Skip to main content
Book cover

AGILE 2015 pp 107–124Cite as

Enabling Semantic Search and Knowledge Discovery for ArcGIS Online: A Linked-Data-Driven Approach

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

ArcGIS Online is a unified Web portal designed by Environment System Research Institute (ESRI). It contains a rich collection of Web maps, layers, and services contributed by GIS users throughout the world. The metadata about these GIS resources reside in data silos that can be accessed via a Web API. While this is sufficient for simple syntax-based searches, it does not support more advanced queries, e.g., finding maps based on the semantics of the search terms, or performing customized queries that are not pre-designed in the API. In metadata, titles and descriptions are commonly available attributes which provide important information about the content of the GIS resources. However, such data cannot be easily used since they are in the form of unstructured natural language. To address these difficulties, we combine data-driven techniques with theory-driven approaches to enable semantic search and knowledge discovery for ArcGIS Online. We develop an ontology for ArcGIS Online data, convert the metadata into Linked Data, and enrich the metadata by extracting thematic concepts and geographic entities from titles and descriptions. Based on a human participant experiment, we calibrate a linear regression model for semantic search, and demonstrate the flexible queries for knowledge discovery that are not possible in the existing Web API. While this research is based on the ArcGIS Online data, the presented methods can also be applied to other GIS cloud services and data infrastructures.

Keywords

  • Metadata
  • Semantic search
  • Linked data
  • Geoportal
  • ArcGIS online

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-16787-9_7
  • Chapter length: 18 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   119.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-16787-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   179.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1
Fig. 2
Fig. 3

Notes

  1. 1.

    http://www.arcgis.com.

  2. 2.

    http://resources.arcgis.com/en/help/arcgis-rest-api/index.html.

  3. 3.

    http://dublincore.org/documents/dcmi-terms/.

  4. 4.

    http://xmlns.com/foaf/spec/.

  5. 5.

    http://schemas.opengis.net/geosparql/1.0/geosparql_vocab_all.rdf.

  6. 6.

    SPARQL (http://www.w3.org/TR/sparql11-overview/) is the query language for graphed data, e.g., Linked Data, standardized by the World Wide Web Consortium (W3C).

  7. 7.

    http://live.dbpedia.org/page/Santa_Barbara,_California.

  8. 8.

    http://live.dbpedia.org/page/Santa_Barbara_(TV_series).

  9. 9.

    https://jena.apache.org/.

References

  • Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z. (2007). DBpedia: A nucleus for a web of open data. In: The semantic web (pp. 722–735). Berlin, Springer.

    Google Scholar 

  • Battle, R., & Kolas, D. (2012). Enabling the geospatial semantic web with parliament and geosparql. Semantic Web, 3(4), 355–370.

    Google Scholar 

  • Berners-Lee, T., Hendler, J., Lassila, O. (2001) The semantic web (pp. 29–37). Scientific American (2001).

    Google Scholar 

  • Bizer, C., Heath, T., & Berners-Lee, T. (2009a). Linked data—the story so far. International Journal on Semantic Web and Information Systems, 5(3), 1–22.

    CrossRef  Google Scholar 

  • Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., & Cyganiak, R. (2009b). DBpedia-a crystallization point for the web of data. Web Semantics: Science, Services and Agents on the World Wide Web, 7(3), 154–165.

    CrossRef  Google Scholar 

  • Butuc, M. G. (2009). Semantically enriching content using opencalais. EDITIA, 9, 77–88.

    Google Scholar 

  • Dangermond, J. (2009). GIS: Design and evolving technology. ESRI, Fall: ArcNews.

    Google Scholar 

  • Goodchild, M. F., & Glennon, J. A. (2010). Crowdsourcing geographic information for disaster response: a research frontier. International Journal of Digital Earth, 3(3), 231–241.

    CrossRef  Google Scholar 

  • Guha, R., McCool, R., Miller, E. (2003) Semantic search. In: Proceedings of the 12th International Conference on World Wide Web, (pp. 700–709). ACM.

