This document describes the Bio2RDF project, which aims to integrate biological data from multiple sources using Semantic Web technologies. It proposes applying linked data principles and semantic graph ranking methods to provide an integrated search interface for querying post-genomic knowledge about human and mouse. The results section describes the initial Bio2RDF knowledge map integrating data from 30 sources, with statistics on its coverage. A demo query about Paget disease is also presented to illustrate searching the data using SPARQL.
The Linked Open Data (LOD) cloud contains tremendous amounts of interlinked instances, from where we can retrieve abundant knowledge. However, because of the heterogeneous and big ontologies, it is time consuming to learn all the ontologies manually and it is difficult to observe which properties are important for describing instances of a specific class. In order to construct an ontology that can help users easily access to various data sets, we propose a semi-automatic ontology inte- gration framework that can reduce the heterogeneity of ontologies and retrieve frequently used core properties for each class. The framework consists of three main components: graph-based ontology integration, machine-learning-based ontology schema extraction, and an ontology merger. By analyzing the instances of the linked data sets, this framework acquires ontological knowledge and constructs a high-quality integrated ontology, which is easily understandable and effective in knowledge ac- quisition from various data sets using simple SPARQL queries.
What is Reproducibility? The R* brouhaha (and how Research Objects can help)Carole Goble
presented at 1st First International Workshop on Reproducible Open Science @ TPDL, 9 Sept 2016, Hannover, Germany
http://repscience2016.research-infrastructures.eu/
RARE and FAIR Science: Reproducibility and Research ObjectsCarole Goble
Keynote at JISC Digifest 2015 on Reproducibility and Research Objects in Scholarly Communication
Includes hidden slides
All material except maybe the IT Crowd screengrab reusable
Some tools developed at OEG (Ontology Engineering Group) for facilitating ontology engineering activities as evaluation, documentation, releasing and publication.
PaNOSC and Research Data Management / Battery2030+ Initiative Workshop / 12 M...PaNOSC
On March 12th, 2021, PaNOSC coordinator, Andy Götz, attended with an invited talk the 2nd online workshop of the Battery2030+ Initiative, focused on the benefits of research data management (RDM) and guidelines, through the showcase of best practice examples, including PaNOSC.
Due to the increasing uptake of semantic technologies, ontologies are becoming part of a growing number of software development projects. As a result, ontology development teams have to combine their activities with software development practices. In this presentation some practices, tools and examples of new trends in ontological engineering are provided.
The Linked Open Data (LOD) cloud contains tremendous amounts of interlinked instances, from where we can retrieve abundant knowledge. However, because of the heterogeneous and big ontologies, it is time consuming to learn all the ontologies manually and it is difficult to observe which properties are important for describing instances of a specific class. In order to construct an ontology that can help users easily access to various data sets, we propose a semi-automatic ontology inte- gration framework that can reduce the heterogeneity of ontologies and retrieve frequently used core properties for each class. The framework consists of three main components: graph-based ontology integration, machine-learning-based ontology schema extraction, and an ontology merger. By analyzing the instances of the linked data sets, this framework acquires ontological knowledge and constructs a high-quality integrated ontology, which is easily understandable and effective in knowledge ac- quisition from various data sets using simple SPARQL queries.
What is Reproducibility? The R* brouhaha (and how Research Objects can help)Carole Goble
presented at 1st First International Workshop on Reproducible Open Science @ TPDL, 9 Sept 2016, Hannover, Germany
http://repscience2016.research-infrastructures.eu/
RARE and FAIR Science: Reproducibility and Research ObjectsCarole Goble
Keynote at JISC Digifest 2015 on Reproducibility and Research Objects in Scholarly Communication
Includes hidden slides
All material except maybe the IT Crowd screengrab reusable
Some tools developed at OEG (Ontology Engineering Group) for facilitating ontology engineering activities as evaluation, documentation, releasing and publication.
PaNOSC and Research Data Management / Battery2030+ Initiative Workshop / 12 M...PaNOSC
On March 12th, 2021, PaNOSC coordinator, Andy Götz, attended with an invited talk the 2nd online workshop of the Battery2030+ Initiative, focused on the benefits of research data management (RDM) and guidelines, through the showcase of best practice examples, including PaNOSC.
