As it was said to me recently, very often something goes from being mysterious and magical to obvious (I'm looking at you not-so-magical-anymore Grails wiring). So it goes with test driving ontologies (or at least it did for me). One of the standard methodologies for ontology development is creating competency questions for an ontology to successfully answer. This generally means gathering requirements, use cases, organising the data and ontology, testing them and maintenance. Doesn't that sound an awful lot like waterfall? It would appear obvious, that just like code, ontologies could be developed using test driven design (neh development) instead. This should have the same benefits as agile code - like being more adaptable to change.
The closest paper I've come to that talks about this idea is, "Unit tests for ontologies" (and there's also bachelor thesis, Unit Testing for Ontologies). Unfortunately, it talks about adding tests during the maintenance of software. Luckily, it talks about using them as a way to initially build the ontology and surely that would be a way to drive out an ontology. Good stuff. I could imagine that certain good patterns of ontology design like making them small, self consistent, fast, intuitive, etc. would all come naturally out of applying TDD techniques.
As an aside the unit testing paper also mentions the usefulness of autoepistemic operators (K and A). You can state that for a given instance there must exist a certain property, for example, a country must have a capital city.
Showing posts with label ontology. Show all posts
Showing posts with label ontology. Show all posts
Monday, April 13, 2009
Tuesday, March 03, 2009
A Quick Survey of Bio-Ontologies
The other day I was trying to find a paper that talks about the need for ontologies in biology that dated sometime around the early 90s - way before OWL and the Semantic Web. I couldn't find the paper I was thinking about, but here are some others that are pretty good that seem to follow at least the same themes:
"Ontologies for molecular biology": "Molecular biology has a communication problem. There are many databases using their own labels and categories for storing data objects and some using identical labels and categories but with a different meaning."
"Ontological Foundations for Biology Knowledge Models": This one was good because it talked about processes and transformations which is where the rules and inferencing stuff comes in.
"Toward Principles for the Design of Ontologies Used for Knowledge Sharing": While not specifically about biology, this is probably the most cited paper and it's what I often think about when you're explaining ontologies and the process of improvement that occurs when you make one.
"Bio-ontologies: current trends and future directions": This covers the process and the parts that make a good ontology on the web. I guess the key for the use of the Web for ontologies is a means to share knowledge. It also has a good history of ontologies going back to the 1600s.
And this one is one of the original GO papers, "Gene Ontology: tool for the unification of biology".
"Ontologies for molecular biology": "Molecular biology has a communication problem. There are many databases using their own labels and categories for storing data objects and some using identical labels and categories but with a different meaning."
"Ontological Foundations for Biology Knowledge Models": This one was good because it talked about processes and transformations which is where the rules and inferencing stuff comes in.
"Toward Principles for the Design of Ontologies Used for Knowledge Sharing": While not specifically about biology, this is probably the most cited paper and it's what I often think about when you're explaining ontologies and the process of improvement that occurs when you make one.
"Bio-ontologies: current trends and future directions": This covers the process and the parts that make a good ontology on the web. I guess the key for the use of the Web for ontologies is a means to share knowledge. It also has a good history of ontologies going back to the 1600s.
And this one is one of the original GO papers, "Gene Ontology: tool for the unification of biology".
Friday, July 04, 2008
Of Mats and Cats
No universal things Re: comparing XML and RDF data models was started by Bernard Vatant. This comes to the heart of whether people can know reality (well that's how I'd summarize the idea of universals see Beyond Concepts).
There were a few quotes that I found interesting:
Bijan wrote:
I really feel like an interested amateur and my view is probably influenced by databases in computer science, where you are taking the non-realist approach. I say this because there are usually properties in databases that are not really based on reality but are a result of other requirements (like a column like "isDeleted" rather than actually deleting the statement).
There were a few quotes that I found interesting:
It's been counter-productive in science for centuries. Physics had to go over the notion of universal thing to understand that light is neither a wave, nor a particle. Biology to go over the notion of taxa as rigid concepts based on phenotypes to understand genetics etc. Many examples can be found in all science domains. My day-to-day experience in ontology building, listening to domain experts, is indeed not that 'there are things that people are trying to describe', but that 'there are descriptions people take for granted they represent things before you ask, but really don't know exactly what those things are when you make them look closely'.
