spatial queries entity recognition and disambiguation
TRANSCRIPT
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Spatial Queries Entity Recognition and DisambiguationBY: EHSAN HAMZEI
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Table of contents 1- Introduction
2- Query Processing (Related Works)
3- State of the Art
4- Our approach
5- Conclusion
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Introduction December 1990 >> First Search engine (W3Catalog) >> Entirely indexed by hand
September 1993 >> WebCrawler >> Finding automatically
…
January 1994 >> Yahoo!
September 1997>> Google
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Introduction(Spatial Search Engine)
New Sources on the web:◦ New Search Engines for Images, Videos◦ New Search engine for geospatial data (Google Maps, Bing Maps)
February 2005 >> Google Maps
December 2010 >> Bing Maps
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Query Processing (What is Query Processing?)
Search Engine two major process:◦ 1- Offline (For crawling and collection data)◦ 2- Online (Started from user’s query and end with returning the results)
Where is Query Processing?
What is Query Processing brings to us?
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Query Processing and Related Works
NLP >> Natural Language Processing
ER >> Entity Recognition
Related Works:◦ Guo et al. (2009) addresses the problem of Named Entity Recognition in Query (NERQ)◦ …◦ Dalvi et al.(2014) developed a four step algorithm named Topic-specific Language Model (TLM method)
for doing Entity Recognition and Disambiguation from search queries.
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Query Processing (State Of The Art)
An Example of two same query by google maps:
1- Intersection of shariati and resalat
2- Intersection of valiasr and enqelab
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Proposed Approach (Definition) Spatial Query = Combination of:
◦ 1- Location Name◦ 2- Location Type ◦ 3- Spatial Relationship◦ Example : Hospitals around Resalat Square
Based On NLP (ER) We can recognize and tag these types for further processes
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Proposed Approach (Algorithm) 1- Input Query > Segmentation (Top to Down)
2- Candidate ◦ 2-1 Location Name◦ 2-2 Location Type◦ 2-3 Spatial Relationship
3- Validate The Result◦ 3-1 Check that it is fully understand◦ 3-2 Check the conceptual criteria◦ 3-3 Check the logical criteria
4- Returning the result
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Proposed Approach (Evaluation) Two kind of evaluations can be possible:
1- Disambiguation:◦ The average disambiguation for 100 spatial queries: 89.45%
2- According to 100 spatial queries compared to Google Maps◦ Google Maps : 72◦ Our Approach : 91
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Conclusion Changing the perspective from textual to spatial
Take the spatial relationship into account◦ Make them answerable in general◦ Using them for disambiguation
Future Work:◦ Using the combination of Geocode APIs◦ Develop more sophisticated algorithm (2 or more spatial relationship)
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Thanks For Your Attention