Monday, October 23, 2017

OntoGraph Server

In order to make everyone's life easier, we (Nine Points Solutions) are sponsoring a hosted server running the latest release of OntoGraph and Stardog.

Feel free to give it a try by uploading your ontology, and generating a graph.

Anything that is uploaded is used only in generating the graph and then all details are deleted.


P.S. Maintenance will be done (if needed) on Saturdays, noon-4pm Eastern time.

Graphing with OWL Reasoning

Another version of OntoGraph (V1.0.2) was released today. The main goal was to add OWL reasoning to determine individuals' types. Why might this be important? Well, an individual might be referenced in an ontology, but not defined with a rdfs:type. Or, the individual might be defined with a type, and then also used as the subject or object in a triple. If the predicate of the triple (the relating property) is defined with domains and/or ranges, then a reasoner can infer the type(s) of the individual. This is also useful to find errors in the ontology, its logic and its semantics (more on that later).

Here is a simple example:
@prefix ninepts: <> .
@prefix owl: <> .
@prefix rdf: <> .

<> rdf:type owl:Ontology .

<> rdf:type owl:Class .

<> rdf:type owl:Class .

<> rdf:type owl:Class .

<> rdf:type owl:Class .

<> rdf:type owl:ObjectProperty ;
   rdfs:domain ninepts:class3, ninepts:class4.

<> rdf:type owl:ObjectProperty ;
   rdfs:range ninepts:class1, ninepts:class2 .

<> ninepts:objProp1 ninepts:individual2.

<> ninepts:objProp2 ninepts:individual4.
This example is written in using the Turtle syntax, and basically defines 4 classes (class1 - class4), 2 properties (objProp1 and objProp2), and 4 individuals (individual1 - individual4). The property, objProp1, is defined with 2 classes as its domain (and no range), while objProp2 is defined with 2 classes as its range (and no domain). (No domain or no range for an object property means that there is no intended semantic - that anything, any "owl:Thing", is the domain or range.) The individuals are defined in 2 triples indicating that individual1 is related to individual2 (via objProp1), and individual3 is related to individual4 (via objProp2).

Without OWL reasoning, the individuals have no types. In fact, OntoGraph does not even find any individuals since it "locates" individuals by querying for any entity that has an explicit type of owl:NamedIndividual, or that has a type that begins with a prefix other than OWL or RDF/RDFS. (The query allows us to avoid returning classes (type owl:Class) and properties (type owl:ObjectProperty or owl:DatatypeProperty) when searching for individuals and their types.)

But, if we run OntoGraph with reasoning turned on, then we find that there are indeed 4 individuals, and that individual1 has the types defined for the domain of objProp1, and individual2 has the types defined for the range of objProp2. This is shown in the figure below, which was generated by OntoGraph.

If this seems odd, think about how the reasoner works ... The ontology defined individual1 as the subject of a triple with the predicate, objProp1. We know that any subject (the domain of the property) of objProp1 is defined to be of types, class3 and class4. So, individual1 is "reasoned" to be of those 2 types. Similarly, individual4 is the object of a triple with the predicate, objProp2. And, we know from the ontology that any object (the range of the property) of objProp2 is defined to be of types, class1 and class2. There you have it ...

The reasoner can't determine anything about individual2 or individual3, except that they are themselves individuals. The reasoner figures this out since they are an object and subject (respectively) in triples whose predicates are object properties. By the way, the reasoner also determined that they are of type, owl:Thing, which doesn't tell you much (everything is of type, owl:Thing, unless there is a logical inconsistency in the ontology). OntoGraph does not bother to show that detail since it adds no information to the graph (but does clutter it up).

Now, why did I talk earlier, about illustrating errors in the ontology? If you look at the ontology definition above, you see that the domain of objProp1 is "ninepts:class3, ninepts:class4". Many people writing ontologies mistakenly think that the definition means that the domain is EITHER class3 OR class4. But, that is incorrect. The definition actually means that the domain is BOTH class3 AND class4. Therefore, an individual must be of BOTH types (multiple inheritance), or stated another way, is defined as the intersection of both types. There are some ways to get around this, as discussed in these two posts from StackOverflow (using one property with multiple domains and how to define multiple domains and ranges). I am not going to repeat the answers (which are both very good), but will talk more about reasoning and errors in my next post.

As always, let me know if you have any questions.