Due to the increasing uptake of semantic technologies, ontologies are becoming part of a growing number of software development projects. As a result, ontology development teams have to combine their activities with software development practices. In this presentation some practices, tools and examples of new trends in ontological engineering are provided.
Capturing Context in Scientific Experiments: Towards Computer-Driven Sciencedgarijo
Scientists publish computational experiments in ways that do not facilitate reproducibility or reuse. Significant domain expertise, time and effort are required to understand scientific experiments and their research outputs. In order to improve this situation, mechanisms are needed to capture the exact details and the context of computational experiments. Only then, Intelligent Systems would be able help researchers understand, discover, link and reuse products of existing research.
In this presentation I will introduce my work and vision towards enabling scientists share, link, curate and reuse their computational experiments and results. In the first part of the talk, I will present my work for capturing and sharing the context of scientific experiments by using scientific workflows and machine readable representations. Thanks to this approach, experiment results are described in an unambiguous manner, have a clear trace of their creation process and include a pointer to the sources used for their generation. In the second part of the talk, I will describe examples on how the context of scientific experiments may be exploited to browse, explore and inspect research results. I will end the talk by presenting new ideas for improving and benefiting from the capture of context of scientific experiments and how to involve scientists in the process of curating and creating abstractions on available research metadata.
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...dkNET
Presenter: Chen Li, PhD. Professor, Department of Computer Science, University of California Irvine
Abstract
Many data analytics projects have collaborators with complementary backgrounds, including biologists, bioinformaticians, computer scientists, and AI/ML experts. Many of them have limited experience to code, set up a computing infrastructure, and use MLmodels. Existing tools and services, such as email attachments, GitHub, and Google Drive are inefficient for sharing data and analyses. In this talk, we present an open source system called Texera that provides a cloud computing platform for collaborators to share data and analyses as workflows. After seven years of development, the system has a rich set of powerful features, such as shared editing, shared execution, version control, commenting, debugging, user-defined functions in multiple languages (e.g., Python, R, Java), and support of state-of-the-art AI/ML techniques. Its backend parallel engine enables scalable computation on large data sets using computing clusters. We will show a demo of the system, and present our vision supported by a recent NIH award, dkNET(NIDDK Information Network, https://dknet.org), to serve the diabetes, endocrinology, and metabolic diseases research communities through the FAIR sharing of data and knowledge.
Resource link: https://github.com/Texera/texera
Upcoming webinars schedule: https://dknet.org/about/webinar
Being FAIR: FAIR data and model management SSBSS 2017 Summer SchoolCarole Goble
Lecture 1:
Being FAIR: FAIR data and model management
In recent years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship [1] have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems and Synthetic Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Our FAIRDOM project (http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. The FAIRDOM Platform has been installed by over 30 labs or projects. Our public, centrally hosted Asset Commons, the FAIRDOMHub.org, supports the outcomes of 50+ projects.
Now established as a grassroots association, FAIRDOM has over 8 years of experience of practical asset sharing and data infrastructure at the researcher coal-face ranging across European programmes (SysMO and ERASysAPP ERANets), national initiatives (Germany's de.NBI and Systems Medicine of the Liver; Norway's Digital Life) and European Research Infrastructures (ISBE) as well as in PI's labs and Centres such as the SynBioChem Centre at Manchester.
In this talk I will show explore how FAIRDOM has been designed to support Systems Biology projects and show examples of its configuration and use. I will also explore the technical and social challenges we face.
I will also refer to European efforts to support public archives for the life sciences. ELIXIR (http:// http://www.elixir-europe.org/) the European Research Infrastructure of 21 national nodes and a hub funded by national agreements to coordinate and sustain key data repositories and archives for the Life Science community, improve access to them and related tools, support training and create a platform for dataset interoperability. As the Head of the ELIXIR-UK Node and co-lead of the ELIXIR Interoperability Platform I will show how this work relates to your projects.
