David S. Read

I had the pleasure of attending the Semantic Technology and Business Conference in Washington, DC last week.  I have a strong interest in semantic technology and its capabilities to enhance the way in which we leverage information systems.  There was a good selection of topics discussed by people with a variety of  backgrounds working in different verticals.

To begin the conference I attended the half day “Ontology 101” presented by Elisa Kendall and Deborah McGuinness.  They indicated that this presentation has been given at each semantic technology conference and the interest is still strong.  The implication being that new people continue to want to understand this art.

Their material was very useful and if you are someone looking to get a grounding in ontologies (what are they?  how do you go about creating them?) I recommend attending this session the next time it is offered.  Both leaders clearly have deep experience and expertise in this field.  Also, the discussion was not tied to a technology (e.g. RDF) so it was applicable regardless of underlying implementation details.

I wrapped up the first day with Richard Ordowich who discussed the process of reverse engineering semantics (meaning) from legacy data.  The goal of such projects being to achieve a data harmonization of information across the enterprise.

A point he stressed was that a business really needs to be ready to start such a journey.  This type of work is very hard and very time consuming.  It requires an enterprise wide discipline.  He suggests that before working with a company on such an initiative one should ask for examples of prior enterprise program success (e.g. something like BPM, SDLC).

Fundamentally, a project that seeks to harmonize the meaning of data across an enterprise requires organization readiness to go beyond project execution.  The enterprise must put effective governance in place to operate and maintain the resulting ontologies, taxonomies and metadata.

The full conference kicked off the following day.  One aspect that jumped out for me was that a lot of the presentations dealt with government-related projects.  This could have been a side-effect of the conference being held in Washington, DC but I think it is more indicative that spending in this technology is more heavily weighted to public rather than private industry.

Being government-centric I found any claims of “value” suspect.  A project can be valuable, or show value, without being cost effective.  Commercial businesses have gone bankrupt even though they delivered value to their customers.  More exposure of positive-ROI commercial projects will be important to help accelerate the adoption of these technologies.

Other than the financial aspect, the presentations were incredibly valuable in terms of presenting lessons learned, best practices and in-depth tool discussions.  I’ll highlight a few of the sessions and key thoughts that I believe will assist as we continue to apply semantic technology to business system challenges.

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I was recently asked about the SPARQL support in Sparql Droid and whether it could serve as a way for other Android applications to execute SPARQL queries against remote data sources.  It could be used in this way but there is a simpler alternative I’d like to discuss here.

On the Android platform it is actually quite easy to execute SPARQL against remote SPARQL endpoints, RDF data and local models.  The heavy lifting is handled by Androjena’s ARQoid, an Android-centric port of HP’s Jena ARQ engine.

Both engines (the original and the port) do a great job of simplifying the execution of SPARQL queries and consumption of the resulting data.  In this post I’ll go through a simple example of using ARQoid.  Note that all the code being shown here is available for download.  This post is based specifically on the queryRemoteSparqlEndpoint() method in the com.monead.androjena.demo.arqoid.SparqlExamples class.

Setup

To begin, some environment setup needs to be done in order to have a properly configured Android project ready to use ARQoid.

First, obtain the ARQoid JAR and its dependencies.  This is easily accomplished using the download page on the ARQoid Wiki and obtaining the latest ARQoid ZIP file.  Unzip the downloaded archive.   Since I’m discussing an Android application I’d expect that you would have created an Android project and that it contains a libs directory where the JAR files should be placed.

Second, add the JAR files to the classpath for your Android project.  I use the ADT plugin for Eclipse to do Android development.  So to add the JARs to my project I choose the Project menu item, select Properties, choose Build Path, select the Libraries tab, click the Add JARs… button, navigate to the libs directory, select the JAR files and click OK on the open dialogs.

Third, setup a minimal Android project.  The default layout, with a small change to its definition will work fine.

Overview

Now we are ready to write the code that uses ARQoid to access some data.  For this first blog entry I’ll focus on a trivial query against a SPARQL endpoint.  There would be some small differences if we wanted to query a local model or a remote data set.  Those will be covered in follow-on entries.