Bijan wrote:
I do think that the family of views in computational ontologies generally called "realist" is indeed naive and fundamentally wrong headed. Whether it's a "useful fiction" that helps people write better or more compatible ontologies is an open empirical question.
But I, for one, wouldn't bet on it.
I remember also a project where we were trying to get people to write simple triples. They got that they needed triples. But what they ended up putting into the tool was things like
S P O
"The cat is" "on the" "mat".
"Mary eats" "pudding" "on toast"
They just split up the sentences into somewhat equal parts!
I really feel like an interested amateur and my view is probably influenced by databases in computer science, where you are taking the non-realist approach. I say this because there are usually properties in databases that are not really based on reality but are a result of other requirements (like a column like "isDeleted" rather than actually deleting the statement).
Tuesday, March 04, 2008
Save Ontologies from the Ontologists
I presented a talk at InterOntology08 last week (list of slides presented). It was only 15 minutes so there wasn't room for much content. What I think is the most important slide was number 11 about how the BioMANTA project is attempting to produce ontologies as an agile, engineering artefact that are verified in reality (due to experiments being performed, provenance tracked and data analysis on the quality of the provenance to filter out irrelevant or incorrect data).
There were some good things to come out if it. Thinking about how to describe to other people problems with ontologies in terms of inconsistencies - what will be inferred that contradicts your ontology - was very useful. The work done by Werner Ceusters, Nicola Gurino and Yu Lin were the most close to our work. One of the speakers gave what I think as a succinct description of the difference between top down vs. bottom ontology development: "what to expect" vs. "what to extract". I also met a lot of great people who I hope to meet again and Japan was very cool.
Easily the best, in terms of the most thought provoking, was Barry Smith's, "The Evaluation of Ontologies: Editorial Review vs Democratic Ranking". This discussed the work of the Gene Ontology and the OBO Foundry. He cited the Gene Ontology as the most useful and most used ontology which has been developed using a top down process. It allows comparable data to be produced, it removes data silos and he compared it to creation of standard measures (metric system). He said that in order to achieve this standardisation you need editorial committees. An ontology becomes part of the peer review, journal process. He introduced the OBO Foundry which has many principles such as being open, has a formal language, collaborative, orthogonal components, versioned, well documented and must have data before it can be accepted.
The alternative view he offered was attributed to Mark Musen. It's a bottom up, annotation of ontologies and many of the slides were taken from a previous talk. Mark believes that ontologies are still a cottage industry and that it is often difficult to ascertain the quality of an ontology just by inspection. He said it is also true that we may wish to use parts of ontologies even if they are not well designed. He questions whether a top down approach can scale. He is developing BioPortal which offers a way to upload and rate various ontologies. The key question about BioPortal is whether it will generate enough interest to reach a critical mass of reviews.
I had many problems with this talk. Firstly, the way it was characterised as one vs. the other - why can't they both work? What stops peer reviews of popular ontologies or getting popular ratings of peer reviewed ontologies. Barry mentioned that a selection approach works for refrigerators (where peer review designs the function of the refrigerator and colour is selected by the masses) but questioned whether this should work for science. This is an obviously negative view of what mass selection can do - we choose representatives in a democracy or successful products in a capitalist market, surely these are very important things that are left to the masses. Are ontologies any less than these things?
Beyond that, both of these methods seem to suggest a certain centralisation. Doesn't this encourage gatekeepers, people holding onto power, hasn't the web (governments, capitalism, science etc.) shown that decentralisation is better? I see science as a competition of ideas, the best model is chosen over many possible ones that best fits existing data and predicts new observations.
One of the OBO Foundary principles is that you can't reuse an ontology. That is, if you're outside the OBO Foundry and you make a change you can't redistribute or use the same identifiers. This just seems wrong. I must be misunderstanding this part, because it is supported by people who I would expect to support the idea of reusing ideas and, most importantly, sharing them with others.
