Exclusive Interview with David Milward, CTO, Linguamatics
February 16, 2009
Stephen Arnold and Harry Collier interviewed David Milward,the chief technical officer of Linguamatics, on February 12, 2009. Mr. Milward will be one of the featured speakers at the April 2009 Boston Search Engine Meeting. You will find minimal search “fluff” at this important conference. The focus is upon search, information retrieval, and content processing. You will find no trade show booths staffed, no multi-track programs that distract, and no search engine optimization sessions. The Boston Search Engine Meeting is focused on substance from informed experts. More information about the premier search conference is here. Register now.
The full text of the interview with David Milward appears below:
Will you describe briefly your company and its search / content processing technology?
Linguamatics’ goal is to enable our customers to obtain intelligent answers from text – not just lists of documents. We’ve developed agile natural language processing (NLP)-based technology that supports meaning-based querying of very large datasets. Results are delivered as relevant, structured facts and relationships about entities, concepts and sentiment.
Linguamatics’ main focus is solving knowledge discovery problems faced by pharma/biotech organizations. Decision-makers need answers to a diverse range of questions from text, both published literature and in-house sources. Our I2E semantic knowledge discovery platform effectively treats that unstructured and semi-structured text as a structured, context-specific database they can query to enable decision support.
Linguamatics was founded in 2001, is headquartered in Cambridge, UK with US operations in Boston, MA. The company is privately owned, profitable and growing, with I2E deployed at most top-10 pharmaceutical companies.
What are the three major challenges you see in search / content processing in 2009?
The obvious challenges I see include:
- The ability to query across diverse high volume data sources, integrating external literature with in-house content. The latter content may be stored in collaborative environments such as SharePoint, and in a variety of formats including Word and PDF, as well as semi-structured XML.
- The need for easy and affordable access to comprehensive content such as scientific publications, and being able to plug content into a single interface.
- The demand by smaller companies for hosted solutions.
With search / content processing decades old, what have been the principal barriers to resolving these challenges in the past?
People have traditionally been able to do simple querying across multiple data sources, but there has been an integration challenge in combining different data formats, and typically the rich structure of the text or document has been lost when moving between formats.
Publishers have tended to develop their own tools to support access to their proprietary data. There is now much more recognition of the need for flexibility to apply best of breed text mining to all available content.
Potential users were reluctant to trust hosted services when queries are business- sensitive. However, hosting is becoming more common, and a considerable amount of external search is already happening using Google and, in the case of life science researchers, PubMed.
What is your approach to problem solving in search and content processing?
Our approach encompasses all of the above. We want to bring the power of NLP-based text mining to users across the enterprise – not just the information specialists. As such we’re bridging the divide between domain-specific, curated databases and search, by providing querying in context. You can query diverse unstructured and semi-structured content sources, and plug in terminologies and ontologies to give the context. The results of a query are not just documents, but structured relationships which can be used for further data mining and analysis.
Multi core processors provide significant performance boosts. But search / content processing often faces bottlenecks and latency in indexing and query processing. What’s your view on the performance of your system or systems with which you are familiar?
Our customers want scalability across the board – both in terms of the size of the document repositories that can be queried and also appropriate querying performance. The hardware does need to be compatible with the task. However, our software is designed to give valuable results even on relatively small machines.
People can have an insatiable demand for finding answers to questions – and we typically find that customers quickly want to scale to more documents, harder questions, and more users. So any text mining platform needs to be both flexible and scalable to support evolving discovery needs and maintain performance. In terms of performance, raw CPU speed is sometimes less of an issue than network bandwidth especially at peak times in global organizations.
Information governance is gaining importance. Search / content processing is becoming part of eDiscovery or internal audit procedures. What’s your view of the the role of search / content processing technology in these specialized sectors?
Implementing a proactive e-Discovery capability rather than reacting to issues when they arrive is becoming a strategy to minimize potential legal costs. The forensic abilities of text mining are highly applicable to this area and have an increasing role to play in both eDiscovery and auditing. In particular, the ability to search for meaning and to detect even weak signals connecting information from different sources, along with provenance, is key.
As you look forward, what are some new features / issues that you think will become more important in 2009? Where do you see a major break-through over the next 36 months?
Organizations are still challenged to maximize the value of what is already known – both in internal documents or in published literature, on blogs, and so on. Even in global companies, text mining is not yet seen as a standard capability, though search engines are ubiquitous. This is changing and I expect text mining to be increasingly regarded as best practice for a wide range of decision support tasks. We also see increasing requirements for text mining to become more embedded in employees’ workflows, including integration with collaboration tools.
Graphical interfaces and portals (now called composite applications) are making a comeback. Semantic technology can make point and click interfaces more useful. What other uses of semantic technology do you see gaining significance in 2009? What semantic considerations do you bring to your product and research activities?
Customers recognize the value of linking entities and concepts via semantic identifiers. There’s effectively a semantic engine at the heart of I2E and so semantic knowledge discovery is core to what we do. I2E is also often used for data-driven discovery of synonyms, and association of these with appropriate concept identifiers.
In the life science domain commonly used identifiers such as gene ids already exist. However, a more comprehensive identification of all types of entities and relationships via semantic web style URIs could still be very valuable.
Where can I find more information about your products, services, and research?
Please contact Susan LeBeau (susan.lebeau@linguamatics.com, tel: +1 774 571 1117) and visit www.linguamatics.com.
Stephen Arnold (ArnoldIT.com) and Harry Collier (Infonortics, Ltd.), February 16, 2009
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