BFO decision diagram and alignment tool

How to align your domain ontology to a foundational ontology? It’s a well-known question, and one that I’ve looked into before as well. In some of that earlier work, we used DOLCE to align one’s ontology to. We devised the DOLCE decision diagram as part of the FORZA method to assist with the alignment process and implemented that in the MoKI ontology development tool [1]. MoKI is no more, but the theory and the algorithm’s design approach still stand. Instead of re-implementing it as a Protégé plugin and have it go defunct in a few years again (due to incompatible version upgrades, say), it sounded like more fun to design one for BFO and make a stand-alone tool out of it. And that design and the evaluation thereof is precisely what two of my ontology engineering course students—Chiadika Emeruem and Steve Wang—did for their mini-project of the course. That was then finalised and implemented in a tool for general use as part of the DOT4D project extension for my (award-winning) OE textbook afterward.

More precisely, as first part, there’s a diagram specifically for BFO – well, for one of its 2.0-ish versions in existence at least. Deciding on which version to use and what would be good questions was not as trivial as it may sound. While the questions seem to work (as evaluated with several ontologies), it might still be of use to set up an experiment to assess usability from a modeller’s viewpoint.

BFO ‘decision diagram’ to assist trying to align one’s class of a domain or core ontology to BFO (click to enlarge, or navigate to the user guide at https://bfo-classifier.github.io/)

Be this as it may, this decision diagram was incorporated into the tool that wraps around it with a nice interface with user guidance and feedback, and it has the option to load an ontology and save the alignment into the ontology (along with BFO). The decision tree itself is stored as a separate XML file so that it easily can be replaced with any update thereto, be it to reflect changes in question formulation or to adjust it to some later version of BFO. The stand-alone tool is a jar file that can be downloaded from the GitHub repo, and the repo also has the source code that may be used/adapted (i.e., has an open source licence). There’s also a user guide with explanations and screenshots. Here’s another screenshot of the tool in action:

Example of the BFO classifier in use, trying to align CODO’s ‘Disease’ to BFO, the trail of questions answered to get to ‘Disposition’, and the subsumption axiom that can be added to the ontology.

If you have any questions, please feel free to contact either of us.

References

[1] Keet, C.M., Khan, M.T., Ghidini, C. Ontology Authoring with FORZA. 22nd ACM International Conference on Information and Knowledge Management (CIKM’13). ACM proceedings, pp569-578. Oct. 27 – Nov. 1, 2013, San Francisco, USA.

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Reblogging 2013: Release of the (beta version of the) foundational ontology library ROMULUS

From the “10 years of keetblog – reblogging: 2013”: also not easy to choose regarding research. Here, then, the first results of Zubeida Khan’s MSc thesis on the foundational ontology library ROMULUS, being the first post of several papers on the theoretical and methodological aspects of it (KCAP’13 poster, KEOD’13 paper, MEDI’13 paper, book chapter, EKAW’14 paper) and her winning the award for best Masters from the CSIR. The journal paper on ROMULUS has just been published last week in the Journal on Data Semantics, in a special issue edited by Alfredo Cuzzocrea.

Release of the (beta version of the) foundational ontology library ROMULUS; April 4

—————-

With the increase on ontology development and networked ontologies, both good ontology development and ontology matching for ontology linking and integration are becoming a more pressing issue. Many contributions have been proposed in these areas. One of the ideas to tackle both—supposedly in one fell swoop—is the use of a foundational ontology. A foundational ontology aims to (i) serve as a building block in ontology development by providing the developer with guidance how to model the entities in a domain, and  (ii) serve as a common top-level when integrating different domain ontologies, so that one can identify which entities are equivalent according to their classification in the foundational ontology. Over the years, several foundational ontologies have been developed, such as DOLCE, BFO, GFO, SUMO, and YAMATO, which have been used in domain ontology development. The problem that has arisen now, is how to link domain ontologies that are mapped to different foundational ontologies?

To be able to do this in a structured fashion, the foundational ontologies have to be matched somehow, and ideally have to have some software support for this. As early as 2003, this issue as foreseen already and the idea of a “WonderWeb Foundational Ontologies Library” (WFOL) proposed, so that—in the ideal case—different domain ontologies can to commit to different but systematically related (modules of) foundational ontologies [1]. However, the WFOL remained just an idea because it was not clear how to align those foundational ontologies and, at the time of writing, most foundational ontologies were still under active development, OWL was yet to be standardised, and there was scant stable software infrastructure. Within the Semantic Web setting, the solvability of the implementation issues is within reach yet not realised, but their alignment is still to be carried out systematically (beyond the few partial comparisons in the literature).