Many of these arguments seems to be around whether an ontology is attempting to create or represent reality or if its an engineering artefact. I see it as a bit of both but its primary utility, I'd suggest, is as an engineering artefact. It represents a (hopefully working) system.
A simple example is our "fixing" of BioPAX. BioPAX uses string literals for certain properties and this prevents them being used as subjects in RDF. I would like to link, maybe dereference them and do other cool things with them that you can only do with URIs. So I'd like to make a change now, get something working and distribute my software with these changes.
I do think that ontologies should be well documented but documentation can be a barrier when you want to change something, try it out, make more changes, try it out again - the documentation is potentially going to be missing or wrong. The whole process seems to be trying to do too much upfront - which is terrible for the few, overworked ontologist that there are.
I don't want to wait while my ontology gets peer reviewed necessarily - the chances of the right person finding a mistake really doesn't sit with a committee or voting process - I'd like it to include everyone. I'd like to do it cheaply both in time and money; if for no other reason than to see whether it works well. If it doesn't work then it's not a big deal I can just change it back. It seems that if this was part of a big process then it would be less likely to happen.
Both methods also lack verification (or at least it wasn't discussed). There's nothing to say that a bunch of people in the OBO Foundary or a voting process will necessarily achieve certain modelling objectives - something that is right for me or for everyone. Like most systems, ontologies will have contradictory requirements such as flexibility and completeness or security and privacy - there really isn't one true answer. I'd prefer a process that quickly adapts to changing requirements which can then be verified.
There were some good things to come out if it. Thinking about how to describe to other people problems with ontologies in terms of inconsistencies - what will be inferred that contradicts your ontology - was very useful. The work done by Werner Ceusters, Nicola Gurino and Yu Lin were the most close to our work. One of the speakers gave what I think as a succinct description of the difference between top down vs. bottom ontology development: "what to expect" vs. "what to extract". I also met a lot of great people who I hope to meet again and Japan was very cool.
Easily the best, in terms of the most thought provoking, was Barry Smith's, "The Evaluation of Ontologies: Editorial Review vs Democratic Ranking". This discussed the work of the Gene Ontology and the OBO Foundry. He cited the Gene Ontology as the most useful and most used ontology which has been developed using a top down process. It allows comparable data to be produced, it removes data silos and he compared it to creation of standard measures (metric system). He said that in order to achieve this standardisation you need editorial committees. An ontology becomes part of the peer review, journal process. He introduced the OBO Foundry which has many principles such as being open, has a formal language, collaborative, orthogonal components, versioned, well documented and must have data before it can be accepted.
The alternative view he offered was attributed to Mark Musen. It's a bottom up, annotation of ontologies and many of the slides were taken from a previous talk. Mark believes that ontologies are still a cottage industry and that it is often difficult to ascertain the quality of an ontology just by inspection. He said it is also true that we may wish to use parts of ontologies even if they are not well designed. He questions whether a top down approach can scale. He is developing BioPortal which offers a way to upload and rate various ontologies. The key question about BioPortal is whether it will generate enough interest to reach a critical mass of reviews.
I had many problems with this talk. Firstly, the way it was characterised as one vs. the other - why can't they both work? What stops peer reviews of popular ontologies or getting popular ratings of peer reviewed ontologies. Barry mentioned that a selection approach works for refrigerators (where peer review designs the function of the refrigerator and colour is selected by the masses) but questioned whether this should work for science. This is an obviously negative view of what mass selection can do - we choose representatives in a democracy or successful products in a capitalist market, surely these are very important things that are left to the masses. Are ontologies any less than these things?
Beyond that, both of these methods seem to suggest a certain centralisation. Doesn't this encourage gatekeepers, people holding onto power, hasn't the web (governments, capitalism, science etc.) shown that decentralisation is better? I see science as a competition of ideas, the best model is chosen over many possible ones that best fits existing data and predicts new observations.
One of the OBO Foundary principles is that you can't reuse an ontology. That is, if you're outside the OBO Foundry and you make a change you can't redistribute or use the same identifiers. This just seems wrong. I must be misunderstanding this part, because it is supported by people who I would expect to support the idea of reusing ideas and, most importantly, sharing them with others.
