Enabling Semantic Search in Geospatial Metadata Catalogue to Support Polar Sciences

Transcription

1 Enabling Semantic Search in Geospatial Metadata Catalogue to Support Polar Sciences Wenwen Li 1 and Vidit Bhatia 2 1 GeoDa Center for Geospatial Analysis and Computation School of Geographical Sciences and Urban Planning 2 Department of Computer Science Ira. Fulton School of Engineering Arizona State University

2 Outline Motivation Intelligent geospatial data discovery A knowledge mining approach Integrated solution for semantic-enabled metadata catalogue Experimental results Demo Conclusion and discussion 2

3 Towards a CyberPolar Community Studies about polar regions has attracted great attention: Increasing interest in mining and natural resource exploration Both poles are sensitive to human activities and global, environmental, and climate changes Polar regions are key drivers of the Earth climate President s National Strategy for the Arctic Region Released in May 2013 by the white house Overarching stewardship objectives: Make decisions using the best available information 3

4 Towards a CyberPolar Community Earlier efforts: Virtual Arctic Spatial Data Infrastructure (Li et al. 2011) 4

5 Existing work 5

6 Existing work 6

7 Problem Statement Current full-text search cannot address fuzzy search need Frozen ground vs. Permafrost. Gap between the best available data and the most suitable research applications. A smart mechanism for data discovery is needed to bridge the gap. 7

8 Intelligent Geospatial Data Discovery Ontology-based approach Formalize the definition of domain knowledge Classes, individuals, properties, relationships Current Work: LEAD (Droegemeler et al. 2005) VSTO (Fox et al. 2008) SWEET based semantic search (Li et al. 2011, Li et al. 2012a) 8

9 Limitation Hard to model spatial relationship (Shi, 2011) Equal, within, touch, disjoint, intersect.. Hard to build a consensus domain ontology (ontology mapping) Ontology Full Name Creator SWEET CUAHSI Limited spatial reasoning capability Similarity reasoning Semantic Web for Earth and Environmental Terminology Consortium of Universities for the Advancement of Hydrologic Science NASA JPL CUAHSI MMI Marine Metadata Interoperability NSF INSPIRE Infrastructure for Spatial Information in Europe European Commission GEMET General Multilingual Environmental Thesaurus EEA, ETC/CDS Rodriguez and Egenhofer, 2004; Janowicz et al. 2008; Li et al. 2012a 9

10 A Knowledge Mining Approach Methodology: LSATTR (Li et al. 2012) Latent Semantic Analysis and Two Tier Ranking Identify latent semantic association among terminologies through singular value decomposition and lower rank estimation Proposed a new similarity measure (first tier): Second tier: rank by number of exact match 10

11 LSA, SVD and Explaination Singular Valued Decomposition A mn = U mm S mn V T nn Reduced Singular valued Decomposition LSA 11

12 An Integrated Solution Combine LSATTR and Geonetwork to support semantic enabled metadata catalogue Why Geonetwork? 12

13 Service-Oriented Extension of Metadata Catalogue 13

14 Business logic of semantic search flow 14

15 Creation of Semantic Association Matrix Count Matrix Stemming TF*IDF Matrix Semantic Matrix 15

16 Demo 16

17 Performance Evaluation Factors: Precision Recall Experiments: How does the degree of rank reduction impact the search performance? If the proposed algorithm introduce enhancement to the search capability? 17

18 Queries used offshore hydrocarbon offshore hydrocarbon mining Marine biology data Frozen ground Canada Sediment data Yukon Canada air pilot Earthquake magnitude data Arctic Sea level increase climate 21

19 Comparison of Recall 22

20 Comparison of Precision 23

21 Summary A data mining approach to improve geospatial data discovery LSA outperforms full-text search An alternative approach to establish domain ontology to complement the top-down approach 24

22 Future Work Investigate the impact of stemming algorithm to the search effectiveness Further improve the performance of semantic search algorithm to handle big spatial data 25

23 References W. Li, Automated Data Discovery, Reasoning and Ranking in Support of Building an Intelligent Geographic Search Engine, Ph.D. Dissertation. George Mason University, August W. Li, C. Yang, D. Nebert, R. Raskin and H. Wu, Semantic-based service chaining for building a virtual Arctic spatial data infrastructure. Computers & Geosciences. 37(11), W. Li, R. Raskin and M.F. Goodchild, 2012a. Semantic similarity measurement based on knowledge mining: an artificial neural network approach. International Journal of Geographic Information Science, 26(8), W. Li, M.F. Goodchild and R. Raskin, 2012b. Towards geospatial semantic search: exploiting latent semantic relations in geospatial data. International Journal of Digital Earth, DOI: /

24 Acknowledgement National Science Foundation Polar Cyberinfrastructure Program Dr. Nancy Wiegand for inviting us to share our research outcome 27