Friday, October 6, 2017

OntoGraph V1.0.1 and a Discussion of VOWL

Continuing the evolution of OntoGraph, we published fixes to three minor issues, and updated the README text to address user questions that we received. The README, code, and jar and zip (in the /ontograph-<major.minor.release#> directory) are all updated. The changes are all described in the commit history. In addition, a few new issues were added to our backlog based on feedback and questions. Take a look at the current set of issues and let me know if you have issues to add or want to highlight which ones are important to you. Or, you can just add a comment to the issues directly.

Right now, we are planning on another update (V1.1.0) at the end of October. We will be addressing all the known bugs and adding support for diagramming straight RDF - i.e., to support Linked Data.

Enough of that ... Let's move onto discussing the graph output for a VOWL visualization from OntoGraph versus what is defined in the official specification. First off, per the specification ...
OWL elements such as owl:allValuesFrom, owl:someValuesFrom, owl:hasValue, rdfs:comment, rdfs:seeAlso, rdfs:isDefinedBy, and owl:DataRange (rdfs:datatype in OWL 2 which has a representation in the current specification) are not part of the VOWL visualization but could be displayed in another way (e.g. as text information in a tooltip or sidebar). This is also the case for the OWL elements owl:Ontology, owl:differentFrom, owl:AllDifferent, owl:distinctMembers, owl:Restriction, owl:onProperty, owl:AnnotationProperty, and owl:OntologyProperty that serve as containers of other elements, link individuals, or define ontology metadata.
OntoGraph diverges from the specification for annotation properties. These are displayed in a graph, similar to datatype properties. Ignoring these properties can omit valid information (usable constructs) from a graph. For example, the Friend-Of-A-Friend ontology (FOAF) defines annotation properties for information mapped from the Web of Trust (WOT) and Dublin Core schemas. These properties (especially ones from Dublin Core such as "description") are often used on class, property and element declarataions.

For many of the restriction-related elements listed above (such as owl:Restriction, owl:all/someValuesFrom, ...), OntoGraph outputs labeled edges and text in "UML boxes" that define the details. In my experience, when restrictions are used, understanding them is essential to understanding the ontology.

As regards OWL connectives, VOWL easily shows equivalencies, unions, intersections, complements or disjoint definitions between named classes. But, the defined graphing approach fails when one or more of the related classes are blank (anonymous/un-named) nodes. Consider how equivalent classes are shown - as a circle "with a double border... One of the class labels is the main label, while the rest is listed in square brackets (abbreviated if they do not all fit)." Next, consider how connectives (unions, intersections, etc.) are shown - as two or more classes connected via dashed lines (without arrowheads), to an image of a Venn diagram labeled with a union, intersection or complement logical symbol. The Venn diagram image "represents the anonymous class of the owl:unionOf [owl:intersectionOf, ...] statement".

There are two problems with these conventions when dealing with nested blank nodes. For example, consider a class, foo, that is equivalent to the union of two other blank nodes - the complement of a class, bar, and an intersection of the classes, classA and classB. Since the union node (the equivalency) is anonymous/un-named, there is nothing to display on the second line of foo's node label. As for the second problem, although the union, intersection and complement images can be diagrammed and connected to the relevant classes via dashed lines ... there is no way to understand that the complement and intersection definitions are the entities being unioned, unless arrowheads are used. (Without arrowheads, there could be many interpretations - such as, classB is the intersection of classA and a union declaration.) In standard VOWL, there are simply dashed lines running between all the images. This is shown in the image below.

This same ontology is shown as output by OntoGraph:

OntoGraph addresses VOWL's connectives issues by drawing equivalencies similar to "Subclass of" declarations, and by using arrowheads to indicate exactly what is unioned, intersected or complemented. As another example, here is a snippet of a graph of the W3C Turtle Primer, a complex ontology based on union, intersection, complement and disjoint declarations, as well as restrictions. The majority of this detail would be missing in an "official" VOWL diagram.

Another thing that is missing in the VOWL specification is the display of individuals. Whereas many ontologies do indeed focus on the TBox (the concepts and relationships of a domain), the Linked Data and application worlds have to deal with individuals/instances (the ABox). Being able to diagram your instances is important. But, even if you want to restrict yourself to the TBox world, when you have "one-of" definitions (for enumerations and restrictions), graphing these is important. OntoGraph accepts that Abox individuals are not graphed in VOWL, but does support individual diagrams in the custom, Graffoo and UML visualizations. In addition, OntoGraph displays "one-of" declarations using a UML Note format. An example can be seen at the bottom of the image above.

