Paper Info
Title
Large-Scale Assessment Of Deep Relational Machines
Abstract
Deep Relational Machines (or DRMs) present a simple way for incorporating complex domain knowledge into deep networks. In a DRM this knowledge is introduced through relational features: in the original formulation of [1], the features are selected by an ILP engine using domain knowledge encoded as logic programs. More recently, in [2], DRMs appear to achieve good performance without the need of feature-selection by an ILP engine (the features are simply drawn randomly from a space of relevant features). The reports so far on DRMs though have been deficient on three counts: (a) They have been tested on very small amounts of data (7 datasets, not all independent, altogether with few 1000s of instances); (b) The background knowledge involved has been modest, involving few 10s of predicates; and (c) Performance assessment has been only on classification tasks. In this paper we rectify each of these shortcomings by testing on datasets from the biochemical domain involving 100s of 1000s of instances; industrial-strength background predicates involving multiple hierarchies of complex definitions; and on classification and regression tasks. Our results provide substantially reliable evidence of the predictive capabilities of DRMs; along with a significant improvement in predictive performance with the incorporation of domain knowledge. We propose the new datasets and results as updated benchmarks for comparative studies in neural-symbolic modelling.
Year
DOI
Venue
2018
10.1007/978-3-319-99960-9_2
INDUCTIVE LOGIC PROGRAMMING (ILP 2018)
Field
DocType
Volume
Domain knowledge,Regression,Computer science,Artificial intelligence,Predicate (grammar),Hierarchy,Machine learning
Conference
11105
ISSN
Citations 
PageRank 
0302-9743
3
0.37
References 
Authors
13
5
Name
Order
Citations
PageRank
Tirtharaj Dash13710.89
Ashwin Srinivasan21167121.29
Lovekesh Vig316031.36
Oghenejokpeme I. Orhobor431.72
Ross D. King51774194.85