1. Entrez Neuron: an OWL/RDFa–based Web Application for Information Exploration and Integration in Neuroscience
Matthias Samwald, Ernest Lim, Peter Masiar, Luis Marenco, Huajun Chen, Thomas Morse, Gordon Shepherd, Perry Miller and Kei Cheung
Yale Center for Medical informatics, Yale University
Medical University of Vienna, Vienna, Austria
School of Computer Science, Zhejiang University
Either Matthias or Kei couldn’t make it by themselves, they asked me to extend their apology to you and I was asked to do the presentation job.
Today, I am going to introduce to you a OWL/RDFa-based Web application for Information Exploration and integration in support of integrative neuroscience research.

2. Outline SenseLab Overview Entrez Neuron Project Future Development
Semantic Data Conversion
Semantic Data Browsing/Querying
Future Development
Online demo
First of all, I am going to give some background information about the SenseLab which is major database gateway supporting international neuroscience research.
Next, I will emphasize on the semantic web application built upon those SenseLab datbase from two different perspectives: data conversion from legacy senselab data to RDF/OWL, and tool for semantic data browsing and querying.
I will end the talk by an online demo.

3. Yale SenseLab Overview
SenseLab’s development was funded by the Human Brain Project to disseminate information related to neuronal membrane properties with special emphasis on the olfactory system.
A major information resource for international neuroscience research.
Have garnered several million hits and unique visits over the years.

4. SenseLab Database SenseLab currently contains eight databases:
These databases combine to provide neuronal, genomics/genetics, proteomics and imaging information to the neuroscience research community and the public.
These resources have been used in neuroscience research, teaching, thesis-writing and database design, and Semantic Web exploration.

5. Snapshot of SenseLab Databases

6. Entrez Neuron Project
A neuron-centric interface that allows for keyword-based queries against a coherent repository of OWL ontologies about neuronal structure, physiology, mathematical models and microscopy.

7. Technical Approach
Careful modeling of neuroscience domain knowledge in consideration of reusing many well-defined general purpose biomedical ontologies.
Convert legacy Senselab data to desired RDF/OWL model.
Map out to external biomedical resources.
Provide integrated ontological browse, search services over the knowledge base.
Use RDFa to publish the knowledge as HTML.

8. Ontology modeling
Basic ontology: an ontology containing basic class hierarchies and relations was manually created, based on the structure of existing SenseLab databases.
The ontologies were built upon established foundational ontologies in order to maximize the interoperability with other existing and forthcoming biomedical Semantic Web resources. These ontologies were:
the Relation Ontology [RO] from the Open Biomedical Ontologies repository [OBO], which defines basic relations such as 'part of', 'participant of' or 'contained in'.
the Basic Formal Ontology [BFO], which defines basic classes such as 'process', 'object', 'quality' or 'function'.
This basic ontology could not be created from the database structure in an automated process because this would not have resulted in a logically consistent ontology. This ontology was edited by a domain expert, based on inspection and manual editing with Protege 3.2 [PROTEGE] and Topbraid Composer [TOPBRAID].

9. Data conversion
Based on this manually created basic ontology, the data from the SenseLab databases were then automatically converted to OWL using programs written in Java and Python.
The automated export scripts extends the manually created basic ontology through the creation of subclasses, OWL property restrictions and individuals.

10. Data Conversion: Mapping Out
Mappings were made to the following ontologies:
the BAMS ontology which was derived from the Brain Architecture Management System [BAMS]
the Subcellular Anatomy Ontology (SAO) created by the Cell Centered Database project. [SAO]
the BirnLex ontology developed by members of the Biomedical Informatics Research Network [BIRNLEX]
the Common Anatomy Reference Ontology (CARO) [CARO]
the Gene Ontology [GO]
the Ontology of Biomedical Investigation (OBI) [OBI]

11. Data Conversion: Mapping Out
The mappings were made with the following cross-ontology relations: owl:equivalentClass, rdfs:subClassOf and the "has part" relation from the OBO relation ontology.

12. Data Conversion: Mapping Out
Examples of relations ('mappings') spanning between classes from the NeuronDB ontology (in the middle) and classes from external ontologies.

13. Outcome of SenseLab conversions
Ontology
Subject-predicate-object triples ('RDF triples')
Named classes (including imports)
Individuals (including imports)
Properties (including imports)
NeuronDB
21010
1400
1510 60
ModelDB
3720
1410
1800
BrainPharm
810
1710
1830 80
NeuronDB – BFO mapping
380
1500 70
NeuronDB – SAO mapping 40
2160
1550
330
NeuronDB – Birnlex – BAMS – OBI mapping
130
3640
1650
190
(very brief description of ontologies: they are small, but partly very complex) (give brief overview of number of triples etc.)

14. Example Queries W3C note on http://www.w3.org/TR/hcls-senselab/
Example query
Ontologies needed for query
Return all neuron types that are located in the Neocortex or some part of the Neocortex, and show research notes and Pubmed references for each.
NeuronDB
Return all neurons that use GABA as a neurotransmitter and that have receptors for Glutamate located on their dendrites.
NeuronDB, ModelDB
Return all neurons that might be affected in the early phase of Alzheimer's disease.
NeuronDB, BrainPharm
Return available mathematical models for all neurons that exhibit A-type potassium ion currents on their membranes.
W3C note on

15. But how can end-users profit?
Lack of user-friendly interfaces Spectrum of Semantic Web user interfaces with two extremes:
Very flexible, but very generic and therefore not user friendly
Very specific and user-friendly, but only geared towards a small set of ontological entities
Practical user interfaces probably need to be positioned somewhere in the middle

16. Design the User Interface
the facets in dynamic taxonomies, so that the search user can see exactly the options they have available at any time. They can switch easily between searching and browsing, using their own terminology for search while recognizing the organization and vocabulary of the data.
Features for metadata search include
Displaying aspects of the current results set in multiple categorization schemes
Showing only populated categories, no dead-ends (links leading to empty lists)
Displaying a count of the contents of each category, warning the user how many more choices they will see
Generating groupings on the fly, such as size, price or date
Drill down by facet, so a diamond buyer could choose price, clarity, size and setting.
Adding special facets within categories: a Yellow Pages site would want to show cuisine and location for restaurant listings but not plumbers.

17. Key Features Facet Search enabled by ontology description.
Use RDFa to encode the semantic information to HTML

18. The Entrez Neuron Project
talk about RDFa a bit. RDFa is a good way of giving users the means to 'merge' RDF/OWL data from diverse data sources on their own, through simple cutting & pasting

19. Future developments Adding more RDF/OWL data Querying and Reasoning
PDSP Ki database (ligand-receptor interactions)
SWAN ontology (Alzheimer's disease hypotheses)
Further OBO ontologies
Querying and Reasoning
Switch to using SPARQL (instead of Oracle RDF query language)
Explore and expand reasoning capabilities

20. Future developments User interface Community development
Add more features to the existing user interface
3D Visualization
Community development
Establish Entrez Neuron as an open community platform

21. Acknowledgement Cell Centered Database: Maryann Martone, Willy Wong
Science Commons: Alan Ruttenberg
Oracle: Melliyal Annamalai, Alan Wu

22. Thanks for your Attention