Many of these arguments seems to be around whether an ontology is attempting to create or represent reality or if its an engineering artefact. I see it as a bit of both but its primary utility, I'd suggest, is as an engineering artefact. It represents a (hopefully working) system.
A simple example is our "fixing" of BioPAX. BioPAX uses string literals for certain properties and this prevents them being used as subjects in RDF. I would like to link, maybe dereference them and do other cool things with them that you can only do with URIs. So I'd like to make a change now, get something working and distribute my software with these changes.
I do think that ontologies should be well documented but documentation can be a barrier when you want to change something, try it out, make more changes, try it out again - the documentation is potentially going to be missing or wrong. The whole process seems to be trying to do too much upfront - which is terrible for the few, overworked ontologist that there are.
I don't want to wait while my ontology gets peer reviewed necessarily - the chances of the right person finding a mistake really doesn't sit with a committee or voting process - I'd like it to include everyone. I'd like to do it cheaply both in time and money; if for no other reason than to see whether it works well. If it doesn't work then it's not a big deal I can just change it back. It seems that if this was part of a big process then it would be less likely to happen.
Both methods also lack verification (or at least it wasn't discussed). There's nothing to say that a bunch of people in the OBO Foundary or a voting process will necessarily achieve certain modelling objectives - something that is right for me or for everyone. Like most systems, ontologies will have contradictory requirements such as flexibility and completeness or security and privacy - there really isn't one true answer. I'd prefer a process that quickly adapts to changing requirements which can then be verified.
Friday, June 22, 2007
Getting up to Speed With OWL 1.1
Bijan's "Two Interesting Quotes" really got me started on understanding the new features of OWL 1.1. The addition of the RBox (to ABox and TBox), using SROIQ instead of SHOIN, is the obvious one.
The second interesting quote was from, "Describing chemical functional groups in OWL-DL for the classification of chemical compounds" which gives some clear examples using the new features of OWL 1.1 including qualified cardinality, negation and property chains. The first two are analogous to: chemical has two bonds and chemical has exactly one bond.
Property chains are another interesting feature which is highlighted in the above paper, in "The OBO to OWL mapping, GO to OWL 1.1!" and in the first paper Bijan spoke about. In one way this is a bit disappointing because the OBO work replicates what we been doing in the BioMANTA project (I hate duplication in all its forms) - we based it on the Protégé plugin. Our relationships are quite simple, mainly is-a, so OWL DL was enough. We hadn't yet encountered some of the problems such as reflexive and anti-symmetric relationships. It also links to a web page mapping OBO to OWL 1.1 and OWL DL semantics.
As noted in "Igniting the OWL 1.1 Touch Paper: The OWL API" the addition of an object model along with the OWL 1.1 specification makes it obvious what an OWL 1.1 API should look like. I haven't yet used OWL API much, except looking at how it integrates with Pellet and how closely it matched the OWL specification. The use of axioms made the addition of RBox a bit easier to understand (or misunderstand if I've got it wrong). Hopefully, punning will be made clear to me eventually too (I've been too stupid to understand it just based on the explanations and too lazy to look into it). Pellet 1.5 also introduces incremental inferencing which is hopefully as good as it sounds.
The other papers that were of interest: "Representing Phenotypes in OWL" (again making use of qualified cardinality) and "A survey of requirements for automated reasoning services for bio-ontologies in OWL".
And just to round off, I found a very good paper, "Towards a Quantitative, Platform-Independent Analysis of Knowledge Systems", about all the mistakes that can be made during modeling (errors, assumptions, etc.) and other types of failures such as language (under or over expressive), management, learning, reasoning, querying, and others.
And I know I'll need to come back to this, which lists the fragments of OWL 1.1 that can be used to keep things computable in polynomial time.
The second interesting quote was from, "Describing chemical functional groups in OWL-DL for the classification of chemical compounds" which gives some clear examples using the new features of OWL 1.1 including qualified cardinality, negation and property chains. The first two are analogous to: chemical has two bonds and chemical has exactly one bond.