We’re trying to solve these theoretical and practical shortcomings through the creation of the first such online library of machine-processable, aligned and merged, foundational ontologies: the Repository of Ontologies for MULtiple USes ROMULUS. This version contains alignments, mappings, and merged ontologies for DOLCE, BFO, and GFO and some modularized versions thereof, as a start. It also has a section on logical inconsistencies; i.e., entities that were aligned manually and/or automatically and seemed to refer to the same thing—e.g., a mathematical set, a temporal region—actually turned out not to be (at least from a logical viewpoint) due to other ‘interfering’ axioms in the ontologies. What one should be doing with those, is a separate issue, but at least it is now clear where the matching problems really are down to the nitty-gritty entity-level.

We performed a small experiment on the evaluation of the mappings (thanks to participants from DERI, Net2 funds, and Aidan Hogan), and we would like to have more feedback on the alignments and mappings. It is one thing that we, or some alignment tool, aligned two entities, another that asserting an equivalence ends up logically consistent (hence mapped) or inconsistent, and yet another what you think of the alignments, especially the ontology engineers. You can participate in the evaluation: you will get a small set of a few alignments at a time, and then you decide whether you agree, partially agree, or disagree with it, are unsure about it, or skip it if you have no clue.

Finally, ROMULUS also has a range of other features, such as ontology selection, a high-level comparison, browsing the ontology through WebProtégé, a verbalization of the axioms, and metadata. It is the first online library of machine-processable, modularised, aligned, and merged foundational ontologies around. A poster/demo paper [2] was accepted at the Seventh International Conference on Knowledge Capture (K-CAP’13), and papers describing details are submitted and in the pipeline. In the meantime, if you have comments and/or suggestions, feel free to contact Zubeida or me.

References

[1] Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A. Ontology library. WonderWeb Deliverable D18 (ver. 1.0, 31-12-2003). (2003) http://wonderweb.semanticweb.org.

[2] Khan, Z., Keet, C.M. Toward semantic interoperability with aligned foundational ontologies in ROMULUS. Seventh International Conference on Knowledge Capture (K-CAP’13), ACM proceedings. 23-26 June 2013, Banff, Canada. (accepted as poster &demo with short paper)

Zubeida Khan awarded with best Master’s thesis from CSIR

Zubeida Khan

I’m delighted to highlight here that Zubeida Khan (Dawood) was awarded with a “Best Master’s Thesis” from the CSIR (South Africa’s Council for Scientific and Industrial Research), where she was based when she did her Msc (cum laude) from UKZN, with a scholarship from the UKZN/CSIR-Meraka Centre for Artificial Intelligence Research, and yours truly as her supervisor.

Her thesis was about realising that library of foundational ontologies that had been proposed since late 2003 (in that WonderWeb deliverable D18). The concrete library is the online Repository for Ontologies of MULtiple Uses, ROMULUS, which was described briefly in the MEDI’13 paper [1], and she has a CSIR “technology demonstrator” about it (file) that received an overall panel evaluation of 90%. The theoretical foundations principally had to do with aligning and mapping the foundational ontologies that are included in the library, which are, to date, the OWL versions of DOLCE, GFO, and BFO, which has appeared in a KCAP’13 poster [2] and KEOD’13 full paper [3] and an extended version is due to appear in a best-papers-of-KEOD book [4]. In case you want to have more details: check Zubeida’s thesis, and I have a few blog posts that informally introduce the material: the first announcement of ROMULUS and the KCAP poster.

ROMULUS also contains an online and extended version of the foundational ontology recommender ONSET [5] (which was mostly her Bsc(hons) project, and whose integration into ROMULUS was part of her MSc), various documentation and browse and search features, and the new SUGOI tool for automated foundational ontology interchangeability [6].