There are two more major (but related) issues to discuss regarding VOWL ... The first issue involves how node and property names are displayed in a graph. VOWL recommends that an implementation display any rdfs:label that may be defined for a class or property. But, "if elements do not have an rdfs:label, it is recommended to take the last part of the URI as label, i.e. the part that follows the last slash (/) or hash (#) character. Labels may be abbreviated if they do not fit in the available space (e.g. "Winnie-the-Pooh" → "Winnie…"). The full label should be shown on demand in these cases (e.g. in a tooltip or sidebar)." Unfortunately, this last aspect is not possible to support in yEd or any static copy of a graph. And, even the Example in the VOWL Specification does not show the full label when an abbreviated name is displayed!

The second, related issue is that because either a label or local name is displayed, VOWL does not include prefixes/full URIs in its graph. Instead, colors are used to distinguish what is "external" to an ontology (i.e., when a declared element uses a different base URI than the ontology URI/IRI). "External" classes and properties are shown in a darker color (darker blue for OWL classes and properties). In addition, the class nodes also carry the word, "external", in brackets, on the second line. There are several problems with this approach:
  • It will not be possible to distinguish the source of "external" references, and the problem is compounded if there are multiple imported/referenced vocabularies or ontologies. For example, the FOAF diagram includes a node (Spatial Thing) from the WGS84 Geo Positioning RDF vocabulary (WGS84) and another node (Concept) from the SKOS vocabulary (skos). Both of these are displayed in dark blue in the VOWL graph, with the text, "[external]", under their labels. OntoGraph follows this convention.
  • If there are equivalencies to multiple class declarations (from different, external ontologies) but those declarations have the same local name, then the local name will be repeated. For example, FOAF defines equivalent classes for the FOAF Person concept - linking it to the Person class and the Person class from Tim Berner-Lee's Contact ontology. The result is a node whose label is "Person [Person, Pe...]". For domain experts reviewing a graph, this would be confusing at best. As above, OntoGraph follows this convention.
That's it for VOWL! Let me know if this information is helpful. Thanks for reading!


Tuesday, September 19, 2017

What can be learned from the OntoGraph project?

OntoGraph was introduced in my last post, OWL Ontology Graphing Program Available as Open Source. And there are a lot of interesting things in the code! Over the next weeks, I want to take time to relay my learnings, as well as to provide insights into OWL ontologies, SPARQL queries, Bootstrap, Backbone and RESTful interfaces, the Model-View-Controller and other patterns, Spring Boot, Lombok, programming Stardog, testing, Gradle builds, and much more. Some of this will be basic stuff (but hopefully useful to some of my readers) and some will be more advanced. Feel free to pick and choose, or let me know what you want to hear about!

But, first, I want to talk about our development environment ...

The precursor to OntoGraph was originally created in about 2 days to provide some basic diagrams of a customer's ontology. Hand-drawing all the classes, properties, axioms, etc. of the ontologies was too painful and error-prone. Using a tool like OntoViz with Protege was just not flexible enough, and the images were not what the customer wanted to see. The ProtegeVOWL plug-in was also not sufficient since VOWL does not diagram all the necessary constructs (I will talk more about this in a future post). In addition, the customer did not want to be tied to using Protege since they weren't ontologists. They just wanted a diagram and to be able to play around with the layout.

Well, the 2 day "quick and dirty" version worked and the customer had their diagrams. That could have been the end of the story. But, we hired an intern who needed to learn about ontologies, the Stardog triple store, SPARQL queries and lots of other things. So, we decided to use the graphing program as a learning experience. We took the initial work and decided first to just address some bugs. Then, we decided to add the ability to customize the output, which required a front-end. Then, we added support for different kinds of visualization (Graffoo, VOWL, UML). And, the program grew. We changed directions, rewrote whole sections of the program, updated our approach to the front-end at least three times, updated our approach to testing at least twice, and upgraded our infrastructure at least twice (updating the Gradle, Stardog, Javascript libraries, etc.). We put months of work into the program, definitely taking an agile approach and learning to "fail fast".

There are lessons here ... Good software takes time. There is always more to learn. Don't be afraid to take what you learn and rewrite what is problematic (as long as you have time and there are no other programming fires burning). There is always something that you can do better. And, always remember that Stack Overflow is your friend!

