This document summarizes a research paper on spatial queries, entity recognition, and disambiguation. It begins with an introduction on the history of search engines and the rise of spatial search engines. It then discusses related work on query processing, entity recognition, and disambiguation. The document outlines the author's proposed approach, which uses natural language processing to recognize locations, types of locations, and spatial relationships in queries. It evaluates the approach based on disambiguation accuracy and comparison to Google Maps results. In conclusion, it discusses how the approach changes the perspective to spatial queries and allows disambiguation, with potential for future work combining geocoding APIs and handling multiple relationships.

1. Spatial Queries Entity
Recognition and
Disambiguation
BY: EHSAN HAMZEI

2. Table of contents
1- Introduction
2- Query Processing (Related Works)
3- State of the Art
4- Our approach
5- Conclusion

3. Introduction
December 1990 >> First Search engine (W3Catalog) >> Entirely indexed by hand
September 1993 >> WebCrawler >> Finding automatically
…
January 1994 >> Yahoo!
September 1997>> Google

4. 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

5. 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?

6. 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.

7. 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

8. 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

9. 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

10. 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

11. 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)

12. Thanks For Your Attention