Improving discovery in Life Sciences Linked Open Data Cloud

Life Sciences Linked Open Data Query Federation Query Engine Data Cataloguing Data Linking a posteriori integration

Healthcare

Improving discovery in Life Sciences
Linked Open Data Cloud
Ali Hasnain

We want to query the content, not the source
Proteins
Molecules
Genes
Diseases

A Linked Life Sciences Roadmap
Proteins
Molecules
Genes
Diseases
:Protein
:Molecule
:Gene
:Disease
Uniprot
PDB
Pfam PROSITE
ProDom
Uniref
UniPark Daily
medDrug
Bank ChemBL
Pub
Chem KEGG
Gene
Ontology
GeneID
Affy
metrix
Homo
gene
MGI
Disea
some
SIDER

2- Possible Solutions
To assemble queries over multiple graphs at multiple endpoints,
either
vocabularies and ontologies are reused, Or
translation maps between different terminologies are created (“a posteriori
integration”)

Hypothesis
"Given a heterogeneous Life Sciences Linked Open Data
corpus, an active Compendium containing concepts from
distinct endpoints and properties connecting these
concepts, that can be (partially) leveraged to achieve "a
posteriori" integration"

Methodology
Cataloguing and Linking Life Sciences
LOD Cloud.
Ali Hasnain, Ronan Fox, Stefan Decker
and Helena F. Deus
18th International Conference on
Knowledge Engineering and Knowledge
Management (EKAW 8 - 12 October
2012), Galway, Ireland

Describing DataSets- an Extract from Catalogue

BioFed: Federated Query Processing over Life
Sciences Linked Open Data
BioFed: Federated Query Processing
over Life Sciences Linked Open Data
Ali Hasnain, Qaiser Mehmood, Syeda
Sana e Zainab, Muhammad Saleem,
Claude Warren and Stefan Decker
JBMS-2015 (under review)

FedViz System Architecture
15
FedViz is an online application that
provides Biologist a flexible visual
interface to formulate and execute both
federated and non-federated,
SPARQL queries.
It translates the visually assembled
queries into SPARQL equivalent and
execute using query engine (FedX,
BioFed).

Conclusion and Future Work
• Cataloguing and Linking – Compendium of Life sciences
Datasets
• BioFed and FedViz a Particle Applications
• Improving Catalogue/ Adding Statistical/ Latency etc
information for customised and targeted query processing.
• Evaluating BioFed with other available Query Engines
including Anapsid, Splendid etc.

The life sciences domain has been one of the early adopters of linked data and, a considerable portion of the Linked Open Data cloud is comprised of datasets from Life Sciences Linked Open Data (LSLOD). The deluge of biomedical data in the last few years, partially caused by the advent of high-throughput gene sequencing technologies, has been a primary motivation for these efforts. This success has lead to the growth in size of data sets and to the need for integrating multiples of these data-sets. This growth requires large scale distributed infrastructure and specific techniques for managing large linked data graphs. Especially in combination with Semantic Web and Linked Data technologies these promises to enable the processing of large as well as semantically heterogeneous data sources and the capturing of new knowledge from those. In this tutorial we present the state of the art in large data processing, as well as the amalgamation with Linked Data and Semantic Web technologies for better knowledge discovery and targeted applications. We aim to provide useful information for the Knowledge Acquisition research community as well as the working Data Scientist.

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