Well, ok then ... back to agile. For our agile environment, we used Atlassian's products - JIRA for issue tracking and managing our process (Kanban actually), integrated with a Bitbucket Git repository for version control, and Bamboo as our continuous integration environment. Since we are a small company, this was an easy and cheap solution ($10 for each product). In addition, when we decided to get serious about releasing the code as open-source, we also decided to incoporate SonarQube into our continuous integration environment.

As someone who either spent too much or too little time on code reviews, SonarQube was great! Per Wikipedia, it provides "continuous inspection of code quality to perform automatic reviews with static analysis of code to detect bugs, code smells and security vulnerabilities" ( And, it does this for 20+ programming languages (but we only needed Java, JavaScript and CSS). This took a lot of the pain out of code reviews. I focused on whether the method and property names were understandable, if the code seemed reasonable and was somewhat efficient, and things like that. SonarQube took care of finding problems related to bad practice, lack of efficiency, and errors (such as not initializing a variable). In addition, SonarQube would complain if you nested if/while/for/switch/try statements too deeply, or implemented methods with too many parameters or that were too complex. In reality, SonarQube was tougher on my code than any team review that I had experienced in the past.

Now, you can make things easier on yourself and change the defaults in the SonarQube rules. For example, you can allow a complexity of 30 instead of 15, or allow nesting of if/while/... past 3 levels. But, we didn't do that for OntoGraph. We figured that we would keep the defaults and fix most of the problems (or, we would eventually fix them). There are some "issues" that are just false positives, and others that we have not yet addressed. If you want to find them in the OntoGraph code, just look for "//NOSONAR" and then the explanation that follows. The "//NOSONAR" comment tells SonarQube to ignore the issue for now - either it is a false positive or we acknowledge that there is a problem and are willing to accept the issue for now. I think that this is a valuable approach. Most of the existing issues in OntoGraph are complexity, and we will fix those!

Another important aspect is test coverage. When we decided to release OntoGraph as open source, we set a testing threshold of at least 80% on the back-end processing classes (so this would be, and all the classes in the graphmloutputs folder). All of these classes have coverage between 93.2% and 98.3%, except one. has a test coverage of 77.8%, with 2 (yes, 2) uncovered lines. Those lines throw an IllegalAccessError exception if something tries to instantiate the class (which should not be done since the class contains only 1 static method). Oh well, we decided that this was definitely good enough!

You can see SonarQube in action by downloading OntoGraph and following SonarQube's instructions for Getting Started in Two Minutes. After starting and logging into SonarQube according to the instructions, go to where you downloaded OntoGraph. Type "./gradlew sonar" or "gradlew.bat sonar" for Windows (making sure that you have installed Gradle :-). After that completes, you can see all the rules/issues, statistics and more.

P.S. Sorry for the riff on SonarQube, but I wanted to hit on some cool details. And, I will talk about how Gradle supports SonarQube in a future post. This post just got way too long!


Wednesday, September 13, 2017

OWL Ontology Graphing Program Available as Open Source

It has been forever since I last blogged on this site (more than a year, for which I feel terrible). I have been wrapped up in work for a customer whose details are proprietary, and I was also slowly working to create (what I hope will be valuable) ontology graphing software. I wished that the work on the graphing software would have been available sooner, but better late than never ... The graphing software is called OntoGraph, is finally at a point where it is acceptable to publish, and I can freely discuss it on the blog! So, here we go ...

You can check out the work at Nine Points Solutions' GitHub repository.

OntoGraph is a Spring Boot application for graphing OWL ontologies (yes, the title says this). It lets you go from XML/RDF, Turtle and several other OWL syntaxes to a custom, Graffoo, VOWL or UML-like diagram. For example, you can go from something like this (this excerpt comes from the Friend of a Friend, FOAF.rdf ontology - you can see the complete FOAF ontology at ...

To ...

The above image is a VOWL rendering of FOAF.

OntoGraph is designed with a Bootstrap- and Backbone-based GUI (written in Javascript), interfacing with a RESTful API. The main program is written in Java. It operates by creating various GraphML outputs of a user-provided OWL ontology file. (Or, it also accepts a zip file of a set of ontology files). The program stores the ontologies in the Stardog triple store, then runs a series of queries to return the necessary information on the classes, properties, individuals... to be diagrammed. Layout of the resulting GraphML is handled by another program. (We recommend yEd.)

