Design Thoughts

I was going through some older books that I had stored away, and came across the book, Change By Design, by Tim Brown. The book was published in 2009 and is not particularly earth shattering or deeply enlightening. But, it does contain some nuggets that resonated with me (especially watching less-experienced software engineers try to design a complex system, or (even harder) a good, domain ontology that is somewhat future proof).

There is a valuable quote from the book ...

Design thinking … [matches] human needs with available technical resources within the practical constraints of business.

Seems obvious, right? But there is really much more to this statement than is understood with a cursory reading.

When designing a solution, you have to start with the needs and requirements of your customers (and they are not always realistic). Then, you have your available technologies ... maybe OWL/RDF and triple stores, maybe natural language parsers and libraries, maybe good insights from design patterns that can be reused. Lastly, you have constraints - be they time, money, lack of experienced personnel or any number of other things.

Too often, I see a team focus on what they know, and design some amazing aspect of a project ... but NOT what the customer needs. So, you end up with a partial solution that disappoints your customer, but you are proud of what you accomplished. Then, you decide that the customer or user is unreasonable. I have seen this happen a lot.

So, here are some words of advice ...

• Design the best solution that you can, taking everything into account and being honest where you are stretching beyond your comfort zones.
• Always strive to do incremental development and prototyping.
• Don't be afraid to fail and don't sacrifice the overall design to 1) make everything perfect or 2) focus on what you know. If you do the former, you will never deliver anything. If you do the latter, you will never be able to implement the big picture, because you are ignoring it. Also, you will surprise the customer because you are thinking that you have everything covered, when really, you only have a small aspect of the complete solution covered.

Also, remember that incremental development is just that ... incremental ... nothing in software is ever really perfect or complete. It really comes down to whether it works and whether the customer is happy.

P.S. I have only touched on a few of the points in the Change by Design book. There are lots of good summaries out on the web. One example is the page from the Idea Code Team.

Andrea

More on modular ontologies and tying them together

There is a short email dialog on this topic on the Ontology Summit 2014 mail list. I thought that I would add the reply from Amanda Vizedom as a blog post (to keep everything in one place).

Amanda added:

The style of modularity you mention, with what another summit poster (forgive me for forgetting who at the moment) referred to as 'placeholder' concepts within modules, can be very effective. The most effective technique I've found to date, for some cases.

Two additional points are worth making about how two execute this for maximum effectiveness (they may match what you've done, in fact, but are sometimes missed & so worth calling out for others.

Point 1: lots of annotation on the placeholders. The location & connection of the well-defined concepts to link them to is often being saved for later and possibly for someone else. In order to make sure the right external concept is connected, whatever is known or desired of the underspecifies concept shoud be captured (in the location case, for example, may be that it needs to support enough granularit to be used for location at which a person can be contacted at current time, or must be the kind os location that has a shipping address, or is only intended to be the place of business of the enterprise to which Person is assigned & out of which they operate (e.g., embassy, business office, base, campus). That's often known or easily elicitable without leaving the focus of a specialized module, and can be captured in an annotation for use in finding existing, well defined ontology content and mapping.

Point 2: advantages of modules, as you described are best maintained when the import and mapping are done *not* in the specialized module, but in a "lower" mapping module that inherits the specialized module and the mapping-target ontologies. Spindles of ontologies, which can be more or less intricate, allow for independent development and reuse of specialized modules, with lower mapping and integration modules, with a spindle-bottom that imports all in the spindle and effectivle acts as the integrated query, testing, and application module for all the modules contained in that spindle, providing a simplified and integrated interface to a more complex and highly modular system of ontologies. Meanwhile, specialized modules can be developed with SMEs who don't know, care, or have time to think about the stuff they aren't experts about, like distinguishing kinds location or temporal relations or the weather. Using placeholders and doing your mapping elsewhere may sound like extra work, but considering what it can enable, it can be an incredibly effective approach.

Indeed, the second point is exactly my "integrating" ontology, which imports the target ontologies and does the mapping. As to the first point, that is very much worth highlighting. I err on the side of over-documenting and use various different kinds of notes and annotation. For a good example, take a look at the annotation properties in the FIBO Foundations ontology. It includes comment, description, directSource, keyword, definition, various kinds of notes, and much more.

Another set of annotation properties that I use (which I have not seen documented before, but that I think is valuable for future mapping exercises) are WordNet synset references - as direct references or designating them as hyponyms or hypernyms. (For those not familiar with WordNet, check out this page and a previous blog post.)

Andrea

Creating a modular ontology and then tying the pieces together

In my previous post, I talked about creating small, focused "modules" of cohesive semantic content.  And, since these modules have to be small, they can't (and shouldn't) completely define everything that might be referenced.  Some concepts will be under-specified.  

So, how we tie the modules together in an application?