Zubeida recently started her PhD at UCT with me as advisor, on ontology modularity, but in case you have feedback on the work, suggestions, or perhaps also new mappings to/from your favourite foundational ontology, feel free to contact her (or me)!

p.s.: Engineering news has an item about the awards, and so will CSIR have one.

p.p.s.: The minimum requirements for the award was:
-Published more than one paper in a peer reviewed publication
-Excellent behavioural attributes as attested by fellow team members such as work ethic and developing a good personal and professional relationships and an active contribution as a team member
-Above average performance score
-The studies must have been completed on a record time
-Excellent academic achievement

References

[1] Khan, Z., Keet, C.M. The foundational ontology library ROMULUS. 3rd International Conference on Model & Data Engineering (MEDI’13). A. Cuzzocrea and S. Maabout (Eds.) September 25-27, 2013, Amantea, Calabria, Italy. Springer LNCS 8216, 200-211.

[2] Khan, Z., Keet, C.M. Toward semantic interoperability with aligned foundational ontologies in ROMULUS. Seventh International Conference on Knowledge Capture (K-CAP’13), ACM proceedings. 23-26 June 2013, Banff, Canada. (poster/demo)

[3] Khan, Z., Keet, C.M. Addressing issues in foundational ontology mediation. 5th International Conference on Knowledge Engineering and Ontology Development (KEOD’13), Vilamoura, Portugal, 19-22 September. Filipe, J. and Dietz, J. (Eds.), SCITEPRESS, pp5-16.

[4] Khan, Z.C., Keet, C.M. Foundational ontology mediation in ROMULUS. invited extended version of the KEOD’13 paper, to be published in Springer CCIS.

[5] Khan, Z., Keet, C.M. ONSET: Automated Foundational Ontology Selection and Explanation. 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW’12), A. ten Teije et al. (Eds.). Oct 8-12, Galway, Ireland. Springer, Lecture Notes in Artificial Intelligence LNAI 7603, 237-251.

[6] Khan, Z.C., Keet, C.M. Feasibility of automated foundational ontology interchangeability. 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14). K. Janowicz et al. (Eds.). 24-28 Nov, 2014, Linkoping, Sweden. Springer LNAI 8876, 225-237.

On ‘swapping’ your foundational ontology to increase interoperability

Over the past few years, I’ve been putting some effort into methods and tools and some data collection and analysis that would aid the use of foundational (top-level) ontologies in ontology engineering, such as DOLCE, GFO, and BFO, and some of its relations (mainly part-whole relations). Tools include the Ontology Selection and Explanation Tool to choose the most suitable foundational ontology [1] and OntoPartS [2] and OntoPartS-2 [3] for software-supported modeling of part-whole relations, and experimentally validating using a foundational ontology does make a difference [4]. The latest addition is SUGOI—Software Used to Gain Ontology Interchangeability, initiated by Zubeida Khan’s idea mentioned in her (cum laude) MSc thesis, which I supervised.

In the meantime, SUGOI has been implemented, and we have used it to answer principally two questions:

1. Is it feasible to automatically generate links between ontology Oa and foundational ontology Oy, given Oa is linked to Ox? Say, I have an ontology linked to BFO, then can I swap BFO for DOLCE?
2. If there are issues with the former, what is causing it? Or: in praxis, which entities of Ox are typically used for mappings with domain ontologies that may not be present, or present in an incompatible way, in Oy? Or: if not, then why not?

We tested this with 16 ontologies that are linked to a foundational ontology, and the results have just been accepted at the 19^th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14) [5].

Now, I already know that some of you will say (and, in fact, have said!), this is not feasible at all. Arguments on philosophical distinctions are there, yes, but not all of that appears in an OWL file and in the modeller’s view (see also an earlier post and references therein). Put differently: things are not that clear-cut and black-and-white as it initially may seem. We did observe a basic, or raw, ‘swapping success rate’ from 2% for the PID ontology from the GFO it was aligned to, to BFO, to up to a whopping 82% for the IDO ontology from BFO to either DOLCE or GFO (averaging at 36% for the real ontologies we tested with). Now, there.

So, what’s really happening? The success rate actually depends on several factors. Some entities in, say, BFO, while named differently, do have an equivalent in DOLCE or GFO, that may or may not be in a similar place in the ontology (if not, then you still end up with an inconsistency, which we removed as mapping), others do not. Those mappings have been investigated in detail [6], and, indeed, there aren’t many, but surely there are some. Several domain ontologies have alignments to only a few categories in a foundational ontology, others have more. If there aren’t many links, or predominantly to those for which there exists an equivalence assertion, then your ‘swapping success rate’ (called raw interchangeability in the paper) is high. Thus, it is not that it is not feasible at all.

