  title =	{{Probing the Information Theoretical Roots of Spatial Dependence Measures}},
  booktitle =	{16th International Conference on Spatial Information Theory (COSIT 2024)},
  pages =	{9:1--9:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-330-0},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{315},
  editor =	{Adams, Benjamin and Griffin, Amy L. and Scheider, Simon and McKenzie, Grant},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2024.9},
  URN =		{urn:nbn:de:0030-drops-208247},
  doi =		{10.4230/LIPIcs.COSIT.2024.9},
  annote =	{Keywords: Spatial Autocorrelation, Moran’s I, Information Theory, Surprisal, Self-Information}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.COSIT.2024.23
Towards Formalizing Concept Drift and Its Variants: A Case Study Using Past COSIT Proceedings (Short Paper)

Authors: Meilin Shi, Krzysztof Janowicz, Zilong Liu, and Kitty Currier

Published in: LIPIcs, Volume 315, 16th International Conference on Spatial Information Theory (COSIT 2024)

Abstract
In the classic Philosophical Investigations, Ludwig Wittgenstein suggests that the meaning of words is rooted in their use in ordinary language, challenging the idea of fixed rules determining the meaning of words. Likewise, we believe that the meaning of keywords and concepts in academic papers is shaped by their usage within the articles and evolves as research progresses. For example, the terms natural hazards and natural disasters were once used interchangeably, but this is rarely the case today. When searching for archived documents, such as those related to disaster relief, choosing the appropriate keyword is crucial and requires a deeper understanding of the historical context. To improve interoperability and promote reusability from a Research Data Management (RDM) perspective, we examine the dynamic nature of concepts, providing formal definitions of concept drift and its variants. By employing a case study of past COSIT (Conference on Spatial Information Theory) proceedings to support these definitions, we argue that a quantitative formalization can help systematically detect subsequent changes and enhance the overall interpretation of concepts.
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Meilin Shi, Krzysztof Janowicz, Zilong Liu, and Kitty Currier. Towards Formalizing Concept Drift and Its Variants: A Case Study Using Past COSIT Proceedings (Short Paper). In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 23:1-23:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{shi_et_al:LIPIcs.COSIT.2024.23,
  author =	{Shi, Meilin and Janowicz, Krzysztof and Liu, Zilong and Currier, Kitty},
  title =	{{Towards Formalizing Concept Drift and Its Variants: A Case Study Using Past COSIT Proceedings}},
  booktitle =	{16th International Conference on Spatial Information Theory (COSIT 2024)},
  pages =	{23:1--23:8},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-330-0},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{315},
  editor =	{Adams, Benjamin and Griffin, Amy L. and Scheider, Simon and McKenzie, Grant},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2024.23},
  URN =		{urn:nbn:de:0030-drops-208386},
  doi =		{10.4230/LIPIcs.COSIT.2024.23},
  annote =	{Keywords: Concept Drift, Semantic Aging, Research Data Management}
}
Document
DOI: 10.4230/LIPIcs.COSIT.2022.3
Automatically Discovering Conceptual Neighborhoods Using Machine Learning Methods

Authors: Ling Cai, Krzysztof Janowicz, and Rui Zhu

Published in: LIPIcs, Volume 240, 15th International Conference on Spatial Information Theory (COSIT 2022)

Abstract
Qualitative spatio-temporal reasoning (QSTR) plays a key role in spatial cognition and artificial intelligence (AI) research. In the past, research and applications of QSTR have often taken place in the context of declarative forms of knowledge representation. For instance, conceptual neighborhoods (CN) and composition tables (CT) of relations are introduced explicitly and utilized for spatial/temporal reasoning. Orthogonal to this line of study, we focus on bottom-up machine learning (ML) approaches to investigate QSTR. More specifically, we are interested in questions of whether similarities between qualitative relations can be learned from data purely based on ML models, and, if so, how these models differ from the ones studied by traditional approaches. To achieve this, we propose a graph-based approach to examine the similarity of relations by analyzing trained ML models. Using various experiments on synthetic data, we demonstrate that the relationships discovered by ML models are well-aligned with CN structures introduced in the (theoretical) literature, for both spatial and temporal reasoning. Noticeably, even with significantly limited qualitative information for training, ML models are still able to automatically construct neighborhood structures. Moreover, patterns of asymmetric similarities between relations are disclosed using such a data-driven approach. To the best of our knowledge, our work is the first to automatically discover CNs without any domain knowledge. Our results can be applied to discovering CNs of any set of jointly exhaustive and pairwise disjoint (JEPD) relations.
Cite as

