I was recently at the STI 2011 summit in Riga, Latvia.
This is a meeting of senior participants in the semantic web and sem tech scene, organized by STI of Dieter Fensel fame, with board members like Michael Brodie, Mark Greaves, and Jim Hendler.
This is substantially about the intersection of AI, knowledge representation, and databases. As we have said before, the database side has not been very prominent in these meetings in the past, but this time we had Peter Boncz of CWI, of MonetDB and VectorWise fame, attending the proceedings.
Will DB and AI finally meet? Well, they have met, but how do they get along? Before I try to answer this, let us look at some background.
At present, CWI and OpenLink are working together in the LOD2 EU FP7 project, around the general topic of bringing the best of Relational Database (RDB) science to the Graph Database (GDB) world. Virtuoso has for a few months had a column store capability (which is about to be made available for public preview). CWI has a long history of column store work, with MonetDB and Ingres VectorWise as results. OpenLink's column store implementation is separate in terms of code but is of course influenced by the work at CWI and other published column store results. The plan is to transplant the applicable CWI innovations into the graph context within Virtuoso. These improvements naturally also benefit Virtuoso RDB (SQL), but the LOD2 project is primarily concerned with GDB applications. The RDB yardstick for much of this work is TPC-H, of which we have made a GDB translation. CWI is uniquely qualified as concerns this in light of VectorWise holding some of the top places in the TPC-H charts.
Even now, we do in fact run the 22 TPC-H queries in SPARQL against the Virtuoso column store. True, these run faster in SQL against relational tables but we have established a beach head. From this initial position, we can incrementally improve the GDB/SPARQL and RDB/SQL functions, and see how close to SQL we get with SPARQL. I will make a separate post commenting on the differences between SQL and SPARQL.
So let's get back to Riga. Mark Greaves said in his opening comments that he would be sick if he once again heard complaining about how bad and un-scalable the tools were. From all the talks, I did get the overall impression that just better databasing for Graph Data is still needed. OK, we have 1-1/2 years of unreleased work just for that about to hit the street; advances are substantial. Along these lines, the people from Bio2RDF pointed out that there still is a cost to publishing query services, specially for complex queries. Well, this cost will be substantially reduced.
The takeaway from the meeting is that the most useful thing, for both our public and ourselves, is simply to keep advancing database tech for graph data. In the first instance, this is about launching what we already have; in the second, about going through the CWI record of innovation and adapting this to GDB.
The thinking is that once query-answering on some tens-of-billions of triples is easily interactive no matter what question one asks, a tipping point will be reached, and GDB can efficiently play the role of data-melting-pot that has been envisioned for it.
This is just a beginning, though. Michael Brodie has on a number of occasions pointed out that that (relational) database guys are only about performance with little or no regard to meaning or even questions of the applicability of the relational model. Peter Boncz then comments back that it can well be that the bulk of IT expenditure worldwide in fact goes into data integration. However, data integration is an "AI-complete" problem with infinite variety and consequent difficulty of measurement. So, making better database engines stands a much greater chance of success and has the nicety of relatively unambiguous metrics.
Quite so. We are somewhere in the middle. I'd say that GDB is still at the stage where making better databases is a matter of make-or-break and not a matter of cutting already vanishingly-short response times just for the sake of it. We will have progress if we just keep at it; for now, performance is still a basic need and not a luxury.
Now that there is all this potentially integrable data published as graphs (most commonly as RDF serializations), what do we do? Someone at the Riga meeting suggested we take a look across the tracks to the RDB world to see what is being done there for data integration. The question is raised, what does GDB have for data integration? The automatic answer that GDB and RDF have OWL is not adequate, as was rightly pointed out by many. Having schema-last, global identifiers, and some culture of vocabulary reuse is nice, but this is only a start. To cite an example, owl:sameAs will not work when entities simply do not align: One database models a product as a parts hierarchy; another does the same but now based on the materials used in the parts. One tree just has a node that is not in the other. Besides, things like string matching (as in extracting area codes from phone numbers) are common, and OWL specifically excludes any such functions.
It is now time to look at what will come after all the database advances. In my talk I outlined some things that have or are about to get solutions:
Database technology: Applying advances from RDB (specifically columns, vectoring, and some adaptive query execution) will make GDB a possibility for data warehousing at some scale.
Benchmarks: These advances will be demonstrable through benchmarking. There is a better suite of benchmarks with many variations of BSBM, an GDB-modified TPC-H, and the upcoming Social Intelligence Benchmark (SIBB) with actual graph data. There are the beginnings of an auditing process for result publishing, and a fair chance the semdata world will get its analog of the TPC.
After these basics are more or less in hand, we have a vista of more diverse questions:
What to do about inference? We do not want OWL or RIF for their own sake; instead we want whatever will declaratively facilitate making sense of data. This is an entirely use-case-driven question. If this can have a reasonably generic answer, we will build it into the engine.
Data integration is highly diverse, and tool sets like IBM Infosphere have thousands of modules and functions for different aspects of the problem. To what degree does it make sense to put DI-oriented capabilities into a DBMS?
Is it the case that SQL or SPARQL, plus or minus a few details, is as powerful as a language can be while staying application domain-agnostic? In other words, if more powerful reasoning is built into the query language, will the requirements vary so much between application domains that the work is not generally applicable? Datalog is general enough, but can we demonstrate substantially reduced time to answer with big data if this is built into the engine? Berkeley Orders Of Magnitude claims this, even though their claim is not exactly in a database context. We need use cases to refine the actual requirement for inference.
In all these questions, we of necessity turn to the user community. In fact we do not follow the usage of these technologies as much as we ought to. One outcome of the Riga summit is a set of public challenges that will hopefully ameliorate this state of matters, to be released soon.
The general feeling was that there is more going on on the data side than the AI side. The LOD movement proceeds and lightweight everything predominates, also for knowledge representation. There was some discussion about "pay as you go" integration. On the one hand, there is no up-front integration of information systems just for its own sake, so pay as you go is the only kind that exists, system by system, as the need becomes sufficient. On the other hand, each such integration is a process which has its distinct steps and maintenance and within itself it is planned, and thus pre-paid, so to speak. We need more work with the data itself to better understand the matter. The open government data should offer a playground for this and there will be a special challenge around this.
Schema.org and Microdata got their share of discussion. As we see it, it is good that search engines make their pre-competitive data open. This is better than, for example, Google wanting retailers to put their catalogs in Google Base. We do not care about the specific syntax in which data is embedded; we support them all. Microdata converts easily to triples, and if one wants to make a tabular extraction for use with relational tools, this too is simple enough. Applications will have to do their own entity resolution, but this is independent of data publication format.
All in all, the mood was positive. Mark Greaves noted in his closing remarks that there has been a 1000x increase in published GDB data over a few years. There is in fact a large quantity of technology for tackling almost any aspect of the LOD value chain, but people do not necessarily know about this nor is it easy to integrate. Still there would be great value in integration. Getting software to interoperate in a meaningful way is manual labor, so it might make sense to organize hackathons around this. While the STI Summit is for the senior people, there could be a parallel track of events for bringing the coders together to actually practice tool integration and interoperation.
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Author: Orri Erling
Published: 07/22/2011 11:12 GMT
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