Reasoning with Ontologies

"Science evolved into a hydra who's heads are autonomous and quite ignorant about what happens in most other heads." (in Fuzzy Logic and the Semantic Web).

Semantic Web envisions the possibility of having computers make decisions about relevant data and retrieve knowledge in a human-like style. But this is no easy challenge, particularly in fields of knowledge that are driven by empirically closing the gaps of incomplete knowledge. To make Semantic Web usefull in this fields, particularly in Biology where domains of knowledge have become almost as numerous as the researchers invovled in it, rules of inference will only ever make any logical sense if they are built and designed by the the researchers who retrieve and know the data.

Report S3DB bugs

This section is intended for S3DB users to report bugs, inconsistencies or just to give feedback on how to better use and manage S3DB. Just leave your comment with the description of the problem and the URL where your S3DB is installed (if applicable), and we will try to provide you with an answer.

Incubating Ontologies

To incubate an ontology is to create a data model to integrate in a common structure. A particular set of data without a priori assumptions about how the final product should look like. The task of turning biological data into manageable digitalized formats demands for annotations of the data, which often happens in a sloppy, deorganized manner. Structures for organizing it often take considerable time to developed and are not always fully usefull. Agreement on all parts involved is also hard to achieve.
Therefore, incubating an ontology is the means through which unstructured, annotated data slowly and steadily gains organization by maintenance and small aditions until it turns into a usefull ontology, a strong infrastructure gained from inferences and corrections on the initial annotations, that can be distributed, shared and fit the purpose of bridging the informational gap that exists between universal data storage and mining.
Using an analogy, such ontology development would be somewhat like the process of building a bridge between two points. The first bridge would be "sloppy", just to fit the purpose of enabling a rough passage between the two points. With time and usage, this bridge could then begin to be elaborated, the materials used to build it would differ, become more sturdy until one could easily and safely go through it. Of course, not all first stage bridges achieve this last stage, but those that do will hardly ever lose it's importance as a landmark.

Why go Semantic?

It has recently been discussed in the mindswap weblog (6th November 2006) about the need to discuss and clarify what do semantic web technologies bring new into the field of data analysis, and why not remain in the relational?
I think they have a very good point. Why go semantic when we can stay on the relational? Anyway, anyone who matters already knows the relational, why learn RDF or OWL, right?
No. I think what is so amazing and breakthrough technology about the semantic web is it's intuitiveness. While anyone can understand and visualize nodes connected to nodes that make up a whole, having to memorize tables and table connects, primary and foreign keys, etc, is a bit more cumbersome.
Semantic web will make data resources accessible to more people and to the people that matter - the ones generating the data. I think what defines a technology is the ratio between it's usefullness and the amount of computational support needed.
Semantic web will win, I think, mostly by sociological reasons - if the biologist is the data modeler, and he knows that the tools to analyse his data can be called by complying to a particular ontology (this is my definition of ontology-driven data analysis), then it will become an incentive to use such ontology. With the widespread of both databases and algorithms dependent on ontologies, changes in the ontology will not necessarily affect the flow of analysis, as can happen with relational databases.

Ontology Driven data Analysis

Many researchers have come to realize that ontologies will definitelly bridge the gap between databases and analysis algorithms. But how to do that in a level of abstraction that is usefull regardless of the data structure? Data analysis tools often become obsolete and need to be adapted as new significant parameters emerge from the data collection. Ontologies are already being used as effective tools for integrating databases and data mining tools for deriving knowledge. But this often happens at a large scale of data wharehouses, where the bench biologist trying to derive conclusions from their data have little to say in the manner the data analysis is conducted.
Ontology driven analysis tools should be flexible enough to accept entry of new parameters that might, or not, improve the probability of the conclusions.

New release

The new release of S3DB is finnaly out.
The bugs reported in the previous version have been fixed and many new features have been added.
This new S3DB version (1.0) comes equipped with RDF project importer (besides the already existing XML project importer), as well as enabling projects to share resources, a functionality that has been deeply needed, particularly in projects regarding the life sciences community.
Also, installation has become easier for MySQL users, as the database now creates itself (in previous versions, a small step of creating the database in the MySQL command line as well as creating a user to access it were necessary).

Rich Matlab client at BioinformaticStation.org

The recent development of a query language for automated interaction with S3DB, designated as S3QL, has enabled the development of client applications with rich graphic capabilities. Taking advantage of Matlab's new compiler toolbox, such a prototype is being developed at BioinformaticStation.org. A dedicated Blogger at BioinformaticStation.blogspot.com discusses this further and the S3DB related bits will be collected here.