Ling Cai, Krzysztof Janowicz, and Rui Zhu. Automatically Discovering Conceptual Neighborhoods Using Machine Learning Methods. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 3:1-3:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{cai_et_al:LIPIcs.COSIT.2022.3,
  author =	{Cai, Ling and Janowicz, Krzysztof and Zhu, Rui},
  title =	{{Automatically Discovering Conceptual Neighborhoods Using Machine Learning Methods}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{3:1--3:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-257-0},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{240},
  editor =	{Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.3},
  URN =		{urn:nbn:de:0030-drops-168884},
  doi =		{10.4230/LIPIcs.COSIT.2022.3},
  annote =	{Keywords: Qualitative Spatial Reasoning, Qualitative Temporal Reasoning, Conceptual Neighborhood, Machine Learning, Knowledge Discovery}
}
Document
Complete Volume
DOI: 10.4230/LIPIcs.GIScience.2021.II
LIPIcs, Volume 208, GIScience 2021, Complete Volume

Authors: Krzysztof Janowicz and Judith A. Verstegen

Published in: LIPIcs, Volume 208, 11th International Conference on Geographic Information Science (GIScience 2021) - Part II

Abstract
LIPIcs, Volume 208, GIScience 2021, Complete Volume
Cite as

11th International Conference on Geographic Information Science (GIScience 2021) - Part II. Leibniz International Proceedings in Informatics (LIPIcs), Volume 208, pp. 1-224, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@Proceedings{janowicz_et_al:LIPIcs.GIScience.2021.II,
  title =	{{LIPIcs, Volume 208, GIScience 2021, Complete Volume}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part II},
  pages =	{1--224},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-208-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{208},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.II},
  URN =		{urn:nbn:de:0030-drops-147585},
  doi =		{10.4230/LIPIcs.GIScience.2021.II},
  annote =	{Keywords: LIPIcs, Volume 208, GIScience 2021, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/LIPIcs.GIScience.2021.II.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Krzysztof Janowicz and Judith A. Verstegen

Published in: LIPIcs, Volume 208, 11th International Conference on Geographic Information Science (GIScience 2021) - Part II

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

11th International Conference on Geographic Information Science (GIScience 2021) - Part II. Leibniz International Proceedings in Informatics (LIPIcs), Volume 208, pp. 0:i-0:xiv, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{janowicz_et_al:LIPIcs.GIScience.2021.II.0,
  author =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part II},
  pages =	{0:i--0:xiv},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-208-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{208},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.II.0},
  URN =		{urn:nbn:de:0030-drops-147593},
  doi =		{10.4230/LIPIcs.GIScience.2021.II.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Complete Volume
DOI: 10.4230/LIPIcs.GIScience.2021.I
LIPIcs, Volume 177, GIScience 2021, Complete Volume

Authors: Krzysztof Janowicz and Judith A. Verstegen

Published in: LIPIcs, Volume 177, 11th International Conference on Geographic Information Science (GIScience 2021) - Part I (2020)

Abstract
LIPIcs, Volume 177, GIScience 2021, Complete Volume
Cite as

11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 1-284, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Proceedings{janowicz_et_al:LIPIcs.GIScience.2021.I,
  title =	{{LIPIcs, Volume 177, GIScience 2021, Complete Volume}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
  pages =	{1--284},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-166-5},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{177},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I},
  URN =		{urn:nbn:de:0030-drops-130344},
  doi =		{10.4230/LIPIcs.GIScience.2021.I},
  annote =	{Keywords: LIPIcs, Volume 177, GIScience 2021, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/LIPIcs.GIScience.2021.I.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Krzysztof Janowicz and Judith A. Verstegen

