Title
Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks.
Abstract
Many real world systems need to operate on heterogeneous information networks that consist of numerous interacting components of different types. Examples include systems that perform data analysis on biological information networks; social networks; and information extraction systems processing unstructured data to convert raw text to knowledge graphs. Many previous works describe specialized approaches to perform specific types of analysis, mining and learning on such networks. In this work, we propose a unified framework consisting of a data model -a graph with a first order schema along with a declarative language for constructing, querying and manipulating such networks in ways that facilitate relational and structured machine learning. In particular, we provide an initial prototype for a relational and graph traversal query language where queries are directly used as relational features for structured machine learning models. Feature extraction is performed by making declarative graph traversal queries. Learning and inference models can directly operate on this relational representation and augment it with new data and knowledge that, in turn, is integrated seamlessly into the relational structure to support new predictions. We demonstrate this systemu0027s capabilities by showcasing tasks in natural language processing and computational biology domains.
Year
Venue
Field
2017
arXiv: Artificial Intelligence
Data mining,Query language,Graph traversal,Computer science,Statistical relational learning,Feature extraction,Unstructured data,Information extraction,Artificial intelligence,Declarative programming,Data model,Machine learning
DocType
Volume
Citations 
Journal
abs/1707.07794
0
PageRank 
References 
Authors
0.34
15
7
Name
Order
Citations
PageRank
Parisa Kordjamshidi114318.52
Sameer Singh2106071.63
Daniel Khashabi311415.14
Christos Christodoulopoulos4749.67
Mark Sammons524019.55
Saurabh Sinha61398.57
Dan Roth77735695.19