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Business Maps: Topic Maps Go B2B

August 21, 2002

Interoperability between ontologies is a big, if not the single biggest issue in B2B data exchange. For the foreseeable future there will not be a single, widely accepted B2B vocabulary. Therefore we will need mappings between different ontologies. Since these mappings are inherently situational, and the context is very complex, we cannot expect computers to create more than a small part of those mappings. We need tools to leverage the intelligence of humans business experts. We need portable, reusable, and standardized mappings. Topic Maps are an excellent vehicle to provide those "Business Maps". (This article presumes a basic understanding of Topic Maps, readers may wish to read A Gentle Introduction to Topic Maps in conjunction with this article.)

We have lots of data and descriptions of data. Take for instance the abundance of vocabularies for B2B exchange: xCBL, FinXML, FpML, etc. Those vocabularies can be seen as ontologies. Older EDI technologies such as X.12 and EDIFACT are also ontologies. There are as of yet no general standards for B2B vocabularies in XML. The ebXML initiative did not have actual business documents as one of its deliverables. Right now work is being done on the Universal Business Language (UBL) to fill this gap. Beside those "industry-strength" solutions, there are lots of tailor-made data exchanges between companies, often using nothing more than simple ASCII comma-separated files. Together with their documentation, those ASCII-files also constitute ontologies. And even within larger companies many different ontologies exist within the different legacy databases of different departments. Those different data sources present huge interoperability problems.

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One of those interoperability problems is finding out which data items from different sources are the same. To do that, we need to compare the meanings of those data items. This means we have to look up data definitions for different data sources and compare those data definitions. Comparing human-made definitions is a tough job. Different organizations may come up with very different definitions for things that really are the same, and with very similar definitions for things that are very different in reality.

First of all, hard as we try, mistakes and obscurities occur in our data definitions. Second, in making data definitions we may find that a lot of data aren't that well defined to start with. In other words, when we make data definitions for a data source, it's sometimes the first attempt to define the data at all, and when there already is a definition, it is often not precise enough. Third, when we make a definition like "an employee is a person working at a company", we introduce many new words ("person", "work", "company") from natural language. When meanings in natural language aren't precise, those definitions aren't going to be precise either. We should stop thinking we can fix meanings once and for all in any but the most limited contexts.

Some Solutions and Why They Don't Work

There are some solutions to these problems. The first, which I shall call "the naive approach" is to make a new vocabulary which covers everything, and then let everybody use that vocabulary. It's an easy solution to think of, but it does not work in practice. Multiple vocabularies are a fact of life. Think only of the huge number of existing applications using legacy formats, which won't simply go away. And even for new applications, there are so many different business needs in different contexts that there's a huge drive toward specific, directly applicable vocabularies and away from generic standards which take a long time to evolve. So the main problem should be how to make the different vocabularies interoperate, not how to replace them by a single unifying standard. Developing unifying vocabularies is a good thing; the more success they have, the better. But one should think of them as central pieces in the plethora of vocabularies, not the only ones. The success of new, unifying vocabularies will depend not so much on their inherent capabilities as on their ability to interoperate with existing vocabularies. Interoperability is the shortest route to acceptance.

Another approach to interoperability is the use of Published Subject Indicators (PSIs) as used in Topic Maps. The basic idea is to make public libraries of unique IDs for things. In our vocabularies we incorporate PSIs, and then we can compare the terms in our vocabularies. In an informal example:

Topic: "Last Name"; PSI: familyname
Topic: "Achternaam"; PSI: familyname

The PSIs in the English and Dutch topics allow us to conclude that both topics are the same. Note that this really just shifts the problem from vocabularies to public libraries. In general we can say this approach is successful if the problem space consists of clearly delimited entities and there is a widely accepted canonical public library. Examples of areas were this approach will work are for instance ISO currency and country codes. Currently OASIS is working on standards for PSIs in its OASIS Topic Maps Published Subjects TC. Once this work is done, the situation might improve as more PSIs are being published.

In actual mappings between ontologies, we often do not really establish semantic equivalence in a true sense as needed in PSIs. Consider an example. When GigaSellers decides to let Print & Send handle its invoices, invoice information flows from GigaSellers to Print & Send. When we have found we can use GigaSellers "CustomerAddress" as the "invoice_address" in Print & Send's invoicing application, we stop. We do not need to find out whether they are truly equivalent in all circumstances. There is no direct business need to find this out, and therefore the boss doesn't pay for it.

Solutions like PSIs do not work here because PSIs require true semantic equivalence. The interesting observation is that most real world mappings are unidirectional: we translate from a source ontology to a destination ontology for a specific business process. For instance, an order goes from buyer to supplier. It does not go back (though a different document such as an invoice or order confirmation might go back). So for an order only a translation from the buyer's ontology to the supplier's ontology is needed. This unidirectional nature of business exchange means that usually we do not establish equivalence relationships, but subset relationships between ontologies. In the above example, "CustomerAddress" is a subset of "invoice_address". All instances of "CustomerAddress" constitute a valid instance of "invoice_address". We do not know whether the reverse is true. It could very well be the case that GigaSellers requires its "CustomerAddress" to be a physical address where goods can actually be delivered, and Print & Send allows postal boxes as "invoice_address". Further, there is often no true equivalence because the data items are in different formats. GigaSellers may store all its dates in CCYY-MM-DD format, and Print & Send as MM/DD/CCYY. In this case, too, there is no true equivalence, since a transformation is needed.

It might be tempting to conclude that we simply have to make a mapping between every two ontologies we use. That, however, is going to far. Even when we do not always establish true semantic equivalence relationships, the mappings we make can be reusable. What we need to do is capture knowledge about the mapping process itself. We need to store the fact that we can use "CustomerAddress" as "invoice_address" in this particular context. Then, when someone else needs to find out whether "CustomerAddress" can be used as "mailing_address" in a different context, they can use this information. When we store this kind of information, we could facilitate the process of mapping ontologies through the use of semi-automated tools which show existing mappings for items in our ontology that we need to map onto another ontology. The human expert making the mapping can still make all the relevant choices and provide new mappings where existing ones can't be reused. Such semi-automated tools could then generate a new mapping, which also can be stored to provide information for the next one. It would also become much easier to exchange information about mappings without having to provide full one-on-one equivalence relationships.

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