Here is a list of the ARQoid classes we will be using for this initial example:

• com.hp.hpl.jena.query.Query – represents the query being executed
• com.hp.hpl.jena.query.Syntax – represents the query syntaxes supported by ARQoid
• com.hp.hpl.jena.query.QueryFactory – creates a Query instance based on supplied parameters such as the query string and syntax definition
• com.hp.hpl.jena.query.QueryExecution – provides the service to  execute the query
• com.hp.hpl.jena.query.QueryExecutionFactory – creates a QueryExecution instance based on supplied parameters such as a Query instance and SPARQL endpoint URI
• com.hp.hpl.jena.query.ResultSet – represents the returned data and metadata associated with the executed query
• com.hp.hpl.jena.query.QuerySolution – represents one row of data within the ResultSet.

We’ll use these classes to execute a simple SPARQL query that retrieves some data associated with space exploration.  Talis provides an endpoint that we can use to access some interesting space exploration data.  The endpoint is located at http://api.talis.com/stores/space/services/sparql.
The query we will execute is:

SELECT ?dataType ?data
WHERE {
  <http://nasa.dataincubator.org/launch/1961-012> ?dataType ?data.
}

This query will give us a little information about Vostok 1 launched by the USSR in 1961.

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As I release my 3rd Alpha-version of Sparql Droid I thought I’d document a few lessons learned and open items as I work with the Android environment.  Some of my constraints are based on targeting smart phones rather than tablets, but the lessons learned around development environments, screen layouts, and memory management are valuable.

I’ll start on the development side.  I use Eclipse and the android development plugin is very helpful. It greatly streamlines the development process.  Principally, it automates the generation of the resources from the source files.  These resources, such as screen layouts and menus, require a conversion step after being edited.  The automation, though, comes at a price.

Taking a step back, Android doesn’t use an Oracle-compliant JVM.  Instead it uses the Dalvik VM.  This difference creates two major ramifications: 1) not all the standard packages are available; and 2) any compiled Java code has to go through a step to “align” it for Dalvik. This alignment process is required for class files you create and for any third-party classes (such as those found in external JAR files).  Going back to item 1, if an external JAR file you use needs a package that isn’t part of Dalvik, you’ll need to recreate it.

The alignment process works pretty fast for small projects.  My first application was a game that used no external libraries.  The time required to compile and align was indistinguishable from typical compile time.  However, with Sparql Droid, which uses several large third-party libraries, the alignment time is significant – on the order of a full minute.

That delay doesn’t sound so bad, unless you consider the Build Automatically feature in Eclipse.  This is a feature that you want to turn off when doing Android development that includes third-party libraries of any significance. Turning off that feature simply adds an extra step to the editing process, a manual build, and slightly reduces the convenience of the environment.

With my first Android project, I was able to edit a resource file and immediately jump back to my Java code and have the resource be recognized.   Now I have to manually do a build (waiting a minute or so) after editing a resource file before it is recognized on the code side.  Hopefully the plug-in will be improved to cache the aligned libraries, saving that time when the libraries aren’t being changed.

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The semantic technology concepts that comprise what is generally called the semantic web involve paradigm shifts in the ways that we represent data, organize information and compute results. Such shifts create opportunities and present challenges.  The opportunities include easier correlation of decentralized information, flexible data relationships and reduced data storage entropy.  The challenges include new data management technology, new syntaxes, and a new separation of data and its relationships.

I am a strong advocate of leveraging semantic technology.  I believe that this new paradigms provide a more flexible basis for our journey to create meaningful, efficient and effective business automation solutions. However, one challenge that differentiates leveraging semantic technology from more common technology (such as relational databases) is the lack of mature tools supporting a business system infrastructure.

It will take a while for solid solutions to appear.  Support for mainstream capabilities such as reporting, BI, workflow, application design and development that all leverage semantic technology are missing or weak at best.  Again, this is an opportunity and a challenge.  For those who enjoy creating computer software it presents a new world of possibilities.  For those looking to leverage mature solutions in order to advance their business vision it will take investment and patience.

In parallel with the semantic paradigm we have an ever increasing focus on mobile-based solutions. Smart phones and tablet devices, focused on network connectivity as the enabler of value, rather than on-board storage and compute power, are becoming the standard tool for human-system interaction.  As we design new solutions we must keep the mobile-accessible mantra in mind.

As part of my exploration of these two technologies, I’ve started working on a semantic technology mobile application called Sparql Droid. Built for the Android platform, my goal is a tool for exploring and mashing semantic data sources.  As a small first-step I’ve leveraged the Androjena port of the Jena framework and created an application with some basic capabilities.

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Being a consumer of semantic data I thought creating a SPARQL endpoint would be an interesting exercise.  It would require having some data to publish as well as working with a SPARQL library.  For data, I chose a set of mileage information that I have been collecting on my cars for the last 5 years.  For technology, I decided to use the Joseki SPARQL Server, since I was already using Jena.

