Search Results

Documents authored by Kuhn, Werner


Document
Search Facets and Ranking in Geospatial Dataset Search

Authors: Thomas Hervey, Sara Lafia, and Werner Kuhn

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


Abstract
This study surveys the state of search on open geospatial data portals. We seek to understand 1) what users are able to control when searching for geospatial data, 2) how these portals process and interpret a user’s query, and 3) if and how user query reformulations alter search results. We find that most users initiate a search using a text input and several pre-created facets (such as a filter for tags or format). Some portals supply a map-view of data or topic explorers. To process and interpret queries, most portals use a vertical full-text search engine like Apache Solr to query data from a content-management system like CKAN. When processing queries, most portals initially filter results and then rank the remaining results using a common keyword frequency relevance metric (e.g., TF-IDF). Some portals use query expansion. We identify and discuss several recurring usability constraints across portals. For example, users are typically only given text lists to interact with search results. Furthermore, ranking is rarely extended beyond syntactic comparison of keyword similarity. We discuss several avenues for improving search for geospatial data including alternative interfaces and query processing pipelines.

Cite as

Thomas Hervey, Sara Lafia, and Werner Kuhn. Search Facets and Ranking in Geospatial Dataset Search. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 5:1-5:15, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


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@InProceedings{hervey_et_al:LIPIcs.GIScience.2021.I.5,
  author =	{Hervey, Thomas and Lafia, Sara and Kuhn, Werner},
  title =	{{Search Facets and Ranking in Geospatial Dataset Search}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
  pages =	{5:1--5:15},
  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.5},
  URN =		{urn:nbn:de:0030-drops-130405},
  doi =		{10.4230/LIPIcs.GIScience.2021.I.5},
  annote =	{Keywords: search, portal, discovery, GIR, facet, relevance, ranking}
}
Document
Short Paper
Talk of the Town: Discovering Open Public Data via Voice Assistants (Short Paper)

Authors: Sara Lafia, Jingyi Xiao, Thomas Hervey, and Werner Kuhn

Published in: LIPIcs, Volume 142, 14th International Conference on Spatial Information Theory (COSIT 2019)


Abstract
Access to public data in the United States and elsewhere has steadily increased as governments have launched geospatially-enabled web portals like Socrata, CKAN, and Esri Hub. However, data discovery in these portals remains a challenge for the average user. Differences between users' colloquial search terms and authoritative metadata impede data discovery. For example, a motivated user with expertise can leverage valuable public data about transportation, real estate values, and crime, yet it remains difficult for the average user to discover and leverage data. To close this gap, community dashboards that use public data are being developed to track initiatives for public consumption; however, dashboards still require users to discover and interpret data. Alternatively, local governments are now developing data discovery systems that use voice assistants like Amazon Alexa and Google Home as conversational interfaces to public data portals. We explore these emerging technologies, examining the application areas they are designed to address and the degree to which they currently leverage existing open public geospatial data. In the context of ongoing technological advances, we envision using core concepts of spatial information to organize the geospatial themes of data exposed through voice assistant applications. This will allow us to curate them for improved discovery, ultimately supporting more meaningful user questions and their translation into spatial computations.

Cite as

Sara Lafia, Jingyi Xiao, Thomas Hervey, and Werner Kuhn. Talk of the Town: Discovering Open Public Data via Voice Assistants (Short Paper). In 14th International Conference on Spatial Information Theory (COSIT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 142, pp. 10:1-10:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{lafia_et_al:LIPIcs.COSIT.2019.10,
  author =	{Lafia, Sara and Xiao, Jingyi and Hervey, Thomas and Kuhn, Werner},
  title =	{{Talk of the Town: Discovering Open Public Data via Voice Assistants}},
  booktitle =	{14th International Conference on Spatial Information Theory (COSIT 2019)},
  pages =	{10:1--10:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-115-3},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{142},
  editor =	{Timpf, Sabine and Schlieder, Christoph and Kattenbeck, Markus and Ludwig, Bernd and Stewart, Kathleen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2019.10},
  URN =		{urn:nbn:de:0030-drops-111026},
  doi =		{10.4230/LIPIcs.COSIT.2019.10},
  annote =	{Keywords: data discovery, open public data, voice assistants, essential model, GIS}
}
Document
Enabling the Discovery of Thematically Related Research Objects with Systematic Spatializations

Authors: Sara Lafia, Christina Last, and Werner Kuhn

Published in: LIPIcs, Volume 142, 14th International Conference on Spatial Information Theory (COSIT 2019)


Abstract
It is challenging for scholars to discover thematically related research in a multidisciplinary setting, such as that of a university library. In this work, we use spatialization techniques to convey the relatedness of research themes without requiring scholars to have specific knowledge of disciplinary search terminology. We approach this task conceptually by revisiting existing spatialization techniques and reframing them in terms of core concepts of spatial information, highlighting their different capacities. To apply our design, we spatialize masters and doctoral theses (two kinds of research objects available through a university library repository) using topic modeling to assign a relatively small number of research topics to the objects. We discuss and implement two distinct spaces for exploration: a field view of research topics and a network view of research objects. We find that each space enables distinct visual perceptions and questions about the relatedness of research themes. A field view enables questions about the distribution of research objects in the topic space, while a network view enables questions about connections between research objects or about their centrality. Our work contributes to spatialization theory a systematic choice of spaces informed by core concepts of spatial information. Its application to the design of library discovery tools offers two distinct and intuitive ways to gain insights into the thematic relatedness of research objects, regardless of the disciplinary terms used to describe them.