The interface of the online desktop version of SUGOI.

Sounds obvious when one puts it like that. But what about my ontology, you may wonder. Use SUGOI to find out. The log file shows what’s been done in the process, and does compute those raw interchangeability metrics for you. SUGOI is ‘trivial’ to extend to include foundational ontologies other than DOLCE, BFO, and GFO—just the mapping files have to be added, but it doesn’t really change the algorithm.

We also looked at the data, especially for the ones with a low success rate, to figure out what causes it. It appeared that for those that use DOLCE, they probably do so because it has some nice knowledge about attributive properties that are not represented (BFO) or represented in an incompatible way (GFO) elsewhere. Likewise, those ontologies that were linked to BFO or GFO and for which there was a lower interchangeability to DOLCE, had quite a few links to aspects on roles, which aren’t in DOLCE proper, so that was causing a relatively lower success rate there (more details in the paper). We leave it up to the developers of the respective foundational ontologies to decide whether they wan to fill that ‘gap’ in their respective ontology.

We also checked SUGOI’s output against ontologies that had been aligned manually to more than one foundational ontology by the developers. We could find only two that were: BioTop and the Stuff Ontology. Mainly, we found the odd error in alignment and a few ones missed by manual alignment, but with n=2, those results are quite at the level of interesting anecdote (observing that the plural of anecdote is not data).

Whether you want to swap, or offer your ontology aligned to more than one foundational ontology to increase its interoperability with other ontologies, is, clearly, your choice to make. If you decide to do so, you could do that manually, but SUGOI automates that process for you as much as possible. Both Zubeida and I plan to be at EKAW’14, hopefully also with a demo, so that you not only can test it with your ontology (which you can do already on the SUGOI page already), but also gain some further detailed insights into the algorithm, the mapping files it used, and the consequences for your ontology.

References

[1] Khan, Z., Keet, C.M. ONSET: Automated Foundational Ontology Selection and Explanation. 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW’12), A. ten Teije et al. (Eds.). Oct 8-12, Galway, Ireland. Springer, LNAI 7603, 237-251.

[2] Keet, C.M., Fernandez-Reyes, F.C., Morales-Gonzalez, A. Representing mereotopological relations in OWL ontologies with OntoPartS. 9th Extended Semantic Web Conference (ESWC’12), Simperl et al. (eds.), 27-31 May 2012, Heraklion, Crete, Greece. Springer, LNCS 7295, 240-254.

[3] Keet, C.M., Khan, M.T., Ghidini, C. Ontology Authoring with FORZA. 22nd ACM International Conference on Information and Knowledge Management (CIKM’13). ACM proceedings, pp569-578. Oct. 27 – Nov. 1, 2013, San Francisco, USA.

[4] Keet, C.M. The use of foundational ontologies in ontology development: an empirical assessment. 8th Extended Semantic Web Conference (ESWC’11), G. Antoniou et al (Eds.), Heraklion, Crete, Greece, 29 May-2 June, 2011. Springer, Lecture Notes in Computer Science LNCS 6643, 321-335.

[5] Khan, Z.C., Keet, C.M. Feasibility of automated foundational ontology interchangeability. 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14). 24-28 Nov, 2014, Linkoping, Sweden. Springer LNAI. (accepted)

[6] Khan, Z., Keet, C.M. Addressing issues in foundational ontology mediation. 5th International Conference on Knowledge Engineering and Ontology Development (KEOD’13), Vilamoura, Portugal, 19-22 September. Filipe, J. and Dietz, J. (Eds.), SCITEPRESS, pp5-16.

KCAP13 poster on aligning and mapping foundational ontologies

I announced in an earlier post the realisation of the Repository of Ontologies for MULtiple USes ROMULUS foundational ontology library as part of Zubeida’s MSc thesis, as well as that a very brief overview describing it was accepted as a poster/demo paper [1] at the 7th International Conference on Knowledge Capture (KCAP’13) that will take place next week in Banff, Canada. The ‘sneak preview’ of the poster in jpeg format is included below. To stay in style, it has roughly the same colour scheme as the ontology library.