Property chains are another interesting feature which is highlighted in the above paper, in "The OBO to OWL mapping, GO to OWL 1.1!" and in the first paper Bijan spoke about. In one way this is a bit disappointing because the OBO work replicates what we been doing in the BioMANTA project (I hate duplication in all its forms) - we based it on the Protégé plugin. Our relationships are quite simple, mainly is-a, so OWL DL was enough. We hadn't yet encountered some of the problems such as reflexive and anti-symmetric relationships. It also links to a web page mapping OBO to OWL 1.1 and OWL DL semantics.
As noted in "Igniting the OWL 1.1 Touch Paper: The OWL API" the addition of an object model along with the OWL 1.1 specification makes it obvious what an OWL 1.1 API should look like. I haven't yet used OWL API much, except looking at how it integrates with Pellet and how closely it matched the OWL specification. The use of axioms made the addition of RBox a bit easier to understand (or misunderstand if I've got it wrong). Hopefully, punning will be made clear to me eventually too (I've been too stupid to understand it just based on the explanations and too lazy to look into it). Pellet 1.5 also introduces incremental inferencing which is hopefully as good as it sounds.
The other papers that were of interest: "Representing Phenotypes in OWL" (again making use of qualified cardinality) and "A survey of requirements for automated reasoning services for bio-ontologies in OWL".
And just to round off, I found a very good paper, "Towards a Quantitative, Platform-Independent Analysis of Knowledge Systems", about all the mistakes that can be made during modeling (errors, assumptions, etc.) and other types of failures such as language (under or over expressive), management, learning, reasoning, querying, and others.
And I know I'll need to come back to this, which lists the fragments of OWL 1.1 that can be used to keep things computable in polynomial time.
Friday, June 08, 2007
Bottom Up Semantics
A couple of recent articles on DevX have come up by Rod Coffin exploring how a bit of semantics (and Semantic Web technology) can help improve your applications ability to understand what people are trying to do. The first, Let Semantics Bring Sophistication to Your Applications introduces the concepts of ontologies and querying. The second, Use Semantic Language Tools to Better Understand User Intentions shows how using WordNet you can supplement existing applications to improve search results.
Update: DevX now has a Semantic Web Zone.
Update: DevX now has a Semantic Web Zone.
Monday, April 16, 2007
Ontologies in 3D
"OntoSphere3D is a Protégé plug-in for ontologies navigation and inspection using a 3-dimensional hyper-space where information is presented on a 3D view-port enriched by several visual cues (as the colour or the size of visualized entities)."
While I'm not usually a fan of 3D interfaces this is a fairly intuitive approach. It's certainly seems better than the neighbourhood view and hyperbolic view that they talk about in their paper. They offer a "global view" (projected onto a sphere) and "tree focus".
Java 3D can be pain to install, at least it was for me, as it wouldn't detect Protégé's JRE to install into.
While I'm not usually a fan of 3D interfaces this is a fairly intuitive approach. It's certainly seems better than the neighbourhood view and hyperbolic view that they talk about in their paper. They offer a "global view" (projected onto a sphere) and "tree focus".
Java 3D can be pain to install, at least it was for me, as it wouldn't detect Protégé's JRE to install into.
Thursday, December 14, 2006
Relational OWL
As a sequel (not SQL) to relational SPARQL there's relational OWL, "In this paper we analyzed the similarities and differences between relational databases and description logics, in particular with respect to the role of schema constraints. Our analysis reveals more similarities than differences since, in both cases, constraints are just (restricted) first-order theories. Furthermore, reasoning about the schema is in both systems performed under standard first-order semantics, and it even employs closely related reasoning algorithms. The differences between relational databases and description logics become apparent only if one considers the problems related to reasoning about data. In relational databases, answering queries and constraint satisfaction checking correspond to model checking, whereas, in description logics they correspond to entailment problems."
Via All Problems Solved.
Via All Problems Solved.
Wednesday, September 06, 2006
Relational.OWL
Relational.OWL - A Data and Schema Representation Format Based on OWL "In this paper we introduce a Web Ontology Language (OWL)-based (Miller & Hendler 2004) representation format for relational data and schema components, which is particularly appropriate for exchanging items among remote database systems. OWL, originally created for the Semantic Web enables us to represent not only the data itself, but also its interpretation, i.e. knowledge about its format, its origin, its usage, or its original embedment in specific frameworks.