For those who want to skip the “how” and see the result, the SPARQL endpoint along with sample queries and a link to the ontology and data is at: http://monead.com/semantic/query.html

The first step in this project was to convert my mileage spreadsheets into triples.  I looked briefly for an existing ontology in the automobile domain but didn’t find anything I could use.  I created an ontology that would reflect my approach to recording automobile mileage data.  My data  records the miles traveled between fill-ups as well as the number of gallons used.  I also record the car’s claimed MPG as well as calculating the actual MPG.

The ontology reflects this perspective of calculating the MPG at each fill-up.  This means that the purchase of gas is abstracted to a class with information such as miles traveled, gallons used and date of purchase as attributes.  I abstracted the gas station and location as classes, assuming that over time I might be able to flesh these out (in the spreadsheet I record the name of the station and the town/state).

A trivial Java program converts my spreadsheet (CSV) data into triples matching the ontology.  I then run the ontology and data through Pellet to derive any additional triples from the ontology.  The entire ontology and current data are available at http://monead.com/semantic/data/HybridMileageOntologyAll.Inferenced.xml.

It turns out that the ontology creation and data conversion were the easy parts of this project.  Getting Joseki to work as desired took some time, mostly because I couldn’t find much documentation for deploying it as a servlet rather than using its standalone server feature.  I eventually downloaded the Joseki source in order to understand what was going wrong.  The principle issue is that Joseki doesn’t seem to understand the WAR environment and relative paths (e.g. relative to its own WAR).

I had two major PATH issues: 1) getting Joseki to find its configuration (joseki-config.ttl); and 2) getting Joseki to find the triple store (in this case a flat file).

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I attended my first semantic web conference this week, the Semantic Web Summit (East) held in Boston.  The focus of the event was how businesses can leverage semantic technologies.  I was interested in what people were actually doing with the technology.  The one and a half days of presentations were informative and diverse.

Our host was Mills Davis, a name that I have encountered frequently during my exploration of the semantic web.  He did a great job of keeping the sessions running on time as well as engaging the audience.  The presentations were generally crisp and clear.  In some cases the speaker presented a product that utilizes semantic concepts, describing its role in the value chain.  In other cases we heard about challenges solved with semantic technologies.

My major takeaways were: 1) semantic technologies work and are being applied to a broad spectrum of problems and 2) the potential business applications of these technologies are vast and ripe for creative minds to explore.  This all bodes well for people delving into semantic technologies since there is an infrastructure of tools and techniques available upon which to build while permitting broad opportunities to benefit from leveraging them.

As a CTO with 20+ years focused on business environments, including application development, enterprise application integration, data warehousing, and business intelligence I identified most closely with the sessions geared around intra-business and B2B uses of semantic technology.  There were other sessions looking a B2C which were well done but not applicable to the world in which I find myself currently working.

Talks by Dennis Wisnosky and Mike Dunn were particularly focused on the business value that can be achieved through the use of semantic technologies.  Further, they helped to define basic best practices that they apply to such projects.  Dennis in particular gave specific information around his processes and architecture while talking about the enormous value that his team achieved.

Heartening to me was the fact that these best practices, processes and architectures are not significantly different than those used with other enterprise system endeavors.  So we don’t need to retool all our understanding of good project management practices and infrastructure design, we just need to internalize where semantic technology best fits into the technology stack.

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I’ve been at Oracle’s combined JavaOne and OpenWorld events for two days.  I am here as both an attendee, learning from a variety of experts, and as a speaker.  Of course this is the first JavaOne since Oracle acquired Sun.  I have been to several JavaOne conferences over the years so I was curious how the event might be different.

One of the first changes that I’ve noticed is that due to the co-location of these two large conferences the venue is very different than when Sun ran JavaOne as a standalone event.  The time between sessions is a full half hour, probably due to the fact that you may find yourself going between venues that are several blocks apart.  I used to think that having getting from Moscone North the Moscone South took a while.   Now I’m walking from the Moscone center to a variety of hotels and back again.  Perhaps this is actually a health regime for programmers!

The new session pre-registration system is interesting. I don’t know if this system has been routine with Oracle’s other conferences but it is new to JavaOne.  Attendees go on-line and pre-register for the sessions they want to attend.  When you show up at the session your badge is scanned.  If you had registered you are allowed in.  If you didn’t preregister and the session is full you have to wait outside the room to see if anyone who registered fails to show up.