The poster’s content is slightly updated compared to the contents of the 2-page poster/demo paper: it has more detail on the results obtained with the automated alignments. On reason for that is the limited space of the KCAP paper, another is that a more comprehensive evaluation has been carried out in the meantime. We report on those results in a paper [2] recently accepted at the 5th International Conference on Knowledge Engineering and Ontology Development (KEOD’13). The results of the tools aren’t great when compared to the ‘gold standard’ of manual alignments and mappings, but there are some interesting differences due to—and thanks to—the differences in the algorithms that the tools use. Mere string matching generates false positives and misses ‘semantic [near-]synonyms’ (e.g., site vs. situoid, but missing perdurant/occurrent), and a high reliance on structural similarity causes a tool to miss alignments (compare, e.g., the first subclasses in GFO vs. those in DOLCE). One feature that surely helps to weed out false positives is the cross-check whether an alignment would be logically consistent or not, as LogMap does. That is also what Zubeida did with the complete set of alignments between DOLCE, BFO, and GFO, aided by HermiT and Protégé’s explanation feature.

The KEOD paper describes those ‘trials and tribulations’; or: there are many equivalence alignments that do not map due to a logical inconsistency. They have been analysed on the root cause (mainly: disjointness axioms between higher-level classes), and, where possible, solutions are proposed, such as subsumption instead of equivalence or proposing to make them sibling classes. Two such examples of alignments that do not map are shown graphically in the poster: a faltering temporal region that apparently means something different in each of the ontologies, and necessary-for does not map to generic-dependent due to conflicting domain/range axioms. The full list of alignments, mappings, and logical inconsistencies is now not only browsable on ROMULUS, as announced in the KCAP demo paper, but also searchable.

Having said that, it is probably worthwhile repeating the same caution made in the paper and previous blog post: what should be done with the inconsistencies is a separate issue, but at least now it is known in detail where the matching problems really are, so that we can go to the next level. And some mappings are possible, so some foundational ontology interchangeability is possible (at least from a practical engineering viewpoint).

References

[1] Khan, Z.C., Keet, C.M. Toward semantic interoperability with aligned foundational ontologies in ROMULUS. Seventh International Conference on Knowledge Capture (K-CAP’13), ACM proceedings. 23-26 June 2013, Banff, Canada. (poster &demo)

[2] Khan, Z.C., Keet, C.M. Addressing issues in foundational ontology mediation. Fifth International Conference on Knowledge Engineering and Ontology Development (KEOD’13). 19-22 September, Vilamoura, Portugal.

Release of the (beta version of the) foundational ontology library ROMULUS

With the increase on ontology development and networked ontologies, both good ontology development and ontology matching for ontology linking and integration are becoming a more pressing issue. Many contributions have been proposed in these areas. One of the ideas to tackle both—supposedly in one fell swoop—is the use of a foundational ontology. A foundational ontology aims to (i) serve as a building block in ontology development by providing the developer with guidance how to model the entities in a domain, and  (ii) serve as a common top-level when integrating different domain ontologies, so that one can identify which entities are equivalent according to their classification in the foundational ontology. Over the years, several foundational ontologies have been developed, such as DOLCE, BFO, GFO, SUMO, and YAMATO, which have been used in domain ontology development. The problem that has arisen now, is how to link domain ontologies that are mapped to different foundational ontologies?

To be able to do this in a structured fashion, the foundational ontologies have to be matched somehow, and ideally have to have some software support for this. As early as 2003, this issue as foreseen already and the idea of a “WonderWeb Foundational Ontologies Library” (WFOL) proposed, so that—in the ideal case—different domain ontologies can to commit to different but systematically related (modules of) foundational ontologies [1]. However, the WFOL remained just an idea because it was not clear how to align those foundational ontologies and, at the time of writing, most foundational ontologies were still under active development, OWL was yet to be standardised, and there was scant stable software infrastructure. Within the Semantic Web setting, the solvability of the implementation issues is within reach yet not realised, but their alignment is still to be carried out systematically (beyond the few partial comparisons in the literature).

