Reading List

Papers ( related to data model and scalability)

1. A review of Relational Machine Learning for Knowledge Graphs. This gives ideas how to describe the application of Saul graph for the popular notion of knowledge graphs.
2. KeLP: a Kernel-based Learning Platform for Natural Language Processing. This gives a good example for how to write a paper about feature extraction library that facilitates machine learning.
3. SystemT: A Declarative Information Extraction System
4. An Algebraic Approach to Rule-Based Information Extraction, related to 3.
5. GraphX: Graph Processing in a Distributed Dataflow Framework
6. TOKENSREGEX: Defining cascaded regular expressions over tokens. There are several works related to pattern matching and text processing. In Saul we basically can do those kind of processes using Saul's query language with our predefined NLP data model which contains NLP sensors and annotators.
7. [A Survey of Heterogeneous Information Network Analysis] (http://arxiv.org/pdf/1511.04854.pdf). Important from the perspective of information systems and data management related to CIKM community. The features like dealing with heterogeneous data and features like metapath are the things we can cover with Saul data model language.