Hence, remote databases are instantly able to understand each other without having to arrange an explicit exchange format - the usage of OWL on both sides is sufficient. This would be impossible using present XML formats."
"Bizer introduced in (Bizer 2003) a mapping language between relational data and RDF, particularly between specific relational query results and RDF. Contrary to our approach, D2R MAP converts the stored data into ”real” RDF objects, i.e. an address would be represented as a RDF address ob ject. This approach takes into account, that the original database cannot be reconstructed using this kind of data representation anymore, since it does not contain information concerning the original schema of the database. As a result, the data represented with the D2R MAP language looses its relationship to the original database. Tracing the data to its original storage position is thus hardly possible."
Homepage, other papers (such as "Database to Semantic Web Mapping using RDF Query Languages" and the good "Bringing Relational Data into the Semantic Web using SPARQL and Relational.OWL") and documentation with beta versions of the software written in Java (uses Jena).
D2R Server 0.3 was release recently too.
Hence, remote databases are instantly able to understand each other without having to arrange an explicit exchange format - the usage of OWL on both sides is sufficient. This would be impossible using present XML formats."
"Bizer introduced in (Bizer 2003) a mapping language between relational data and RDF, particularly between specific relational query results and RDF. Contrary to our approach, D2R MAP converts the stored data into ”real” RDF objects, i.e. an address would be represented as a RDF address ob ject. This approach takes into account, that the original database cannot be reconstructed using this kind of data representation anymore, since it does not contain information concerning the original schema of the database. As a result, the data represented with the D2R MAP language looses its relationship to the original database. Tracing the data to its original storage position is thus hardly possible."
Homepage, other papers (such as "Database to Semantic Web Mapping using RDF Query Languages" and the good "Bringing Relational Data into the Semantic Web using SPARQL and Relational.OWL") and documentation with beta versions of the software written in Java (uses Jena).
D2R Server 0.3 was release recently too.
Saturday, September 02, 2006
Attacking the Boundary of Ignorance
- The Ontology Integration Problem "The reason I think a mapping tool is a critical need is that I think while in theory it's a nice idea to imagine ontologists reusing ontologies from one another, in practice many ontologists (especially those working on large complex ontologies) would rather write their own internally consistent ontologies and map them to other ontologies rather than importing other ontologies into what they are making and then having to deal with all the inconsistencies and confusion that arises from doing that."
- Language Wars "I know Paul told you that he made his app in Lisp and then he made millions of dollars because he made his app in Lisp, but honestly only two people ever believed him and, a complete rewrite later, they won't make that mistake again.
The safe answer, for the Big Enterprisy Thing where you have no interest in being on the cutting edge, is C#, Java, PHP, or Python..." - Circles of knowledge and boundaries of ignorance "...earning can be viewed as either expanding your circle of knowledge or as increasing your boundary of ignorance. So, the more you learn the more you know, but also the more you know that you don’t know. Depending on your temperament, this can be either encouraging or discouraging to your efforts to continue learning."
- Bigtable: A Distributed Storage System for Structured Data From Google Research Papers
- JUnit 4 vs. TestNG A few advantages of TestNG such as dependencies and providing test data (even complex objects).
Friday, December 23, 2005
More on Disjunction
I think this is just going to happen time and time again, "RDF non-sense": "The W3C Working Group members argue SPARQL is a mixed mode language that does support OR, though they are calling it an "optional union". Frankly, I see no point in renaming something people understand to mean one thing. It gives me the impression the W3C want to be thought police and enforce a certain way of thinking.
On the practical side, many analysts happen to like OR disjunctions and would complain loudly. Of course, there are plenty of cases where users abuse the power and write deeply nested disjunctions. That is not a valid reason in my mind to avoid disjunction. It saves the user time and allows them to write simpler rules using disjunction. The W3C seems love RDF and wants the world to love it. Unfortunately, the current specification is a complete piece of junk. I hope RDF dies a quick and public death."