We’re trying to solve these theoretical and practical shortcomings through the creation of the first such online library of machine-processable, aligned and merged, foundational ontologies: the Repository of Ontologies for MULtiple USes ROMULUS. This version contains alignments, mappings, and merged ontologies for DOLCE, BFO, and GFO and some modularized versions thereof, as a start. It also has a section on logical inconsistencies; i.e., entities that were aligned manually and/or automatically and seemed to refer to the same thing—e.g., a mathematical set, a temporal region—actually turned out not to be (at least from a logical viewpoint) due to other ‘interfering’ axioms in the ontologies. What one should be doing with those, is a separate issue, but at least it is now clear where the matching problems really are down to the nitty-gritty entity-level.

We performed a small experiment on the evaluation of the mappings (thanks to participants from DERI, Net2 funds, and Aidan Hogan), and we would like to have more feedback on the alignments and mappings. It is one thing that we, or some alignment tool, aligned two entities, another that asserting an equivalence ends up logically consistent (hence mapped) or inconsistent, and yet another what you think of the alignments, especially the ontology engineers. You can participate in the evaluation: you will get a small set of a few alignments at a time, and then you decide whether you agree, partially agree, or disagree with it, are unsure about it, or skip it if you have no clue.

Finally, ROMULUS also has a range of other features, such as ontology selection, a high-level comparison, browsing the ontology through WebProtégé, a verbalization of the axioms, and metadata. It is the first online library of machine-processable, modularised, aligned, and merged foundational ontologies around. A poster/demo paper [2] was accepted at the Seventh International Conference on Knowledge Capture (K-CAP’13), and papers describing details are submitted and in the pipeline. In the meantime, if you have comments and/or suggestions, feel free to contact Zubeida or me.

References

[1] Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A. Ontology library. WonderWeb Deliverable D18 (ver. 1.0, 31-12-2003). (2003) http://wonderweb.semanticweb.org.

[2] Khan, Z., Keet, C.M. Toward semantic interoperability with aligned foundational ontologies in ROMULUS. Seventh International Conference on Knowledge Capture (K-CAP’13), ACM proceedings. 23-26 June 2013, Banff, Canada. (accepted as poster &demo with short paper)

A new version of ONSET and more technical details are now available

After the first release of the foundational ONtology Selection and Explanation Tool ONSET half a year ago, we—Zubeida Khan and I—continued its development by adding SUMO, conducting a user evaluation, and we wrote a paper about it, which was recently accepted [1] at the 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW’12).

There are theoretical and practical reasons why using a foundational ontology improves the quality and interoperability of the domain ontology, be this by means of reusing DOLCE, BFO, GFO, SUMO, YAMATO, or another one, in part or in whole (see, e.g., [2,3] for some motivations). But as a domain ontology developer, and those who are potentially interested in using a foundational ontology in particular, do ask: which one of them would be best to use for the task at hand? That is not an easy question to answer, and hitherto required from a developer to pore over all the documentation, weighing the pros and cons for the scenario, make an informed decision, know exactly why, and be able to communicate that. This bottleneck has been solved with the ONSET tool. Or, at least: we claim it does, and the user evaluation supports this claim.

In short, ONSET, the foundational ONtology Selection and Explanation Tool helps the domain ontology developer in this task. Upon answering one or more questions and, optionally, adding any scaling to indicate some criteria are more important to you than others, it computes the most suitable foundational ontology for that scenario and explains why this is so, including reporting any conflicting answers (if applicable). The questions themselves are divided into five different categories—Ontology, representation language, software engineering properties, applications, and subject domain—and there are “explain” buttons to clarify terms that may not be immediately clear to the domain ontology developer. (There are a few screenshots at the end of this post.)

Behind the scenes is a detailed comparison of the features of DOLCE, BFO, GFO, and SUMO, and an efficient algorithm. The latter and the main interesting aspects of the former are included in the paper; the complete set of criteria is available in a file on the ONSET webpage. You can play with ONSET using your real or a fictitious ontology development scenario after downloading the jar file. If you don’t have a scenario and can’t come up with one: try one of the scenarios we used for the user evaluation (also online). The user evaluation consisted of 5 scenarios/problems that the 18 participants had to solve, half of them used ONSET and half of them did not. On average, the ‘accuracy’ (computed from selecting the appropriate foundatinal ontology and explaining why) was 3 times higher for those who used ONSET compared to those who did not. The ONSET users also did it slightly faster.