I think I like the system, with the assumption that they would stop people from entering when the room was full.  At previous conferences it seemed like popular sessions would just be standing room only, but that was probably a violation of fire codes.  The big advantage of this approach is that it reduces the likelihood of your investing the time to walk to the venue only to find out you can’t get in.  As long as you arranged your schedule on-line and you show up on-time, you’re guaranteed a seat.

Enough about new processes.  After all, I came here to co-present a session and to learn from a variety of others.

Paul Evans and I spoke on the topic of web services and their use with a rules engine. Specifically we were using JAX-WS and Drools.  We also threw in jUDDI to show the value of service location decoupling.  The session was well attended (essentially the room was full) and seemed to keep the attendees’ attention.  We had some good follow-up conversations regarding aspects of the presentation that caught people’s interest, which is always rewarding. The source code for the demonstration program is located at http://bit.ly/blueslate-javaone2010.

Since I am a speaker I have access to both JavaOne and OpenWorld sessions.  I took advantage of that by attending several OpenWorld sessions in addition to a bunch of JavaOne talks.

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I received a helpful push from Paul Evans this evening.  He reminded me that the Semantic Workbench SourceForge project (semanticwb.sourceforge.net) is just sitting idle, waiting to be kicked-off.  We talked about the vision around the project, which needs to be clearly and concisely articulated as a mission.  At that point we’ll have a direction to take.

This conversation coincided with my attendance at two semantic-web presentations at Oracle OpenWorld, which I am able to attend since it is co-located with JavaOne.  I’ll write more about my experiences at this year’s JavaOne conference soon.

These semantic -web presentations validated the value of semantic technologies and the need to make them more visible to the IT community.  For my part, this means I need to do more writing and presenting about semantic technologies while creating a renewed vigor around the Semantic Workbench project.

As Paul and I spoke and I tried to define my vision around the project, I realized that I was being too wordy for a mission statement.  The fundamentals of my depiction were also different from the current project overview on SourceForge.  The overview does not describe the truly useful application that I would like to see come out of the project.

Recognizing this disconnect reinforced the need to come up with a more useful and actionable mission.  In the hopes that the project can be of value, I present this mission statement:

The Semantic Workbench strives to provide a complete Java-based GUI and tool set for exploring, testing, and validating common semantic web-based operations.

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As a way to work with semantic web concepts, including asserting triples, seeing the resulting inferences and also leveraging SPARQL, I have needed a GUI.  In this post I’ll describe a very basic tool that I have created and released that allows a user to interact with a semantic model.

My objectives for this first GUI were basic:

1. Support input of a set of triples in any format that Jena supports (e.g. REF/XML, N3, N-Triples and Turtle)
2. See the inferences that result for a set of assertions
3. Create a tree view of the ontology
4. Make it easy to use SPARQL queries with the model
5. Allow the resulting model to be written to a file, again using any format supported by Jena

Here are some screen shots of the application.  Explanations of the tabs are then provided.

The program provides each feature in a very basic way.  On the Assertions tab a text area is used for entering assertions.  The user may also load a text file containing assertions using the File|Open menu item.  Once the assertions are entered, a button is enabled that allows the reasoner to process the assertions.  The reasoner level is controlled by the user from a drop down.

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In an earlier blog entry I discussed the potential reduction in refactoring effort if our data is represented as RDF triples rather than relational structures.  As a way to give myself easy access to RDF data and to work more with semantic web tool features I have created a program to export relational data to RDF.

The program is really a proof-of-concept.  It takes a SQL query and converts the resulting rows into assertions of triples.  The approach is simple: given a SQL statement and a chosen primary key column (PK) to represent the instance for the exported data, assert triples with the primary key column value as the subject, the column names as the predicates and the non-PK column values as the objects.

Here is a brief sample taken from the documentation accompanying the code.

• Given a table named people with the following columns and rows:

       id    name    age
       --    ----    ---
       1     Fred    20
       2     Martha  25

• And a query of:  select id, name, age from people
• And the primary key column set to: id
• Then the asserted triples (shown using Turtle and skipping prefixes) will be:

       dsr:PK_1
          a       owl:Thing , dsr:RdbData ;
          rdfs:label "1" ;
          dsr:name "Fred" ;
          dsr:age "20" .

       dsr:PK_2
          a       owl:Thing , dsr:RdbData ;
          rdfs:label "2" ;
          dsr:name "Martha" ;
          dsr:age "25" .

You can see that the approach represents a quick way to convert the data.

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