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Documents authored by Schockaert, Steven

Document
Invited Paper
DOI: 10.4230/OASIcs.AIB.2022.3
Integrating Ontologies and Vector Space Embeddings Using Conceptual Spaces (Invited Paper)

Authors: Zied Bouraoui, Víctor Gutiérrez-Basulto, and Steven Schockaert

Published in: OASIcs, Volume 99, International Research School in Artificial Intelligence in Bergen (AIB 2022)

Abstract
Ontologies and vector space embeddings are among the most popular frameworks for encoding conceptual knowledge. Ontologies excel at capturing the logical dependencies between concepts in a precise and clearly defined way. Vector space embeddings excel at modelling similarity and analogy. Given these complementary strengths, there is a clear need for frameworks that can combine the best of both worlds. In this paper, we present an overview of our recent work in this area. We first discuss the theory of conceptual spaces, which was proposed in the 1990s by Gärdenfors as an intermediate representation layer in between embeddings and symbolic knowledge bases. We particularly focus on a number of recent strategies for learning conceptual space representations from data. Next, building on the idea of conceptual spaces, we discuss approaches where relational knowledge is modelled in terms of geometric constraints. Such approaches aim at a tight integration of symbolic and geometric representations, which unfortunately comes with a number of limitations. For this reason, we finally also discuss methods in which similarity, and other forms of conceptual relatedness, are derived from vector space embeddings and subsequently used to support flexible forms of reasoning with ontologies, thus enabling a looser integration between embeddings and symbolic knowledge.
Cite as

Zied Bouraoui, Víctor Gutiérrez-Basulto, and Steven Schockaert. Integrating Ontologies and Vector Space Embeddings Using Conceptual Spaces (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 3:1-3:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{bouraoui_et_al:OASIcs.AIB.2022.3,
  author =	{Bouraoui, Zied and Guti\'{e}rrez-Basulto, V{\'\i}ctor and Schockaert, Steven},
  title =	{{Integrating Ontologies and Vector Space Embeddings Using Conceptual Spaces}},
  booktitle =	{International Research School in Artificial Intelligence in Bergen (AIB 2022)},
  pages =	{3:1--3:30},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-228-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{99},
  editor =	{Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.3},
  URN =		{urn:nbn:de:0030-drops-160015},
  doi =		{10.4230/OASIcs.AIB.2022.3},
  annote =	{Keywords: Conceptual Spaces, Ontologies, Vector Space Embeddings, Learning and Reasoning}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.34
Mapping Wildlife Species Distribution With Social Media: Augmenting Text Classification With Species Names (Short Paper)

Authors: Shelan S. Jeawak, Christopher B. Jones, and Steven Schockaert

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

Abstract
Social media has considerable potential as a source of passive citizen science observations of the natural environment, including wildlife monitoring. Here we compare and combine two main strategies for using social media postings to predict species distributions: (i) identifying postings that explicitly mention the target species name and (ii) using a text classifier that exploits all tags to construct a model of the locations where the species occurs. We find that the first strategy has high precision but suffers from low recall, with the second strategy achieving a better overall performance. We furthermore show that even better performance is achieved with a meta classifier that combines data on the presence or absence of species name tags with the predictions from the text classifier.
Cite as

Shelan S. Jeawak, Christopher B. Jones, and Steven Schockaert. Mapping Wildlife Species Distribution With Social Media: Augmenting Text Classification With Species Names (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 34:1-34:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{jeawak_et_al:LIPIcs.GISCIENCE.2018.34,
  author =	{Jeawak, Shelan S. and Jones, Christopher B. and Schockaert, Steven},
  title =	{{Mapping Wildlife Species Distribution With Social Media: Augmenting Text Classification With Species Names}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{34:1--34:6},
  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.2018.34},
  URN =		{urn:nbn:de:0030-drops-93626},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.34},
  annote =	{Keywords: Social media, Text mining, Volunteered Geographic Information, Ecology}
}
Document
DOI: 10.4230/LIPIcs.COSIT.2017.21
Using Flickr for Characterizing the Environment: An Exploratory Analysis