[1] Wilkinson et al, The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
Interlinking educational data to Web of Data (Thesis presentation)Enayat Rajabi
This is a thesis presentation about interlinking educational data to Web of Data. I explain how I used the Linked Data approach to expose and interlink educational data to the Linked Open Data cloud
Reproducibility in human cognitive neuroimaging: a community-driven data sha...Nolan Nichols
Access to primary data and the provenance of derived data are increasingly recognized as an essential aspect of reproducibility in biomedical research. While productive data sharing has become the norm in some biomedical communities, human brain imaging has lagged in open data and descriptions of provenance. The overarching goal of my dissertation was to identify barriers to neuroimaging data sharing and to develop a fundamentally new, granular data exchange standard that incorporates provenance as a primitive to document cognitive neuroimaging workflow.
For my dissertation research, I led the development of the Neuroimaging Data Model (NIDM), an extension to the W3C PROV standard for the domain of human brain imaging. NIDM provides a language to communicate provenance by representing primary data, computational workflow, and derived data as bundles of linked Agents, Activities, and Entities. Similar to the way a sentence conveys a standalone thought, a bundle contains provenance statements that parsimoniously express the way a given piece of data was produced. To demonstrate a system that implements NIDM, I developed a modern, semantic Web application platform that provides neuroimaging workflow as a service and captures provenance statements as NIDM bundles. The course of this work necessitated interaction with an international community, which adopted and extended central elements of this work into prevailing brain imaging software. My dissertation contributes neuroinformatics standards to advance the current state of computational infrastructure available to the cognitive neuroimaging community.
Infrastructure for the Data Revolution: How OpenAIRE supports the EC’s Open ...OpenAIRE
OpenAIRE2020 is an Open Access (OA) infrastructure for research which supports open scholarly communication and access to the research output of European funded projects. With over five years experience of supporting the European Commission’s OA policies, OpenAIRE now has a key role in supporting the EC’s Horizon 2020 Open Data Pilot. OpenAIRE’s community network works to gather research outputs, highlight the OA mandate, and advance open access initiatives at national levels. It has National Open Access Desks in over 30 countries, and operates a European Helpdesk system for all matters concerning open access, copyright and repository interoperability. At the same time, OpenAIRE harvests metadata information from a network of Open Access repositories, data repositories, aggregators and OA journals. It then enriches this metadata by linking people, publications, datasets, projects and funding streams. This interlinked information – which currently encompasses more than 13 million publications and 12 thousand datasets from more than 6 thousand data sources – helps optimise the research process, increasing research visibility, facilitating data sharing and reuse and enabling the monitoring of research impact. This presentation will outline how an infrastructure like OpenAIRE can help turn OA policy into successful implementation.
Approach and outcome of the Biodiversity Virtual e-Laboratory (BioVeL) projectAlex Hardisty
Describes what we set out to do, what we achieved, and some of the lessons learnt during the BioVeL project. This presentation was given at the BioVeL final event "BioVeL In Practice and In Future", Paris, 13th November 2014
From Scientific Workflows to Research Objects: Publication and Abstraction of...dgarijo
Presentation of my PhD work to the UPM group on the 12th of Feb of 2014. Summary of goals, motivation, OPMW, Standards, PROV, p-plan, Workflow Motifs, Workflow fragment detection and Research Objects.
Slides from the presentation at IDAMO 2016, Rostock. May 2016.
Most scientific discoveries rely on previous or other findings. A lack of transparency and openness led to what many consider the "reproducibility crisis" in systems biology and systems medicine. The crisis arose from missing standards and inappropriate support of
standards in software tools. As a consequence, numerous results in low-and high-profile publications cannot be reproduced.
In my presentation, I summarise key challenges of reproducibility in systems biology and systems medicine, and I demonstrate available solutions to the related problems.
Open Research: Manchester leading and learningCarole Goble
Open and FAIR science has an international momentum. Large scale communities are striving to make and manage the digital infrastructure needed for scientists to be open as possible, closed as necessary, as expected by the NIH, OECD, UNESCO and the EC. ELIXIR is such a research infrastructure in Europe for Life Sciences. This talk will highlight two of ELIXIR's Open Science resources built by Open Science communities to enable life science researchers to be open, and led by Manchester. And how can we learn from these and bring these practices to Manchester?
Launch: Manchester Office for Open Research, 4th April 2022
https://www.openresearch.manchester.ac.uk/
Building mashup from Linked Data using Bio2RDF’s Talend components François Belleau, Vincent, Emonet, Arnaud Droit Centre de Biologie Computationnelle Centre de recherche du CHUQ
The initial Bio2RDF project description shown at Semantic Web bird of a feather during ISMB2005.