I declare...backward chaining suits me fine! "My two favorite declarative tools right now are Pellet and Prova, both of which are open source java SemWeb tools that are highly compatible with Jena, which recently got a bump to 2.3 with fairly complete SPARQL support. Pellet is an implementation of OWL-DL and some related description logic facilities by the Mindswap guys in Maryland, who have absorbed some of the l33t Kowari/Tucana guys, too (Tucana was recently picked up by Northrop, BTW)."
"Prova is a prolog-variant built on top of Mandarax. It is a very effective and fun medium for scripting of high-level relationships and operations. The integration of prolog unification, java types, java methods, and java exceptions is done very nicely, and yields fine code economy. There are some rough edges in the docs, but we are helping to get these worked out in the pretty soon."
I guess that means David W is l33t! :-)
On the practical side, many analysts happen to like OR disjunctions and would complain loudly. Of course, there are plenty of cases where users abuse the power and write deeply nested disjunctions. That is not a valid reason in my mind to avoid disjunction. It saves the user time and allows them to write simpler rules using disjunction. The W3C seems love RDF and wants the world to love it. Unfortunately, the current specification is a complete piece of junk. I hope RDF dies a quick and public death."
I declare...backward chaining suits me fine! "My two favorite declarative tools right now are Pellet and Prova, both of which are open source java SemWeb tools that are highly compatible with Jena, which recently got a bump to 2.3 with fairly complete SPARQL support. Pellet is an implementation of OWL-DL and some related description logic facilities by the Mindswap guys in Maryland, who have absorbed some of the l33t Kowari/Tucana guys, too (Tucana was recently picked up by Northrop, BTW)."
"Prova is a prolog-variant built on top of Mandarax. It is a very effective and fun medium for scripting of high-level relationships and operations. The integration of prolog unification, java types, java methods, and java exceptions is done very nicely, and yields fine code economy. There are some rough edges in the docs, but we are helping to get these worked out in the pretty soon."
I guess that means David W is l33t! :-)
Saturday, January 29, 2005
New SOFA
SOFA (Simple Ontology Framework API) New home and new release available for download. Updated support for Jena 2, new methods on OntologyModel and ThingModel discarded (will have to change this for Kowari).
Saturday, February 21, 2004
Ontological Programming
Semantic Integration "Basically semantic integration seems to involve using RDF/OWL to define mappings between XML vocabularies.
Recently I've been involved in co-ordinating a number of large scale data migrations to help integrate several systems. This inevitably involved exploring the business models in the affected applications to define a mapping layer to allow the data to be cleanly migrated. Also inevitably this mapping ends up being expressed as a bunch of (hairy!) procedural code. It would have been nice to have been able to express that mapping in a more declarative way, if no other reason than it's easier to understand and debug possible problems. An example of not being able to see the wood for the trees."
There are many way to declaratively map different schemas - SQL, OO and XML all have their own tools. The value that RDF has is that you can program to a single ontology and map all of these different data sources to it.
With RDF there's not only the ability to map between XML vocabularies but to program to an ontology rather than a schema. In this case, the problem goes from MxN to M+N (or even just M).
One of the articles that highlights this issue is The Impedance Imperative Tuples + Objects + Infosets =Too Much Stuff!.
Recently I've been involved in co-ordinating a number of large scale data migrations to help integrate several systems. This inevitably involved exploring the business models in the affected applications to define a mapping layer to allow the data to be cleanly migrated. Also inevitably this mapping ends up being expressed as a bunch of (hairy!) procedural code. It would have been nice to have been able to express that mapping in a more declarative way, if no other reason than it's easier to understand and debug possible problems. An example of not being able to see the wood for the trees."
There are many way to declaratively map different schemas - SQL, OO and XML all have their own tools. The value that RDF has is that you can program to a single ontology and map all of these different data sources to it.
With RDF there's not only the ability to map between XML vocabularies but to program to an ontology rather than a schema. In this case, the problem goes from MxN to M+N (or even just M).
One of the articles that highlights this issue is The Impedance Imperative Tuples + Objects + Infosets =Too Much Stuff!.
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