In a recent project, I used the equivalentClass OWL semantic to do this. For example, in a Person ontology, I defined the Person concept with its relevant properties.  When it came to the Person's Location - that was just an under-specified (i.e., empty) Location class.  I then found a Location ontology, developed by another group, and opted to use that.  Lastly, I defined an "integrating" ontology that imported the Person and Location ontologies, and specified an equivalence between the relevant concepts.  So, PersonNamespace:Location was defined as an equivalentClass to LocationNamespace:Location. Obviously, the application covered up all this for the users, and my triple store (with reasoner) handled the rest.

This approach left me with a lot of flexibility for reuse and ontology evolution, and didn't force imports except in my "integrating" ontology.  And, a different application could bring in its own definition of Location and create its own "integrating" ontology.

But, what happens if you can't find a Location ontology that does everything that you need?  You can still integrate/reuse other work, perhaps defined in your integrating ontology as subclasses of the (under-specified) PersonNamespace:Location concept.

This approach also works well when developing and reusing ontologies across groups.  Different groups may use different names for the same semantic, may need to expand on some concept, or want to incorporate different semantics.  If you have a monolithic ontology, these differences will be impossible to overcome.  But, if you can say things like  "my concept X is equivalent to your concept Y" or "my concept X is a kind of your Y with some additional restrictions" - that is very valuable.  Now you get reuse instead of redefinition.

Andrea

Reuse of ontology and model concepts

Reuse is a big topic in this year's Ontology Summit.  In a Summit session last week, I discussed some experiences related to my recent work on a network management ontology.  The complete presentation is available from the Summit wiki.  And, I would encourage you to look at all the talks given that day since they were all very interesting! (The agenda, slides, chat transcript, etc. are accessible from the conference call page.)

But ... I know that you are busy.  So, here are some take-aways from my talk:

• What were the candidates for reuse?  There were actually several ontologies and models that were looked at (and I will talk about them in later posts), but this talk was about two specific standards:  ISO 15926 for the process industry, and FIBO for the financial industry.
• Why did we reuse since there was not perfect overlap of the chosen domain models/ontologies and network management?  Because there was good thought and insight put into the standards, and there also was tooling developed that we want to reuse.  Besides that, we have limited time and money - so jump starting the development was "a good thing". 
• Did we find valuable concepts to reuse?  Definitely.  Details are in the talk but two examples are:
• Defining individuals as possible versus actual.  For anyone that worries about network and capacity planning, inventory management, or staging of new equipment, the distinction between what you have now, what you will have, and what you might have is really important.
• Ontology annotation properties.  Documentation of definitions, sources of information, keywords, notes, etc. are extremely valuable to understand semantics.  I have rarely seen good documentation in an ontology itself (it might be done in a specification that goes with the ontology).  The properties defined and used in FIBO were impressive.
• Was reuse easy?  Not really.  It was difficult to pull apart sets of distinct concepts in ISO 15926, although we should have (and will do) more with templates in the future.  Also, use of OWL was a mapping from the original definition, which made it far less "natural"/native.  FIBO was much more modular and defined in OWL.  But due to ontology imports, we pretty much ended up loading and working through the complete foundational ontology.  

Given all this, what are some suggestions for getting more reuse?

1. Create and publish more discrete, easily understood "modules" that:
• Define a maximum of 12-15 core entities with their relationships (12-15 items is about the limit of what people can visually retain)
• Document the assumptions made in the development (where perhaps short cuts were made, or could be made)
• Capture the axioms (rules) that apply separately from the core entities (this could allow adjustments to the axioms or assumptions for different domains or problem spaces, without invalidating the core concepts and their semantics)
• Encourage evolution and different renderings of the entities and relationships (for example, with and without short cuts)
2. Focus on "necessary and sufficient" semantics when defining the core entities in a module and leave some things under-specified  
• Don't completely define everything just because it touches your semantics (admittedly, you have to bring all the necessary semantics together to create a complete model or ontology, but more on that in the next post)
• A contrived example is that physical hardware is located somewhere in time and space, but it is unlikely that everyone's requirements for spatial and temporal information will be consistent.  So, relate your Hardware entity to a Location and leave it at that.  Let another module (or set of modules) handle the idiosyncrasies of Location.

In my next post, I promise to talk more about how to combine discrete "modules" with under-specified concepts to create a complete solution.

Andrea

Semantic Technologies and Ontologies Overview Presentation

Last year, I did a short talk on semantic technologies and ontologies, and thought that I would share it. The audience was mostly people who were new to the technologies, and needed to understand their basics and how/where they are used.

[Disclaimer] The presentation is pretty basic ...

But it seemed to work. It overviews key terms (like the "o-word", ontology :-) and standards (based on the ever popular, semantic "layer cake" image). In looking over the deck, I see that I should have talked about RIF (Rule Interchange Format). But, I was using SWRL at the time, and so gravitated to that. (My apologies for not being complete.)

