Abstract
This demo presents Sya; the first full-fledged spatial probabilistic knowledge base construction system. Sya is a comprehensive extension to the DeepDive [18] system that enables exploiting the spatial relationships between extracted relations during the knowledge base construction process, and hence results in a better knowledge base output. Sya runs existing DeepDive programs as is, yet, it extracts more accurate relations than DeepDive when dealing with input data that have spatial attributes. Sya employs a simple spatial high-level language, a rule-based spatial SQL query engine, a spatially-indexed probabilistic graphical model, and an adapted spatial statistical inference technique to infer the factual scores of relations. We demonstrate a system prototype of Sya, showing a case study of constructing a crime knowledge base. The demonstration shows to the audience the internal steps of building the knowledge base, as well as a comparison with the output of DeepDive.
Original language | English (US) |
---|---|
Title of host publication | SIGMOD 2018 - Proceedings of the 2018 International Conference on Management of Data |
Editors | Gautam Das, Christopher Jermaine, Ahmed Eldawy, Philip Bernstein |
Publisher | Association for Computing Machinery |
Pages | 1689-1692 |
Number of pages | 4 |
ISBN (Electronic) | 9781450317436 |
DOIs | |
State | Published - May 27 2018 |
Event | 44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018 - Houston, United States Duration: Jun 10 2018 → Jun 15 2018 |
Publication series
Name | Proceedings of the ACM SIGMOD International Conference on Management of Data |
---|---|
ISSN (Print) | 0730-8078 |
Other
Other | 44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018 |
---|---|
Country/Territory | United States |
City | Houston |
Period | 6/10/18 → 6/15/18 |
Bibliographical note
Publisher Copyright:© 2018 Association for Computing Machinery.
Keywords
- Knowledge base construction
- Spatial knowledge bases