Thus, ONSET greatly facilitates in selecting a foundational ontology. However, I concede that from the Ontology (philosophy) viewpoint, the real research component is, perhaps, only beginning. Among others, what is the real effect of the differences between those foundational ontolgoies for ontology development, if any? Is one category of criteria, or individual criterion, always deemed more important than others? Is there one or more ‘typical’ combination of criteria, and if so, is there a single particular foundational ontology suitable, and if not, where/why are the current ones insufficient? In the case of conflicts, which criteria do they typically involve? ONSET clearly can be a useful aid investigating these questions, but answering them is left to future works. Either way, ONSET contributes to taking a scientific approach to comparing and using a foundational ontology in ontology development, and provides the hard arguments why.

We’d be happy to hear your feedback on ONSET, be this on the tool itself or when you have used it for a domain ontology development project. Also, the tool is very easy to extend thanks to the way it is programmed, so if you have your own pet foundational ontology that is not yet included in the tool, you may like to provide us with the values for the criteria so that we can include it.

Here are a few screenshots: of the start page, questions and an explanation, other questions, and the result (of a fictitious example):

Startpage of ONSET, where you select inclusion of additional questions that don’t make any difference right now, and where you can apply scaling to the five categories.

Section of the questions about ontological commitments and a pop-up screen once the related “Explain” button is clicked.

Another tab with questions. In this case, the user selected “yes” to modularity, upon which the tool expanded the question so that a way of modularisation can be selected.

Section of the results tab, after having clicked “calculate results” (in this case, of a fictitious scenario). Conflicting results, if any, will be shown here as well, and upon scrolling down, relevant literature is shown.

References

[1] Khan, Z., Keet, C.M. ONSET: Automated Foundational Ontology Selection and Explanation. 18th International Conference on Knowledge Engineering and Knowledge Management (EKAW’12). Oct 8-12, Galway, Ireland. Springer, LNAI, 15p. (accepted)

[2] Keet, C.M. The use of foundational ontologies in ontology development: an empirical assessment. 8th Extended Semantic Web Conference (ESWC’11), G. Antoniou et al (Eds.), Heraklion, Crete, Greece, 29 May-2 June, 2011. Springer, Lecture Notes in Computer Science LNCS 6643, 321-335.

[3] Borgo, S., Lesmo, L. The attractiveness of foundational ontologies in industry. In: Proc. of FOMI’08, Amsterdam, The Netherlands, IOS Press (2008), 1-9.

Lecture notes for the ontologies and knowledge bases course

The regular reader may recollect earlier posts about the ontology engineering courses I have taught at FUB, UH, UCI, Meraka, and UKZN. Each one had some sort of syllabus or series of blog posts with some introductory notes. I’ve put them together and extended them significantly now for the current installment of the Ontologies and Knowledge Bases Honours module (COMP718) at UKZN, and they are bound and printed into lecture notes for the enrolled students. These lecture notes are now online and I will add accompanying slides on the module’s webpage as we go along in the semester.

Given that the target audience is computer science students in their 4th year (honours), the notes are of an introductory nature. There are essentially three blocks: logic foundations, ontology engineering, and advanced topics. The logic foundations contain a recap of FOL, basics of Description Logics with ALC, all the DL-based OWL species, and some automated reasoning. The ontology engineering block covers top-down and bottom-up ontology development, and methods and methodologies, with top-down ontology development including mainly foundational ontologies and part-whole relations, and bottom-up the various approaches to extract knowledge from ‘legacy’ representations, such as from databases and thesauri. The advanced topics are balanced in two directions: one is toward ontology-based data access applications (i.e., an ontology-drive information system) and the other one has more theory with temporal ontologies.

Each chapter has a section with recommended/required reading and a set of exercises.

Unsurprisingly, the lecture notes have been written under time constraints and therefore the level of relative completeness of sections varies slightly. Suggestions and corrections are welcome!

The DiDOn method to develop bio-ontologies from semi-structured life science diagrams

It is well-known among (bio-)ontology developers that ontology development is a resource-consuming task (see [1] for data backing up this claim). Several approaches and tools do exists that speed up the time-consuming efforts of bottom-up ontology development, most notably natural language processing and database reverse engineering. They are generic and the technologies have been proposed from a computing angle, and are therefore noisy and/or contain many heuristics to make them fit for bio-ontology development. Yet, the most obvious one from a domain expert perspective is unexplored: the abundant diagrams in the sciences that function as existing/’legacy’ knowledge representation of the subject domain. So, how can one use them to develop domain ontologies?