Thank to Chistopher Baker, Kei Cheung, Johanne Luciano and Eric Neumann for initial inspiration.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
1. BIO2RDF : A Semantic Web Atlas of
post genomic knowledge about
Human and Mouse
François Belleau, Nicole Tourigny,
Benjamin Good and Jean Morissette
● Centre de Recherche du CHUL, Université Laval
● Département d'informatique et de génie logiciel, Université Laval
4. Evry, June 27, 2008 CHUL research center Laval University 4
Outline
Introduction
− Problem definition
− Proposed approach
− The 4 rules of linked data
− Related Work
Results
− Bio2RDF first knowledge map
− Semantic ranking
Paget query demo with SPARQL
Future work and Conclusion
5. Evry, June 27, 2008 CHUL research center Laval University 5
Problem definition
● The objective of data integration is to
make data distributed over a number of
distinct, heterogeneous databases
accessible via a single interface
[Davidson 1995].
● We already use global text search engine
on the web (Google, Yahoo).
● There is many specialized integrated
search tools in bioinformatics (NCBI
Entrez, EBI search, KEGG GenomeNet).
13. Evry, June 27, 2008 CHUL research center Laval University 13
Proposed approach
● Apply the semantic web model to data
integration in bioinformatics;
● Use a PageRank [Brin 1998] variation
adapted to semantic graph, a method
analog to Aleman-Meza group's work: the
LinkRank;
● Adopt standard (RDF, OWL) and use
existing software (Sesame, Virtuoso,
PiggyBank).
14. Evry, June 27, 2008 CHUL research center Laval University 14
Outline
Introduction
− Problem definition
− Proposed approach
− The 4 rules of linked data
− Related Work
Results
− Bio2RDF first knowledge map
− Semantic ranking
Paget query demo with SPARQL
Future work and Conclusion
16. Evry, June 27, 2008 CHUL research center Laval University 16
Rule #1: Use URIs as names for
things.
● Using normalized identifier to name
concept is already a reality in biology
domain.
● Hexokinase is GO:0004396
● Definition :
− Catalysis of the reaction: ATP + D-hexose =
ADP + D-hexose 6-phosphate.
● Synonym of EC:2.7.1.1
17. Evry, June 27, 2008 CHUL research center Laval University 17
Rule #2 : Use HTTP URIs so that
people can look up those names.
● Derefencable URL
● The Banff Manifesto rule for URN
− urn:bm:public_namespace:private_identifier
● Normalized URL according to Banff
Manifesto:
http://bio2rdf.org/public_namespace:private_identifier
● http://bio2rdf.org/go:0004396
20. Evry, June 27, 2008 CHUL research center Laval University 20
Outline
Introduction
− Problem definition
− Proposed approach
− The 4 rules of linked data
− Related Work
Results
− Bio2RDF first knowledge map
− Semantic ranking
Paget query demo with SPARQL
Future work and Conclusion
23. Evry, June 27, 2008 CHUL research center Laval University 23
Related work – Linked data map
● If we were to draw a map of the existing
relations between linked data from
bioinformatics database providers, what
would it look like?
● Could we measure the amount of post
genomic knowledge available related to a
mouse or human genome sequence?
● Could it help answer the what is known
question?
29. Evry, June 27, 2008 CHUL research center Laval University 29
Outline
Introduction
− Problem definition
− Proposed approach
− The 4 rules of linked data
− Related Work
Results
− Bio2RDF first knowledge map
− Semantic ranking
Paget query demo with SPARQL
Future work and Conclusion
33. Evry, June 27, 2008 CHUL research center Laval University 33
Outline
Introduction
− Problem definition
− Proposed approach
− The 4 rules of linked data
− Related Work
Results
− Bio2RDF first knowledge map
− Semantic ranking
Paget query demo with SPARQL
Future work and Conclusion
47. Evry, June 27, 2008 CHUL research center Laval University 47
Bio2RDF Semantic Web Atlas
in numbers
● 30 different datasources, 30 different
namespaces
− go, geneid, uniprot, pubmed, pdb, reactome, omim,
etc.
● 195 namespaces referencing non-rdfized
datasource
− cog, genethon, tigr, cath, goa, etc.
