INQLE version 0.1 is born!

My open source project INQLE (Intelligent Network of Querying and Learning Engines) has reached the ripe old age of 0.1! I had intended this version to be very bare bones, such that it would barely work. But I found that a few features were needed to make the bloody thing usable. Most notably I added 3 big features:

• a wizard for loading spreadsheet data as RDF. This is a pretty powerful feature.
  
• a setup wizard which runs upon starting INQLE for the first time
• an embedded database. This dramatically simplifies the process of installing INQLE. To my delight, I discovered that Jena has recently begin supporting the fantastic H2 database, which performs very well as an embedded database (in fact it outperforms non-embedded databases significantly). I find INQLE runs much faster using an embedded H2 database than an external PostgreSQL database.

Next up: probably will add security, probably using OpenID and Google accounts.

INQLE Scores 8.5 out of 10 on killer app scale

I stumbled across this post on scoring a semantic web application on a 10 point killer scale. [OK, well actually I wrote it.]
So let me score my project INQLE on this scale.

At this writing, IQNLE is very "early doors" (version 0.0.9). Currently, INQLE scores 6 on the 10 point scale. However, our vision/roadmap puts INQLE on a path to score about 8.5:

Immediate Value to User
+1: The tool adds immediate value to the human user. INQLE permits automated machine learning experiments. Users must merely load data and they can then immediately start running experiments.
+1: We are aware of no product that does this.
+1: The tool is free.

Generation of Semantic (RDF) Data
+0.5: INQLE allows users to generate data in spreadsheets (as they are want to do). Users must then use the INQLE interface to import that data.
+1: The new semantic data that INQLE generates are assertions about the correlations that exist between different things in the universe.
+1: Those things which INQLE correlates are real world objects. Some of INQLE's future sampling algorithms will combine local data with remote, pre-existing RDF entities.

Consumption of Semantic (RDF) Data
+1: In future versions of INQLE, users will be able to annotate how valid or trivial or novel or spurious a correlation is.
+0: Such human annotation will require use of INQLE's interface.
+1: Future INQLE algorithms will be able to discover the results of past experiments.
+1: INQLE can then use the power of linked data and semantic reasoning, to perform repeated or related experiments. INQLE servers can therefore accrue an expanding body of knowledge.

So 8.5 out of 10 is pretty decent. But we have to remember that we (and by "we", we mean "I") wrote the damn thing, thru our own myopic specs.

So is INQLE the killer app for the semantic web? Um if your standard for a killer app is Google then probably not. But if you could live with lower expectations and if INQLE could really deliver on its ambition to effect true artificial intelligence and/or revolutionize the way research & discovery is done, then it could deliver some degree of killerness.

Semantic Web Killer App Scale

Many smart people have asked this question:
"What is the killer app for the semantic web?". Well I do not have the answer to that question. But I can tell you some of the attributes that characterize a killer semantic web application.

I came up with a scoring system you can use for evaluating semantic web technologies. The maximum score is 10.

Immediate Value to User
1 point: The tool adds immediate value to the human user.
1 point: That immediate value to the user is novel functionality that is not available for free elsewhere.
1 point: The tool is free.

Generation of Semantic (RDF) Data
1 point: Use existing human workflows to generate new semantic data.
1 point: Automated computer process generates new semantic data, without direct human involvement.
1 point: Generated semantic data links extensively to pre-existing semantic data, hosted remotely.

Consumption of Semantic (RDF) Data
1 point: Humans may annotate the semantic data through a simple procedure, increasing the value thereof.
1 point: Such human annotation occurs automatically, using existing human workflows.
1 point: An automated computer process can consume the generated semantic data in some useful way. That is, humans are not the sole consumers of the generated semantic data.
1 point: Such automated processing increases the value of the body of semantic data, thereby facilitating cumulative accrual of value by the computer.

Not sure how accurate the above model is for capturing the key features of a semantic web application. For example, maybe it puts too much emphasis on machine processing of data. But that's what the semantic web is all about, right? Most agree that it's not just another paradigm for presentation.

So assuming that above scoring system is good enough, let's try to answer: "What is the killer app for the semantic web?"
Well it will be a tool for generating semantic data, of immediate value, using simple, human + automated methods. Such semantic data is processable by automated agents, in such a way that its value grows with time.