Cite as

Sara Lafia, Christina Last, and Werner Kuhn. Enabling the Discovery of Thematically Related Research Objects with Systematic Spatializations. In 14th International Conference on Spatial Information Theory (COSIT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 142, pp. 18:1-18:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{lafia_et_al:LIPIcs.COSIT.2019.18,
  author =	{Lafia, Sara and Last, Christina and Kuhn, Werner},
  title =	{{Enabling the Discovery of Thematically Related Research Objects with Systematic Spatializations}},
  booktitle =	{14th International Conference on Spatial Information Theory (COSIT 2019)},
  pages =	{18:1--18:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-115-3},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{142},
  editor =	{Timpf, Sabine and Schlieder, Christoph and Kattenbeck, Markus and Ludwig, Bernd and Stewart, Kathleen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2019.18},
  URN =		{urn:nbn:de:0030-drops-111102},
  doi =		{10.4230/LIPIcs.COSIT.2019.18},
  annote =	{Keywords: spatialization, core concepts of spatial information, information discovery}
}
Document
Improving Discovery of Open Civic Data

Authors: Sara Lafia, Andrew Turner, and Werner Kuhn

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


Abstract
We describe a method and system design for improved data discovery in an integrated network of open geospatial data that supports collaborative policy development between governments and local constituents. Metadata about civic data (such as thematic categories, user-generated tags, geo-references, or attribute schemata) primarily rely on technical vocabularies that reflect scientific or organizational hierarchies. By contrast, public consumers of data often search for information using colloquial terminology that does not align with official metadata vocabularies. For example, citizens searching for data about bicycle collisions in an area are unlikely to use the search terms with which organizations like Departments of Transportation describe relevant data. Users may also search with broad terms, such as "traffic safety", and will then not discover data tagged with narrower official terms, such as "vehicular crash". This mismatch raises the question of how to bridge the users' ways of talking and searching with the language of technical metadata. In similar situations, it has been beneficial to augment official metadata with semantic annotations that expand the discoverability and relevance recommendations of data, supporting more inclusive access. Adopting this strategy, we develop a method for automated semantic annotation, which aggregates similar thematic and geographic information. A novelty of our approach is the development and application of a crosscutting base vocabulary that supports the description of geospatial themes. The resulting annotation method is integrated into a novel open access collaboration platform (Esri's ArcGIS Hub) that supports public dissemination of civic data and is in use by thousands of government agencies. Our semantic annotation method improves data discovery for users across organizational repositories and has the potential to facilitate the coordination of community and organizational work, improving the transparency and efficacy of government policies.

Cite as

Sara Lafia, Andrew Turner, and Werner Kuhn. Improving Discovery of Open Civic Data. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 9:1-9:15, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


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@InProceedings{lafia_et_al:LIPIcs.GISCIENCE.2018.9,
  author =	{Lafia, Sara and Turner, Andrew and Kuhn, Werner},
  title =	{{Improving Discovery of Open Civic Data}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{9:1--9:15},
  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.2018.9},
  URN =		{urn:nbn:de:0030-drops-93376},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.9},
  annote =	{Keywords: data discovery, metadata, query expansion, interoperability}
}
Document
Spatial reference in the Semantic Web and in Robotics (Dagstuhl Seminar 14142)

Authors: Aldo Gangemi, Verena V. Hafner, Werner Kuhn, Simon Scheider, and Luc Steels

Published in: Dagstuhl Reports, Volume 4, Issue 3 (2014)


Abstract
The seminar on "Spatial reference in the Semantic Web and in Robotics" was held from March 30 until April 4, 2014. Seminar participants presented their work related to spatial reference from the viewpoint of Robotics, Spatial Cognition, Geospatial information and the Semantic Web. Groups worked on concrete questions and challenges which were developed during the seminar, some of which resulted in follow up work. This report summarizes the outcomes of the seminar discussions and presents the abstracts of participant talks.

Cite as

Aldo Gangemi, Verena V. Hafner, Werner Kuhn, Simon Scheider, and Luc Steels. Spatial reference in the Semantic Web and in Robotics (Dagstuhl Seminar 14142). In Dagstuhl Reports, Volume 4, Issue 3, pp. 181-201, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2014)


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@Article{gangemi_et_al:DagRep.4.3.181,
  author =	{Gangemi, Aldo and Hafner, Verena V. and Kuhn, Werner and Scheider, Simon and Steels, Luc},
  title =	{{Spatial reference in the Semantic Web and in Robotics (Dagstuhl Seminar 14142)}},
  pages =	{181--201},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2014},
  volume =	{4},
  number =	{3},
  editor =	{Gangemi, Aldo and Hafner, Verena V. and Kuhn, Werner and Scheider, Simon and Steels, Luc},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.4.3.181},
  URN =		{urn:nbn:de:0030-drops-46134},
  doi =		{10.4230/DagRep.4.3.181},
  annote =	{Keywords: Spatial reference systems, Semantic Web, Robotics, Embodiment, Spatial cognition}
}
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