I’ve just found out that I can write complex math equations using Latex syntax on WP. Here’s my first try:
Wow! Great! Learned this from this blog post.
OwlDlTrHReasoner is a new OWL DL reasoning module added to Bossam, which shows much better performance than Bossam’s default OWL DL reasoning module. You can create an instance of
OwlDlTrHReasoner by calling
In Bossam shell, instead of
owltr as follows:
load owltr from http://www.w3.org/TR/2004/REC-owl-guide-20040210/wine;
Yeap. That’s it.
OwlDlTrHReasoner completes a forward-chaining reasoning session over the W3C wine ontology in less than 3 seconds, on my 1.8Ghz Core Duo notebook with 128MB of Java heap memory. Not bad, right?
Doesn’t that reasoner lose too many derivations? Well, actually, I have to see from now on… 🙂 I’d be greatly appreciated for any feedback and problem reports on this new reasoner. 🙂
I’m working on a new implementation of Bossam’s OWL reasoning module. There’re roughly two approaches for rule-based OWL reasoning. One is translation-based approach and the other is meta-reasoning approach. Bossam’s OWL reasoning module originally is implemented in meta-reasoning approach, but now I’m working on a translation-based OWL reasonnig implementation for Bossam. I’ve just finished the first draft of the code and it looks quite promising in the sense of performance!
- A full forward-chained reasoning session on the W3C wine ontology completed in 1.5 seconds!
- A full forward-chained reasoning session on the LUBM benchmark with 100,000 triples completed in 97 seconds! All the 13 sample queries are processed in less than 1 second!
Stay tuned! One major performance-tuned Bossam is coming!
‘Robots in the ubiquitous environments’ are called ubiquitous robots. Ubiquitous robots are basically networked robots. They natively possess communication channels through which they can interact with any kinds of networked agents e.g. information appliances, mobile phones, web services, internet agents, networked sensors and actuators etc. The channels pave the broad way for the robots to the world of globalized and ubiquitous interactions with the real world.
For ubiquitous robots to intelligently interact with the world, they need to understand the situations aroud and react appropriately. Traditional robots did similar things only through their own sensors like camera, ultrasonic sensors, laser scanners, etc. Ubiquitous robots can utilize wider spectrum of data sources e.g. sensors remotely installed in the environment, web services on the internet, and any kinds of other devices wired to the network. As such, ubiquitous robots need context-awareness.
Recently, semantic web technologies are widely adopted for context-awareness. Why? That’s because the semantic web provides tools for building common knowledge infrastructure. The ‘common’ is used to mean that ‘it can be interchanged for global communication’. The semantic web provides ‘the semantic glue’. RDF and OWL are used to define vocabularies for representing various kinds of contexts. The vocabularies are used to build context models and instances. Context-awarenss applications or services convert raw context data into the symbolic representation using the vocabularies and try to exploit application-level contexts that’re meaningul to trigger context-aware functionalities. OWL inferencing and rule reasoning are two main enabling technologies for the process.
Since 2004, we developed context-aware service platform for ubiquitous robots based on the semantic web and web services. Bossam has been one of the main tools for the development; it has been used for context interpretation, context model inferencing, robot service coordination, mixing results from various semantic web services, etc.
Bossam is under ongoing improvement.