Faculties: Latifur Khan Bhavani Thuraisingham

1. Semantic Web Research at University of Texas at Dallas(Schema Matching + Storage & Retrieval of RDF graph) Faculties: Latifur KhanBhavani Thuraisingham

2. Semantic Matching in the GIS Domain Jeffrey Partyka (Ph.D. Student) Faculties: Funded by Latifur KhanBhavani Thuraisingham

3. Schema Matching • Performing semantic similarity between two tables by mapping the properties of instances to one another: EBD similarity

4. Jeffrey Partyka, Neda Alipanah, Nilesh Singhania, Latifur Khan, Bhavani Thuraisingham, “Content Based Ontology Matching for GIS Datasets“, ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2008), Page: 407-410, Irvine, California, USA, November 2008. • Jeffrey Partyka, Neda Alipanah, Nilesh Singhania, Latifur Khan, Bhavani Thuraisingham, “Content Based Ontology Matching for GIS Datasets“, ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2008), Page: 407-410, Irvine, California, USA, November 2008. • Jeffrey Partyka, Neda Alipanah, Nilesh Singhania, Latifur Khan, Bhavani Thuraisingham, “Content Based Ontology Matching for GIS Datasets“, ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2008), Page: 407-410, Irvine, California, USA, November 2008. • Jeffrey Partyka, Neda Alipanah, Nilesh Singhania, Latifur Khan, Bhavani Thuraisingham, “Content Based Ontology Matching for GIS Datasets“, ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2008), Page: 407-410, Irvine, California, USA, November 2008. Representing types using N-grams* • Use commonly occurring N-grams in compared columns to determine similarity (N = 2) CA CB N-gram types from A.StrName = {LO, OC, CU,ST,…..} N-gram types from B.Street = {TR, RA, R4, 5/,…..} *Jeffrey Partyka, Neda Alipanah, Latifur Khan, Bhavani Thuraisingham & Shashi Shekhar, “Content Based Ontology Matching for GIS Datasets“, ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2008), Page: 407-410, Irvine, California, USA, November 2008.

5. How do we measure N-gram similarity between columns? • Entropy-Based Distribution (EBD) • EBD is a measurement of type similarity between 2 columns: • EBD takes values in the range of [0,1] . Greater EBD corresponds to more similar type distributions between compared columns. EBD = H(C|T)C = C1 UC2 H(C)

6. Entropy and Conditional Entropy Entropy: measure of the uncertainty associated with a random variable: Conditional Entropy: measures the remaining entropy of a random variable Y given the value of a second random variable X

7. Visualizing Entropy and Conditional Entropy H(C) = –Σpi log pi for all x є C1 U C2 H(C | T) = H (C,T) – H(C) for all x є C1 U C2 and t є T

8. Faults of this Method • Semantically similar columns are not guaranteed to have a high similarity score A є O1 B є O2 2-grams extracted from A: {Da, al, la, as, Ho, ou, us…} 2-grams extracted from B: {Sh, ha, an, ng, gh, ha, ai, Be, ei, ij…}

9. Introducing Google Distance * Jeffrey Partyka, Neda Alipanah, Latifur Khan, Bhavani M. Thuraisingham, Shashi Shekhar, “Ontology Alignment Using Multiple Contexts”, International Semantic Web Conference (ISWC) (Posters & Demos), Karlsruhe, Germany, October, 2008.

10. K-medoid + NGD instance similarity Extract distinct keywords from compared columns Step 1 C1 C2 C1 є O1 C2 є O2 Keywords extracted from columns = {Johnson, Rd., School, 15th,…} Group distinct keywords together into semantic clusters Step 2 : Column 1 “Rd.”,”Dr.”,”St.”,”Pwy”,… “Johnson”,”School”,”Dr.”…. : Column 2 C1UC2 Similarity = H(C|T) / H(C) Calculate Similarity Step 3

11. Problems with K-medoid + NGD* It is possible that two different geographic entities (ie: Dallas, TX and Dallas County) in the same location will have a very low computed NGD value, and thus, be mistaken for being similar: similarity = .797 *Jeffrey Partyka, Latifur Khan, Bhavani Thuraisingham, “Semantic Schema Matching Without Shared Instances,” to appear in Third IEEE International Conference on Semantic Computing, Berkeley, CA, USA - September 14-16, 2009.

12. Using geographic type information* We use a gazetteer to determine the geographic type of an instance: O1 Geotypes O2 *Jeffrey Partyka, Latifur Khan, Bhavani Thuraisingham, “Geographically-Typed Semantic Schema Matching,” submitted to ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2009), Seattle, Washington, USA, November 2009.

13. Disambiguating Geographic Types For A Given Instance We can use metadata and other information to reduce the number of type possibilities for a given instance: City Dallas County Dallas City

14. Geographic Types + NGD It is now possible to make corrections for the geographic co-occurrence mistakes of NGD: similarity = .398

15. Disambiguation Using latlong values • Each input consists of a name and coordinates (Lat/Long values). • Our knowledge base consists of records for a number of different geospatial features such as streets, lakes, schools, etc. for the entire US. • Each entry in the knowledge base contains, coordinates and other spatial information such as length and area of the landmark.

16. Disambiguation Using latlong values (contd..) Geo-Database

17. Disambiguation Using latlong values (contd..) • We first select look for the entries with similar name in knowledge base. • Next, for each feature type in the knowledge base, we choose the entry which is located closest to the input. • In case of two features having close proximity to the input, we disambiguate the feature type on the basis of geospatial properties like area and perimeter.

18. Attribute Weighting • Default weighting scheme is to treat all 1-1 matches between properties/attributes with equal importance: 50% 50%

19. Results of Geographic Matching Over 2 Separate Road Network Data Sources