Authors: Shelan S. Jeawak, Christopher B. Jones, and Steven Schockaert

Published in: LIPIcs, Volume 86, 13th International Conference on Spatial Information Theory (COSIT 2017)

Abstract
The photo-sharing website Flickr has become a valuable informal information source in disciplines such as geography and ecology. Some ecologists, for instance, have been manually analysing Flickr to obtain information that is more up-to-date than what is found in traditional sources. While several previous works have shown the potential of Flickr tags for characterizing places, it remains unclear to what extent such tags can be used to derive scientifically useful information for ecologists in an automated way. To obtain a clearer picture about the kinds of environmental features that can be modelled using Flickr tags, we consider the problem of predicting scenicness, species distribution, land cover, and several climate related features. Our focus is on comparing the predictive power of Flickr tags with that of structured data from more traditional sources. We find that, broadly speaking, Flickr tags perform comparably to the considered structured data sources, being sometimes better and sometimes worse. Most importantly, we find that combining Flickr tags with structured data sources consistently, and sometimes substantially, improves the results. This suggests that Flickr indeed provides information that is complementary to traditional sources.
Cite as

Shelan S. Jeawak, Christopher B. Jones, and Steven Schockaert. Using Flickr for Characterizing the Environment: An Exploratory Analysis. In 13th International Conference on Spatial Information Theory (COSIT 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 86, pp. 21:1-21:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{jeawak_et_al:LIPIcs.COSIT.2017.21,
  author =	{Jeawak, Shelan S. and Jones, Christopher B. and Schockaert, Steven},
  title =	{{Using Flickr for Characterizing the Environment: An Exploratory Analysis}},
  booktitle =	{13th International Conference on Spatial Information Theory (COSIT 2017)},
  pages =	{21:1--21:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-043-9},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{86},
  editor =	{Clementini, Eliseo and Donnelly, Maureen and Yuan, May and Kray, Christian and Fogliaroni, Paolo and Ballatore, Andrea},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2017.21},
  URN =		{urn:nbn:de:0030-drops-77523},
  doi =		{10.4230/LIPIcs.COSIT.2017.21},
  annote =	{Keywords: Social media, Volunteered Geographic Information, Ecology}
}
Document
DOI: 10.4230/LIPIcs.ICLP.2010.34
Communicating Answer Set Programs

Authors: Kim Bauters, Jeroen Janssen, Steven Schockaert, Dirk Vermeir, and Martine De Cock

Published in: LIPIcs, Volume 7, Technical Communications of the 26th International Conference on Logic Programming (2010)

Abstract
Answer set programming is a form of declarative programming that has proven very successful in succinctly formulating and solving complex problems. Although mechanisms for representing and reasoning with the combined answer set programs of multiple agents have already been proposed, the actual gain in expressivity when adding communication has not been thoroughly studied. We show that allowing simple programs to talk to each other results in the same expressivity as adding negation-as-failure. Furthermore, we show that the ability to focus on one program in a network of simple programs results in the same expressivity as adding disjunction in the head of the rules.
Cite as

Kim Bauters, Jeroen Janssen, Steven Schockaert, Dirk Vermeir, and Martine De Cock. Communicating Answer Set Programs. In Technical Communications of the 26th International Conference on Logic Programming. Leibniz International Proceedings in Informatics (LIPIcs), Volume 7, pp. 34-43, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{bauters_et_al:LIPIcs.ICLP.2010.34,
  author =	{Bauters, Kim and Janssen, Jeroen and Schockaert, Steven and Vermeir, Dirk and De Cock, Martine},
  title =	{{Communicating Answer Set Programs}},
  booktitle =	{Technical Communications of the 26th International Conference on Logic Programming},
  pages =	{34--43},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-17-0},
  ISSN =	{1868-8969},
  year =	{2010},
  volume =	{7},
  editor =	{Hermenegildo, Manuel and Schaub, Torsten},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2010.34},
  URN =		{urn:nbn:de:0030-drops-25813},
  doi =		{10.4230/LIPIcs.ICLP.2010.34},
  annote =	{Keywords: }
}
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