67 Bricks Blog

I’ve just contributed some code examples to the OpenHandle project.

OpenHandle exposes data from Handle, which is an interesting way of providing persistent digital identifiers for information, incorporating metadata. It’s used by systems such as DSpace and DOI.  In some ways, it’s a competitor to existing DNS-based ways of providing persistent URIs.

The code examples I’ve written demonstrate how to download OpenHandle data in F# and in C#. The C# implementation uses Linq.

Further to my previous post on bloom filters for efficient ontology lookup, I’ve made a simple implementation in F#. This is based on a Java implementation by Ian Clarke.

The neat thing about Ian’s implementation is its use of Random to extend the hashcode provided by the object being stored into a hash of arbitrary length, suitable for use by the bloom filter algorithm. This will reduce the quality of the hash, but for an arbitrary passed-in object, it’s hard to do better. (For a specific application, like storing ontology labels, it would be better to use a more specific algorithm such as a Jenkin’s Hash).

#light
open System
// Based on Java Bloom Filter implementation by Ian Clarke 
// http://locut.us/blog/2008/01/12/a-decent-stand-alone-java-bloom-filter-implementation/
type BloomFilter(bitArraySize : int, expectedElementCount : int) =
    let bitSet  = new System.Collections.BitArray(bitArraySize, false)
    let bitArraySize = bitArraySize
    let expectedElementCount = expectedElementCount
    let k = (int) (Math.Ceiling( ((double) bitArraySize / (double) expectedElementCount) * Math.Log(2.0) ))
    let bitSequence o =
        let r = new Random( hash o )
        Seq.init_infinite (fun n -> r.Next(bitArraySize))
    member b.expectedFalsePositiveProbability =
        Math.Pow((1.0 - Math.Exp( -((double) k) * (double) expectedElementCount / (double) bitArraySize)), (double) k)
    member b.add o =
        let sq = bitSequence o
        for x in 0 to k do bitSet.Set( Seq.hd sq , true)
    member b.addAll os = Seq.iter b.add os
    member b.clear = bitSet.SetAll(false)
    member b.contains o =
        let sq = bitSequence o
        let isSet n = bitSet.Get( Seq.hd sq )
        Seq.for_all isSet [0..k]
    member b.containsAll os = Seq.for_all b.contains os

Using F# makes the code slightly neater than Ian’s Java version - I’ve been able to factor out the hash code into a sequence supplied by the bitSequence function, and to collapse some of the for loops into operations over lists instead. But, the basic structure of the code is still very similar.

Myself, and Leigh Dodds of Ingenta, recently spoke at the Oxford SWIG. We were both talking about SPARQL, the standard query language and protocol for the Semantic Web.

Eamonn Neylon, who organized the session, has written up a summary of the talks. I was talking about DBPedia and how to write SPARQL extension functions; Leigh was talking about his SPARQL tool Twinkle, and about the different SPARQL query forms (SELECT, DESCRIBE, CONSTRUCT, and ASK) and what they are useful for.

One of the problems with large ontologies such as SNOMED Clinical Terms is that they’re, well, large. So, it’s not typically possible to hold all of the ontology in memory at once, and queries against it require a database lookup. It’s possible to eliminate a number of database accesses, and thus speed up the query process, by using a Bloom filter.

A Bloom filter is a memory-efficient probabilistic data structure that lets you test whether a particular item is a member of a set. It may return false positives, but not false negatives. So, by adding all of the terms in your ontology to a Bloom filter, you can do a fast, in-memory check to see whether an entered term definitely doesn’t exist in your ontology. If the Bloom filter reports that the term does exist, then you can confirm with a slower file or database query for that term.

In an application where you expect to encounter many terms that aren’t in the ontology, such as automated metadata extraction from documents, and automated document classification, then this can potentially lead to large performance improvements.

I think there are also interesting possibilities in using Bloom filters in environments where storing a whole ontology isn’t feasible. For example, a JavaScript implementation of a Bloom filter, initialized with a few 100kb of data, could give a fairly high probability of testing accurately whether a particular term exists in an ontology of half-a-million terms.

Last Friday I attended and spoke at the SWIG-UK Semantic Web workshop in Bristol, organized jointly by Hewlett-Packard Research and the Oxford Semantic Web Interest Group (OxSWIG).

It was a fun day, with a number of interesting speakers. I’d say that the common theme was that the speakers generally were using Semantic Web technologies to solve real world problems, without being too concerned about the “Semantic Web” in abstract. Alberto Reggiori of Asemantics described their “RDF on the inside, Web 2.0 on the outside” approach that they’re using for work around the BBC Memoryshare site, and this pragmatic approach was typical of the other speakers.

A talks that particularly stood out for me was the first talk, from Ian Davis of Talis, talking about their “Platform as a Service” model: providing a hosted RDF datastore with a REST API. It seems very much like a higher level version of Amazon’s S3 - but rather than providing a simple data-bucket API, they’re providing a more sophisticated SPARQL interface, as well as a full text search.

I was also very interested in what Graham Klyne of Oxford University said about their experience in providing access to scientific data on the web. I’ve been working with Graham recently, so I’m generally familiar with their work, but it was good to hear more background. They are using a “Data Webs” approach - putting a SPARQL endpoint on top of their existing ePrints repository, so the data stays in its original location. The key points from his talk were the importance of the loose coupling enabled by their current approach, the advantages of leaving data in place rather than extracting it to a separate store, and that rapid progress is possible

I talked about applying Semantic Web technologies to data integration and visualization, working with Oxford University to help visualize gene expression in fruit flies; and working to combine customer data from several sources including a CRM system and visualize it. My slides are available. In the same session as me, Daniel Lewis spoke about the intersection between the Social Web (i.e. Facebook, LinkedIn, etc.) and the Semantic Web. Unfortunately, he borrowed my laptop to do so, so I don’t have any detailed notes on his talk.

There were a few discussions on the day about a follow-up meeting. With luck, there might be another meeting, possibly in the Oxford/Milton Keynes area, early in the new year. It also sounds like Talis might be arranging a SWIG meeting for next year.

In any case, the next evening meeting of the Oxford SWIG is on Wednesday 12th December, at the Lamb and Flag in Oxford.