• 1-5 April 2012 – European Conference on Information Retrieval (ECIR) in Barcelona, Spain. An academic conference featuring new developments in IR.
• 7-10 May 2012 – Lucid Imagination’s Lucene Revolution in Boston, USA. The largest conference on open source search – this event has a great buzz as the Lucene/Solr community continues to grow.
• 30/31 May 2012 – Enterprise Search Europe in London, after a successful first event last year. Great for those planning or working on enterprise search projects.

More to come as we hear about them – we’ll also be running another Cambridge Search Meetup soon.

Udo Kruschwitz was next with a brief treatment of his ongoing work on automatically extracting domain knowledge and using this to improve search results (for example see the ‘Suggestions’ on the University of Essex website) – he showed us some of the various methods his team have tried to analyze query logs, including Ant Colony Optimisation (modelling ‘trails’ of queries that can be reinforced by repeat visits, or ‘fade’ over time as they are less used). I liked the concept of developing a ‘community’ search profile where individual search profiles are hard to obtain – and how this could be simply subdivided (so for example searchers from inside a university might have a different profile to those outside). The key idea here is that all these techniques are automatic, so the system is continually evolving to give better search suggestions and hints. Udo and his team are soon to release an open source adaptive search framework to be called “Sunny Aberdeen” which we look forward to hearing about.

The evening ended with networking and a pint or two in traditional fashion – thanks to both our speakers and to all who came, from as far afield as Milton Keynes, Essex and Luton. The group now has 70 members and we’re building an active and friendly local community of search enthusiasts.

However we’re increasingly asked to develop features outside the traditional search stack, to make this standard search a lot more accurate/relevant or to apply ’search’ to non-traditional areas. For example, Named Entity Recognition (NER) is a powerful technique to extract entities such as proper names from text – these can then be fed back into the indexing process as metadata for each document. Part of Speech (POS) tagging tells you which words are nouns, verbs etc. Sentiment Analysis promises to give you some idea of the ‘tone’ of a comment or news piece – positive, negative or neutral for example, very useful in e-commerce applications (did customers like your product?). Word Sense Disambiguation (WSD) attempts to tell you the context a word is being used in (did you mean pen for writing or pen for livestock?).

There are commercial offerings from companies such as Nstein and Lexalytics that offer some of these features. An increasing amount of companies provide their services as APIs, where you pay-per-use – for example Thomson Reuters OpenCalais service, Pingar from New Zealand and WSD specialists SpringSense. We’ve also worked with open source tools such as Stanford NLP which perform very well when compared to commercial offerings (and can certainly compete on cost grounds). Gensim is a powerful package that allows for semantic modelling of topics. The Apache Mahout machine learning library allows for these techniques to be scaled to very large data sets.

These techniques can be used to build systems that don’t just provide powerful and enhanced search, but automatic categorisation and classification into taxonomies, document clustering, recommendation engines and automatic identification of similar documents. It’s great to be thinking outside the box – the search box that is!

C Spencer are known for their innovative and modern approach – they’re even building their own green power station on a brownfield site in Hull. It’s thus not surprising that they chose cutting-edge open source technology for search: tracking and managing documents correctly is extremely important to their business.

The API, available on the Boliven website, offers a REST based service and returns patent data in JSON or XML – so users can easily integrate patent data with their own applications. It can also return PDFs or summaries of the selected patents. In addition, the API will allow users to search and query Boliven’s database of 45+ million science literature documents including journal publications and medical device trials. That’s around 100 million items in total.

Like the Guardian’s Open Platform which I wrote about previously, this is a great example of open source search technology as a platform for new delivery methods – showing how effective (and economical) it can be at this large scale.

It didn’t take me long to find my own small contribution to the patent landscape.

One can’t deny that the search market is going through some huge changes at the moment. Larger vendors are being acquired which can lead to some major (and not always welcome) changes in the product, pricing and service. Smaller vendors are finding it increasingly hard to compete with the plethora of powerful open source solutions (we’ve heard rumours of prices of closed source solutions being dropped radically to attempt to secure new business). There are also some interesting moves towards more comprehensive Business Intelligence and Unified Access solutions, such as Attivio.

I don’t think enterprise search is dying as a market or an offering, simply changing – and hopefully for the better, into an era of more realistic pricing, solutions that actually work (rather than promising ‘magic’) and more openness in terms of the technology and capability.

However the reaction isn’t all positive – the FT implies this deal is at the “lunatic end of the valuation spectrum”. Law Technology News says “Autonomy’s e-discovery revenue stream is high-end but unsustainable” and quotes users of the system with problems: “We had a lot of issues with the applications crashing, the documents tending not to get checked in”….”"[Autonomy sales staff] were pricey, arrogant, and they couldn’t care less about us. … It cannot get any worse.”.

HP will have to work hard to integrate Autonomy into both its corporate culture and software frameworks – a problem currently faced by Microsoft since its acquisition of FAST a short while ago. Stephen Arnold thinks this process will be “risky”. What it means for the rest of the search sector is harder to guess, although Martin White of Intranet Focus says this deal indicates HP can see a “future in search applications” and, interestingly, “A number of privately-held search vendors are probably working out what their valuation would be”.

My view is that this is just the latest of huge shifts in the enterprise search market, partly spurred on by the rise of open source options and the gradual realisation that the huge license fees charged by some vendors may be unsustainable. I don’t think Autonomy will be the last company looking for a safe haven in the years to come.

Thanks to all of those who have worked with us and for us over the last decade – we look forward to the next ten years in this exciting field!

It’s going to be a busy time as I’m also chairing a panel on the first day and helping run the evening reception, which is co-hosted by the London and Cambridge Search Meetups – this is likely to be one of the largest Search Meetups ever and is sure to be a fascinating evening, featuring speakers from the conference in an informal setting (i.e., a pub!).

Hope to see some of you there.

There’s an interesting set of slides from last year’s Apache Lucene Eurocon which discuss this kind of Lucene index post-processing, but we didn’t find any tools to do this particular task (although this doesn’t mean they don’t exist – for example Luke may be helpful). So we wrote our own, based on some examples in the ‘contrib’ directory of Solr 4. We override the document() methods of FilterIndexReader to remove the required field from each returned Document’s field list. Terms aren’t interfered with, so it really is like changing the field from being stored to not being stored; it’s still indexed.

The code is available here. It’s written against Lucene 2.9.3 (which is contained in Solr 1.4.1).