Since the talk was meant to show that semantic technologies are not just an academic exercise, I spent most of the time highlighting how and where the technologies are used. IMHO, I think that the major uses are:

• Semantic search and query expansion
• Mapping and merging of data
• Knowledge management

The last bullet might be a bit ambiguous. For me, it means organizing knowledge (my blog's namesake) and inferring new knowledge (via reasoning and logic).

There are also quite a few examples of real companies using ontologies and semantic technologies. It is kind of amazing when you look at what is being done.

So, take a look and let me know what you think.

And, as a teaser, I want to highlight that I will be presenting at the next Ontology Summit 2014 session on Thursday, January 23rd, on "Reuse of Content from ISO 15926 and FIBO". If you want to listen in, the details for the conference call are here.  Hopefully, you can join in.

Andrea

Ontology Summit 2014 Kicks Off Tomorrow, Jan 16

The topic for this year's Summit is "Big Data and Semantic Web Meet Applied Ontology". The Summit kicks off with a conference call on Thursday, January 16th, at 9:30am PST/12:30pm EST/5:30pm GMT (call details). Here is a short excerpt from the Summit's "Goals and Objectives":

Since the beginnings of the Semantic Web, ontologies have played key roles in the design and deployment of new semantic technologies. Yet over the years, the level of collaboration between the Semantic Web and Applied Ontology communities has been much less than expected. Within Big Data applications, ontologies appear to have had little impact. This year's Ontology Summit is an opportunity for building bridges between the Semantic Web, Linked Data, Big Data, and Applied Ontology communities.

For those of you not familiar with the Summit, it was started in 2006, and there is a different theme each year. It is sponsored by a set of organizations including NIST, Ontolog, NCOR, NCBO, IAOA & NITRD. The way that the Summit works is by a series of conference calls (every Thursday) and lots of email discussion. It is culminated by a face-to-face meeting in late April.

I highly recommend participating, or even just lurking. It is not necessary (or even possible :-) to attend the call every week and to read every email on the ontolog-forum. (Also, it is not mandatory to come to the face-to-face.) If you have to miss something, slides and transcripts of the conference chats, as well as an email archive, are available online. In addition, at the end of the Summit, a "communique" is prepared that summarizes the discussions and work.

I have lurked on the edges of the Summit since the late 2000s, but finally have the time to actively participate. This year, I am co-championing Track A on "Common, Reusable Semantic Content". This topic is near and dear to my heart since I am a strong proponent of reuse. IMHO, it is always valuable (when it is possible) to build on someone else's good work, and benefit from their learnings, rather than starting from scratch. So, when modeling or designing, I look to find something similar and then extrapolate from, or build on it.

Needless to say, I usually don't take other ontologies "en masse", but pick and choose the semantics, patterns or ideas that make sense. How to do this is one aspect of Track A, and there is more. Here are some excerpts from our "Mission" statement:

Semantic technologies such as ontologies and reasoning play a major role in the Semantic Web and are increasingly being applied to help process and understand information expressed in digital formats. Indeed, the derivation of assured knowledge from the connection of diverse (and linked) data is one of the main themes of Big Data ... One challenge in these efforts is to build and leverage common semantic content thus reducing the burden of new ontology creation while avoiding silos of different ontologies. Examples of such content are whole or partial ontologies, ontology modules, ontological patterns and archetypes, and common theories related to ontologies and their fit to the real world ... Achieving commonality and reuse in a timely manner and with manageable resources remain key ingredients for practical development of quality and interoperable ontologies ... This track will discuss the reuse problem and explore possible solutions. Among these are practical issues like the use of ontology repositories and tools, and the possibility of using basic and common semantic content in smaller, more accessible pieces. The goal is to identify exemplary content and also define the related information to enable use/reuse in semantic applications and services. A secondary goal is to highlight where more work is needed and enable the applied ontology community to further develop semantic content and its related information.

I hope that you can join me!

Andrea

Restarting my blog and research

Thanks to everyone who has stuck with me over the past few years as I got consumed in projects and neglected my blog. That phase of my life was officially over in mid-December (2013). I just joined a small consulting firm, Nine Points Solutions. I now have the opportunity to both consult and to continue my research into extracting and managing policies in support of security, configuration and operations. I am very excited to have the opportunity to do this, and will share what I am doing and what I learn (when I can).

As part of restarting the blog, I did go through and clean up my overviews and list of blogs that I read. Please take a look and tell me about any blogs that I missed and that you find valuable. I didn't change the overall design of the site, because it works for me. But, let me know if that is not true for you, my readers.

Lastly, I want to bring everyone up-to-speed (at a very high level) on my work over the last two+ years ... I had the opportunity to architect and create a policy-based management system based on OWL ontologies and using semantic technologies. The system was written in Java and used the Stardog triple store from Clark and Parsia. Needless to say, I made mistakes, got a lot of help from the Stardog developers, and learned a lot. I will be sharing some of that learning in future posts.

Thanks again! I am looking forward to writing more in the coming weeks!

Andrea