Published in: LIPIcs, Volume 177, 11th International Conference on Geographic Information Science (GIScience 2021) - Part I (2020)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{janowicz_et_al:LIPIcs.GIScience.2021.I.0,
  author =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
  pages =	{0:i--0:xii},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-166-5},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{177},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.0},
  URN =		{urn:nbn:de:0030-drops-130359},
  doi =		{10.4230/LIPIcs.GIScience.2021.I.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.5
An Empirical Study on the Names of Points of Interest and Their Changes with Geographic Distance

Authors: Yingjie Hu and Krzysztof Janowicz

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
While Points Of Interest (POIs), such as restaurants, hotels, and barber shops, are part of urban areas irrespective of their specific locations, the names of these POIs often reveal valuable information related to local culture, landmarks, influential families, figures, events, and so on. Place names have long been studied by geographers, e.g., to understand their origins and relations to family names. However, there is a lack of large-scale empirical studies that examine the localness of place names and their changes with geographic distance. In addition to enhancing our understanding of the coherence of geographic regions, such empirical studies are also significant for geographic information retrieval where they can inform computational models and improve the accuracy of place name disambiguation. In this work, we conduct an empirical study based on 112,071 POIs in seven US metropolitan areas extracted from an open Yelp dataset. We propose to adopt term frequency and inverse document frequency in geographic contexts to identify local terms used in POI names and to analyze their usages across different POI types. Our results show an uneven usage of local terms across POI types, which is highly consistent among different geographic regions. We also examine the decaying effect of POI name similarity with the increase of distance among POIs. While our analysis focuses on urban POI names, the presented methods can be generalized to other place types as well, such as mountain peaks and streets.
Cite as

Yingjie Hu and Krzysztof Janowicz. An Empirical Study on the Names of Points of Interest and Their Changes with Geographic Distance. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 5:1-5:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{hu_et_al:LIPIcs.GISCIENCE.2018.5,
  author =	{Hu, Yingjie and Janowicz, Krzysztof},
  title =	{{An Empirical Study on the Names of Points of Interest and Their Changes with Geographic Distance}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{5:1--5:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.5},
  URN =		{urn:nbn:de:0030-drops-93337},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.5},
  annote =	{Keywords: Place names, points of interest, geographic information retrieval, semantic similarity, geospatial semantics}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.17
xNet+SC: Classifying Places Based on Images by Incorporating Spatial Contexts

Authors: Bo Yan, Krzysztof Janowicz, Gengchen Mai, and Rui Zhu

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
With recent advancements in deep convolutional neural networks, researchers in geographic information science gained access to powerful models to address challenging problems such as extracting objects from satellite imagery. However, as the underlying techniques are essentially borrowed from other research fields, e.g., computer vision or machine translation, they are often not spatially explicit. In this paper, we demonstrate how utilizing the rich information embedded in spatial contexts (SC) can substantially improve the classification of place types from images of their facades and interiors. By experimenting with different types of spatial contexts, namely spatial relatedness, spatial co-location, and spatial sequence pattern, we improve the accuracy of state-of-the-art models such as ResNet - which are known to outperform humans on the ImageNet dataset - by over 40%. Our study raises awareness for leveraging spatial contexts and domain knowledge in general in advancing deep learning models, thereby also demonstrating that theory-driven and data-driven approaches are mutually beneficial.
Cite as

Bo Yan, Krzysztof Janowicz, Gengchen Mai, and Rui Zhu. xNet+SC: Classifying Places Based on Images by Incorporating Spatial Contexts. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 17:1-17:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{yan_et_al:LIPIcs.GISCIENCE.2018.17,
  author =	{Yan, Bo and Janowicz, Krzysztof and Mai, Gengchen and Zhu, Rui},
  title =	{{xNet+SC: Classifying Places Based on Images by Incorporating Spatial Contexts}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{17:1--17:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.17},
  URN =		{urn:nbn:de:0030-drops-93450},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.17},
  annote =	{Keywords: Spatial context, Image classification, Place types, Convolutional neural network, Recurrent neural network}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.47
OpenPOI: An Open Place of Interest Platform (Short Paper)

