1. DeepDive Introduction Dongfang Xu Ph.D student, School of Information, University of Arizona Sept 10, 2015

2. Agenda Brief Introduction KBC model Workflow Reference DeepDive: A Data Management System for Automatic Knowledge Base Construction. Ce Zhang.Ph.D. Dissertation, University of Wisconsin-Madison, 2015.

3. Brief Introduction What is Deep Dive? DeepDive is a new type of data management system that enables one to tackle extraction, integration, and prediction problems in a single system. It is built by generalizing from experience in building more than ten high-quality Knowledge Base Construction (KBC) systems. (Flexible framework) What is KBC? Knowledge Base Construction (KBC) is the process of populating a knowledge base (KB).

4. Brief Introduction Why Deep Dive? Or Why KBC? Its potential to answer key scientific questions. --- Collect facts, contribute to scientific discoveries Typical knowledge base require a large amount of resource. Common problems in scientific area.

5. Brief Introduction Why Deep Dive? Or Why KBC? Its potential to answer key scientific questions. Typical knowledge base require a large amount of resource. Its good performance. ---- Developer thinks about features (extraction rules), not algorithms. ---- Large amounts of data from a variety of sources; ----High quality in extracting complex knowledge and building entity relation; ---- Calibrated probabilities for each assertion it makes; ----Domain knowledge + framework Deep Dive = KBC system in specific domain

6. Brief Introduction General description Deep Dive ----The application of Relational database. All data in Deep Dive is stored in a relational database. ----The main task it to figure out the relation & entity. ---- A selection of target facts typically defined for an IE task. ---- Multiple non-content cues such as layout information may be used to assist extraction, e.g. section headers or their layout in tabular data. ----Extract all kinds of information about the entity and relation, high data volume.

7. Agenda Brief Introduction KBC model Workflow

8. KBC model  Entity: An entity is a real-world person, place, or thing. ----For example, the entity “Michelle Obama 1” represents the actual entity for a person whose name is “Michelle Obama”.  Relation: A relation associates two (or more) entities. ----For example, the entity “Barack Obama 1” and “Michelle Obama 1” participate in the HasSpouse relation, which indicates that they are married.  Mention: a mention is a span of text in an input file that refers to an entity or relationship. ---- “Michelle” may be a mention of the entity “Michelle Obama 1.”  Relation Mention: A relation mention is a phrase that connects two mentions that participate in a relation. ---- “and his wife” =“Barack Obama” and “M. Obama”.

9. KBC model

10. Agenda Brief Introduction KBC model Workflow

11. Work Flow

12.  Input file

13. Work Flow  Input file  User Schema

14. Work Flow  Candidate Generation Feature Extraction

15. Work Flow  Candidate Generation & Feature Extraction

16. Work Flow  Supervision (1) hand-labeling, and (2) distant supervision

17. Work Flow  Supervision (1) hand-labeling, and (2) distant supervision

18. Work Flow  Learning and Inference  In the learning and inference phase, Deep Dive generates a factor graph.

19. Work Flow  Learning and Inference

20. DeepDive Resource http://deepdive.stanford.eduhttp://deepdive.stanford.edu. https://www.youtube.com/watch?v=SfkLvExfl-s http://pages.cs.wisc.edu/~czhang/zhang.thesis.pdf

21. Thank you! Q&A