The new DiDOn procedure—from Diagram to Domain Ontology—can speed up and simplify bio-ontology development by exploiting the knowledge represented in such semi-structured bio-diagrams. It does this by means of extracting explicit and implicit knowledge, preserving most of the subject domain semantics, and making formalisation decisions explicit, so that the process is done in a clear, traceable, and reproducible way.

DiDOn is a detailed, micro-level, procedure to formalise those diagrams in a logic of choice; it provides migration paths into OBO, SKOS, OWL and some arbitrary FOL, and guidelines which axioms, and how, have to be added to the bio-ontology. It also uses a foundational ontology so as to obtain more precise and interoperable subject domain semantics than otherwise would have been possible with syntactic transformations alone. (Choosing an appropriate foundational ontology is a separate topic and can be done wit, e.g., ONSET.)

The paper describing the rationale and details, Transforming semi-structured life science diagrams into meaningful domain ontologies with DiDOn [2], has just been accepted at the Journal of Biomedical Informatics. They require a graphical abstract, so here it goes:

DiDOn consists of two principal steps: (1) formalising the ‘icon vocabulary’ of a bio-drawing tool, which then functions as a seed ontology, and (2) populating the seed ontology by processing the actual diagrams. The algorithm in the second step is informed by the formalisation decisions taken in the first step. Such decisions include, among others, the representation language and how to represent the diagram’s n-aries (with n≥2, such as choosing between n-aries as relationship or reified as classes).

In addition to the presentation of DiDOn, the paper contains a detailed application of it with Pathway Studio as case study.

The neatly formatted paper is behind a paywall for those with no or limited access to Elsevier’s journals, but the accepted manuscript is openly accessible from my home page.

References

[1] Simperl, E., Mochol, M., Bürger, T. Achieving maturity: the state of practice in ontology engineering in 2009. International Journal of Computer Science and Applications, 2010, 7(1):45-65.

[2] Keet, C.M. Transforming semi-structured life science diagrams into meaningful domain ontologies with DiDOn. Journal of Biomedical Informatics. In print. DOI: http://dx.doi.org/10.1016/j.jbi.2012.01.004

First release of the foundational ONtology SElection Tool ONSET

It is well-known that there are theoretical and practical reasons why using a foundational ontology—such as DOLCE, BFO, GFO, SUMO—improve the quality and interoperability of the domain ontology, which recently also has been shown experimentally. However, it is also known that when one desires to use one, it is difficult to choose which one should be used, and why. Reading all the documentation, becoming familiar with the philosophical underpinnings, looking up what other ontology developers did in similar situation and so on, is a time-consuming task. This bottleneck has now been solved with ONSET.

ONSET, the foundational ONtology SElection Tool, does the hard work for you (download jar file). You answer one or more questions, and it will compute a suggestion based on the answers and your priorities, and it explains why the particular foundational ontology was selected. As usability is important, several “explain” buttons were added, in particular in the “ontology commitments” category. To increase a user’s confidence, ONSET not only simply selects a foundational ontology for you, but also explains why by relating it back to the answers the user chose, and it displays all (if any) request that was not met by the selected ontology. The rather basic main page of ONSET contains an example and links to the various versions of the three ontologies.

Zubeida Khan, a recently graduated (cum laude) BSc honours student I supervised, did most of the work to realise ONSET. She went painstakingly through some 50 publications to extract the features of the ontologies, by considering the ‘selling points’ from the side of the foundational ontology developers, assessing what motivates domain ontology developers of ongoing and completed ontology development projects to choose one over the other, and examined independent characteristics (such as the language in which it is available, modularity). A list was compiled consisting of foundational ontology parameters, and the values were filled in for each ontology (in the current version, they are BFO, DOLCE, and GFO). These values were subsequently verified by the respective foundational ontology developers. Zubeida then implemented it in ONSET (download jar file), following good software design practices and taking into account extensibility of the tool.

While ONSET makes it a lot easier for a domain ontology developer to select a foundational ontology, from the Ontology (philosophy) side of things, it, perhaps, raises more questions than it answers (which deserve attention, but not in this blog post).

Feedback is welcome!