● 8 millions topics
● 65 millions triples
● 973 Mo, size of N3 format compressed data
− http://bio2rdf.org/download/bio2rdf-atlas-080414.n3.gz
48. Evry, June 27, 2008 CHUL research center Laval University 48
Bio2RDF Semantic Web Atlas
in statistics
● Openess Ratio (OR) of 0.58
● Averange Link Rank (ALR) of 4.7
● 8 millions topics are connected by 19 millions
relations within the graph
● 58 % of URIs are referencing the open world
outside the graph
● 19 % of knowledge gain because of the mashup
effect
49. Evry, June 27, 2008 CHUL research center Laval University 49
Outline
Introduction
− Problem definition
− Proposed approach
− The 4 rules of linked data
− Related Work
Results
− Bio2RDF first knowledge map
− Semantic ranking
Paget query demo with SPARQL
Future work and Conclusion
52. Evry, June 27, 2008 CHUL research center Laval University 52
SPARQL query in a URL
http://bio2rdf.org:8890/sparql?defaultgraph
uri=&query=CONSTRUCT+%7B%0D%0A%3Fs1+%3Fp1+%3Fo1+.%0D%0A
%3Fs1+%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22rdf
syntaxns%23type%3E+%3Ftype+.+%0D%0A%3Fs1+%3Chttp%3A%2F
%2Fwww.w3.org%2F2000%2F01%2Frdfschema%23label%3E+
%3Flabel.+%0D%0A%3Fs1+%3Chttp%3A%2F%2Fbio2rdf.org
%2Fbio2rdf%23linkRank%3E+%3FlinkRank.+%0D%0A%7D%0D
%0AWHERE+%7B%0D%0A%3Fs1+%3Fp1+%3Fo1+.+%0D%0A%3Fo1+bif
%3Acontains+%22paget%22+.%0D%0A%3Fs1+%3Chttp%3A%2F
%2Fwww.w3.org%2F1999%2F02%2F22rdfsyntaxns%23type%3E+
%3Ftype+.+%0D%0A%3Fs1+%3Chttp%3A%2F%2Fwww.w3.org
%2F2000%2F01%2Frdfschema%23label%3E+%3Flabel.+%0D%0A
%3Fs1+%3Chttp%3A%2F%2Fbio2rdf.org%2Fbio2rdf%23linkRank
%3E+%3FlinkRank.+%0D%0A%7D%0D%0A%0D%0A%0D%0A%0D
%0A&format=application%2Frdf%2Bxml&debug=on
56. Evry, June 27, 2008 CHUL research center Laval University 56
Outline
Introduction
− Problem definition
− Proposed approach
− The 4 rules of linked data
− Related Work
Results
− Bio2RDF first knowledge map
− Semantic ranking
Paget query demo with SPARQL
Future work and Conclusion
57. Evry, June 27, 2008 CHUL research center Laval University 57
Future works
● Create new rdfizer for public data source;
● Build a community of users around the
Bio2RDF project (visit the Google group);
● Connect more datasources to Bio2RDF by
building collaboration between research
groups;
● Offer a public SPARQL endpoint based on
Virtuoso server :
− http://bio2rdf.org:8890/sparql
60. Evry, June 27, 2008 CHUL research center Laval University 60
Acknowlegments
Jean Morissette
Nicole Tourigny
Benjamin Good
Bioinformatics lab’s team at CHUL Research Center :
Philippe Rigault
Marc-Alexandre Nolin
Thanks to the essential annotators and data provider
and to developers of open source project :
Sesame, Virtuoso and PiggyBank.
François Belleau was a recipient of a studentship from Génome Québec.
This work have been financed in part by the Atlas of Genomic Profiles of Steroid
Action, a Genome Canada project. BMG is funded by Pacific Century
and University of British Columbia Graduate Fellowships.
61. Evry, June 27, 2008 CHUL research center Laval University 61
http://bio2rdf.org
Query the graph with SPARQL
http://bio2rdf.org:8890/sparql
Download our software
http://sourceforge.net/projects/bio2rdf/
Download the Atlas data in N3 format
http://bio2rdf.org/download
Join our group
http://groups.google.ca/group/bio2rdf
Contact us at bio2rdf@gmail.com