    Google Scholar 

  • Han, L., Kashyap, A., Finin, T., Mayfield, J., Weese, J. (2013) UMBC ebiquity-core: Semantic textual similarity systems (p. 44 ). Atlanta, Georgia, USA.

    Google Scholar 

  • Heath, T., & Bizer, C. (2011). Linked data: Evolving the web into a global data space. Synthesis Lectures on the Semantic Web: Theory and Technology, 1(1), 1–136.

    CrossRef  Google Scholar 

  • Hitzler, P., Krotzsch, M., Rudolph, S. (2011). Foundations of semantic web technologies. Boca Raton, CRC Press.

    Google Scholar 

  • Hu, Y., Janowicz, K., McKenzie, G., Sengupta, K., Hitzler, P. (2013). A linked-data-driven and semantically-enabled journal portal for scientometrics. In: The semantic web–ISWC 2013 (pp. 114–129). Berlin, Springer.

    Google Scholar 

  • Janowicz, K., Scheider, S., Pehle, T., & Hart, G. (2012). Geospatial semantics and linked spatiotemporal data–past, present, and future. Semantic Web, 3(4), 321–332.

    Google Scholar 

  • Jones, C.B., Alani, H., Tudhope, D. (2001). Geographical information retrieval with ontologies of place. In: Spatial information theory, (pp. 322–335). Berlin, Springer.

    Google Scholar 

  • Keßler, C., Janowicz, K., Kauppinen, T.: spatial@ linkedscience–Exploring the research field of GIScience with linked data. In: Geographic Information Science (pp. 102–115). Berlin, Springer (2012).

    Google Scholar 

  • Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104(2), 211.

    CrossRef  Google Scholar 

  • Mendes, P.N., Jakob, M., Garca-Silva, A., Bizer, C. (2011). DBpedia spotlight: Shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems (pp. 1–8). ACM.

    Google Scholar 

  • Miller, G. A. (1995). Wordnet: A lexical database for english. Communications of the ACM, 38(11), 39–41.

    CrossRef  Google Scholar 

  • Tran, T., Cimiano, P., Rudolph, S., Studer, R. (2007). Ontology-based interpretation of keywords for semantic search. In: The Semantic Web (pp. 523–536). Berlin, Springer.

    Google Scholar 

  • Zhou, Q., Wang, C., Xiong, M., Wang, H., Yu, Y. (2007) Spark: Adapting keyword query to semantic search. In: The Semantic Web (pp. 694–707). Berlin, Springer.

    Google Scholar 

Download references

Acknowledgments

This work is a collaborative effort from UCSB STKO Lab and ESRI Applications Prototype Lab. The authors would like to thank Jack Dangermond, Hugh Keegan, Dawn Wright, as well as the three anonymous reviewers for their constructive comments and feedbacks.

Author information

Authors and Affiliations

  1. STKO Lab, University of California Santa Barbara, Santa Barbara, CA, USA

    Yingjie Hu, Krzysztof Janowicz & Song Gao

  2. Applications Prototype Lab, ESRI Inc, Redlands, CA, USA

    Sathya Prasad

Authors
  1. Yingjie Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krzysztof Janowicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sathya Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Song Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yingjie Hu .

Editor information

Editors and Affiliations

  1. NOVA IMS, Universidade Nova de Lisboa, Lisboa, Portugal

    Fernando Bacao

  2. School of Engineering, University of Minho, Guimarães, Portugal

    Maribel Yasmina Santos

  3. NOVA IMS, Universidade Nova de Lisboa, Lisboa, Portugal

    Marco Painho

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Hu, Y., Janowicz, K., Prasad, S., Gao, S. (2015). Enabling Semantic Search and Knowledge Discovery for ArcGIS Online: A Linked-Data-Driven Approach. In: Bacao, F., Santos, M., Painho, M. (eds) AGILE 2015. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-16787-9_7

Download citation