Four visualizations of ontology data can be generated:
  • Custom format (defined to fit existing business or personal preferences)
  • Graffoo
  • UML-like
  • VOWL
And, information can be segmented to display:
  • Class-related information (subclassing, equivalent and disjoint classes, class restrictions, ...)
  • Individual instances, their types, and their datatype and object property information
  • Property information (datatype and object properties, functional/symmetric/... properties, domain and range definitions, ...)
  • Both class and property information
Complete information about OntoGraph, how to run it, and issues and upcoming features are available at the GitHub repository. Also, there is a pre-publication version of a paper there, there explains OntoGraph and why it was created. (The paper will be available in the next issue of the Journal of Applied Ontology, from IOS Press.)

So, now that OntoGraph is finally published, I can start to blog about its components, design and design decisions, testing, and lots of other details. I just needed something concrete!

I hope that you find the program useful!


Monday, January 25, 2016

Ontologies for Reuse Versus Integration

There is ongoing email discussion in preparation for this year's Ontology Summit on "semantic integration". I thought that I would share one of my recent posts to that discussion here, on my blog. The issue is reuse versus integration ...

For me, designing for general reuse is a valid goal and valuable (if you have the time, which is not always true). (Also it was the subject of the Summit 2 yrs ago and many of my posts from that time - March-May 2014!) But reusing an ontology or design pattern in multiple places is not semantic integration. Reuse and integration are different beasts, although they are complimentary.

I have designed ontologies for both uses (reuse and integration), but my approach to the two is different. Designing for reuse is usually focused on a small domain that is well understood. There are general problem areas (such as creating ontologies/design patterns for events, or to support Allen's time interval algebra) that are generally applicable. In these areas, general design and reuse makes sense.

Over the years, however, I have been much more focused on designing for integration (especially in the commercial space). In my experience, companies are always trying to combine different systems together - whether these systems are legacy vs new, systems that come into the mix due to acquisition, internal (company-centric) vs external (customer-driven), dictated by the problem space (combining systems from different vendors or different parts of an organization to solve a business problem), ...

It is ok to try to be forward-thinking in designing these integration ontologies ... anticipating areas of integration. But, I have been wrong in my guesses (of what was needed in the "future" ontology) probably more than I have been right - unless it was indeed in general problem domains.

So, my integration "rules of thumb" are:
  • Get the SMEs in a particular domain to define the problem space and their solution (don't ever ask the SMEs about integrating their domains)
  • Don't ever give favor to one domain over another in influencing the ontology (you are sure to not be future-proof)
  • Focus on the biggest problem areas first, and find the commonalities/general concepts (superclasses)
  • Place the domain details "under" these superclasses
  • Never try to change the vocabulary of a domain, just map to/from the domains to the "integration" ontology
  • Never map everything in a domain, just what needs to be integrated
  • Look for smaller areas of "general patterns" that can be broadly reused
  • Have new work start from the integrating ontology instead of creating a totally new model
  • Update the integrating ontology based on mapping problems and new work (never claim that the ontology is immutable)
  • Utilize OWL's equivalentClass/disjointFrom/intersectionOf/unionOf/... (for classes), sameAs/differentFrom (for individuals) and class and property restrictions to tie concepts together in the "mapped" space
  • Be focused on concept diagrams and descriptions, documenting mapping details, ... and not that you are using an ontology
  • Clearly document ontology/concept, relationship, ... evolution
Let me know if this resonates with you or if you have different "rules of thumb".


Sunday, January 3, 2016

2016 and continuing posts on ontologies

Well, 2015 seems to have gotten away from me. Over the last year, I have been working to design and implement several ontologies for policy-based management. The work is based on Complexible's Stardog graph database, with services accessed through a RESTful API, and with a front-end, single-page web application created with Bootstrap and Backbone. It has been a blast working with and learning all these technologies, and my new year's resolution is to get back into writing my blog and share some of my learnings.

Another thing that I am doing is related to the International Association for Ontology and Its Applications (IAOA). More specifically, I am a part of the Semantic Web Applied Ontologies Special Interest Group (SWAO SIG). The SIG is continuing the work of the 2014 Ontology Summit and facilitating discussions of ontologies and their development and application. I will be contributing to those discussions in 2016, and started with a short post on the various definitions of the term, ontology. Check out the SWAO link above for the discussion!

That's it for now. Happy 2016!