Authors: Grant McKenzie and Krzysztof Janowicz

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
Places of Interest (POI) are a principal component of how human behavior is captured in today's geographic information. Increasingly, access to POI datasets are being restricted - even silo-ed - for commercial use, with vendors often impeding access to the very users that contribute the data. Open mapping platforms such as OpenStreetMap (OSM) offer access to a plethora of geospatial data though they can be limited in the attribute resolution or range of information associated with the data. Nuanced descriptive information associated with POI, e.g., ambience, are not captured by such platforms. Furthermore, interactions with a POI, such as checking in, or recommending a menu item, are inherently place-based concepts. Many of these interactions occur with high temporal volatility that involves frequent interaction with a platform, arguably inappropriate for the "changeset" model adopted by OSM and related datasets. In this short paper we propose OpenPOI, an open platform for storing, serving, and interacting with places of interests and the activities they afford.
Cite as

Grant McKenzie and Krzysztof Janowicz. OpenPOI: An Open Place of Interest Platform (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 47:1-47:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{mckenzie_et_al:LIPIcs.GISCIENCE.2018.47,
  author =	{McKenzie, Grant and Janowicz, Krzysztof},
  title =	{{OpenPOI: An Open Place of Interest Platform}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{47:1--47:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.47},
  URN =		{urn:nbn:de:0030-drops-93752},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.47},
  annote =	{Keywords: place, point of interest, open data, gazetteer, check-in}
}
Document
DOI: 10.4230/DagRep.2.5.93
Cognitive Approaches for the Semantic Web (Dagstuhl Seminar 12221)

Authors: Dedre Gentner, Frank van Harmelen, Pascal Hitzler, Krzysztof Janowicz, and Kai-Uwe Kühnberger

Published in: Dagstuhl Reports, Volume 2, Issue 5 (2012)

Abstract
A major focus in the design of Semantic Web ontology languages used to be on finding a suitable balance between the expressivity of the language and the tractability of reasoning services defined over this language. This focus mirrors the original vision of a Web composed of machine readable and understandable data. Similarly to the classical Web a few years ago, the attention is recently shifting towards a user-centric vision of the Semantic Web. Essentially, the information stored on the Web is from and for humans. This new focus is not only reflected in the fast growing Linked Data Web but also in the increasing influence of research from cognitive science, human computer interaction, and machine-learning. Cognitive aspects emerge as an essential ingredient for future work on knowledge acquisition, representation, reasoning, and interactions on the Semantic Web. Visual interfaces have to support semantic-based retrieval and at the same time hide the complexity of the underlying reasoning machinery from the user. Analogical and similarity-based reasoning should assist users in browsing and navigating through the rapidly increasing amount of information. Instead of pre-defined conceptualizations of the world, the selection and conceptualization of relevant information has to be tailored to the user's context on-the-fly. This involves work on ontology modularization and context-awareness, but also approaches from ecological psychology such as affordance theory which also plays an increasing role in robotics and AI. During the Dagstuhl Seminar 12221 we discussed the most promising ways to move forward on the vision of bringing findings from cognitive science to the Semantic Web, and to create synergies between the different areas of research. While the seminar focused on the use of cognitive engineering for a user-centric Semantic Web, it also discussed the reverse direction, i.e., how can the Semantic Web work on knowledge representation and reasoning feed back to the cognitive science community.
Cite as

Dedre Gentner, Frank van Harmelen, Pascal Hitzler, Krzysztof Janowicz, and Kai-Uwe Kühnberger. Cognitive Approaches for the Semantic Web (Dagstuhl Seminar 12221). In Dagstuhl Reports, Volume 2, Issue 5, pp. 93-116, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@Article{gentner_et_al:DagRep.2.5.93,
  author =	{Gentner, Dedre and van Harmelen, Frank and Hitzler, Pascal and Janowicz, Krzysztof and K\"{u}hnberger, Kai-Uwe},
  title =	{{Cognitive Approaches for the Semantic Web (Dagstuhl Seminar 12221)}},
  pages =	{93--116},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2012},
  volume =	{2},
  number =	{5},
  editor =	{Gentner, Dedre and van Harmelen, Frank and Hitzler, Pascal and Janowicz, Krzysztof and K\"{u}hnberger, Kai-Uwe},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.2.5.93},
  URN =		{urn:nbn:de:0030-drops-37115},
  doi =		{10.4230/DagRep.2.5.93},
  annote =	{Keywords: Cognitive methods, Semantic Web, Analogy and similarity-based reasoning, Semantic heterogeneity and context, Symbol grounding, Emerging semantics, Comonsense reasoning}
}
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