Reading Material

Main Reference

Wilkinson MD, Vandervalk B, McCarthy L., SADI Semantic Web Services - 'cause you can't always GET what you want!, Proceedings of the IEEE International Workshop on Semantic Web Services in Practice, 7-11 Dec. 2009, Singapore, pp 13-18, doi:10.1109/APSCC.2009.5394148.


Module 1 :  Semantic Web

Linked data tutorial
OWL primer
Protege 4 / OWL ontology tutorial.

SemTech OWL tutorial (Part 1 & Part 2)

I. Horrocks, Optimising tableaux decision procedures for description logics, PhD thesis, Sections 1 - 3.
Sean Bechhofer, OWL Reasoning Examples

Module 2: Motivation for SADI Web Services
Web Services Tutorial

Module 3 : SADI Semantic Web Services

WSDL tutorial
SAWSDL usage guide

Semantic Automated Discovery and Integration
What is a SADI service
SADI input and output classes

For those who will be building a service in Java:
Building a SADI service in Java

For those who will be building a service in Perl:
SADI Perl extension

For those unfamiliar with Taverna:
Getting started with Taverna


Uses Cases

Chepelev L, Riazanov A, Kouznetsov A, Low HS, Dumontier M, Baker CJO, Prototype Semantic Infrastruct. for Auto. Small Molecule Classific. Annot. in Lipidomics,

As part of a case study in the utility of Semantic Web Technologies for chemical classification, we have developed a prototype framework for automated lipid classification and annotation. This framework comprises of ; a formal lipid ontology developed in OWL-DL, a set of federated Semantic Web services deployed within the SADI framework used to invoke an automated logical classification task. The first service, a structural annotation service, detects and enumerates relevant chemical subgraphs on a given input chemical graph. Secondly a classifier service assigns chemical entities to appropriate ontology classes by reasoning over class description in the ontology and checking them against the set of chemical subgroups provided by the structure annotation service. We illustrate the utility of these core services using the use case of Eicosanoid classification and combine them with additional SADI services linking the annotated lipids to related proteins found in the biomedical literature or within the public databases. Using these services we further contrast the performance of automated Eicosanoid classification with the existing lipid nomenclature systems and curated lipid databases and reflect on the contribution of our methodology in the context of high-throughput Lipidomics.

Laurila JB, Naderi N, Witte R, Riazanov A, Kouznetsov A., Baker CJO, Algorithms and semantic infrastruct. for mutation impact extraction and grounding, BMC Genomics 2010, 11(suppl.4): S24.

We present the first rule-based approach for the extraction of mutation impacts on protein properties, categorizing their directionality as positive, negative or neutral. Furthermore protein and mutation mentions are grounded to their respective UniProtKB IDs and selected protein properties, namely protein functions to concepts found in the Gene Ontology. The extracted entities are populated to an OWL-DL Mutation Impact ontology facilitating complex querying for mutation impacts using SPARQL. We illustrate retrieval of proteins and mutant sequences for a given direction of impact on specific protein properties. Moreover we provide programmatic access to the data through semantic web services using the SADI (Semantic Automated Discovery and Integration) framework.

Riazanov A, Laurila J. B. and C. J. O. Baker, Deploying the Mutation Impact Mining Pipeline with SADI: an Exploratory Case Study, Proc. Workshop on Annotation, Interpretation and Management of Mutations (AIMM-2010) Ghent, Belgium, September 26th, CEUR-WS Vol-645 2010.

This paper explores the possibility of using the SADI framework as a medium for publishing our mutation impact software and data. Here we describe a case study exploring and demonstrating the utility of the SADI approach in our context. We describe several SADI services we created based on our text mining API and data, and demonstrate how they can be used in a number of biologicaly meaningful scenarios through a SPARQL interface (SHARE) to SADI services. In all cases we pay special attention to the integration of mutation impact services with external SADI services providing information about related biological entities, such as proteins, pathways, and drugs.

The NEP-59 CBRASS: Canadian Bioinformatics as Semantic Services project is funded by CANARIE



Semantic Web languages:


Principal Investigators Labs:


Ottawa, ON

Saint John, NB


Upcoming Event:

2nd Web Publishing of Scientific Data and Services Training Course

Date: August 2011

Location: Vancouver (BC)