What Is Concept Searching by Herb Roitblat Transcript by ESIBytes™

Karl Schieneman-Interviewer

Herb Roitblat-Guest

K: Hello everyone.  Welcome to another edition of ESI Bytes.  This is Karl Schieneman, Director of Legal Analytics and Review at JurInnov.  I’m real excited about today’s show as it’s an area that I work in.  We’re going to talk about concept searching and what people mean when they say, “concept searching.”  It means so many different things (depending on who’s saying it).  We have with us one of the pioneers of the concept searching and search engine fields – Herb Roitblat, or Dr. Herb Roitblat we could say.  He’s a PhD and co-founder of the principle at Orcatec LLC.  Before starting at Orcatec, Herb was also the Executive Vice President and Chief Scientist (and co-founder) of Dolphin Search – one of the early search engines in the field.  Herb led the design of the Dolphin Search review tools.  He’s part of the team that brought concept searching and native file review to the e-discovery industry.  Herb’s recognized as an expert in cognitive search, information management, data mining, statistics and e-discovery processes.  He’s been writing about data mining and how technology can ease the burden of e-discovery currently (as well as for years).  Herb, thanks for joining us on the show.

H: Thanks for having me.  (It’s) a great pleasure. 
 
K: Let’s start off…I always like to ask everyone on the show…how did you first become interested in electronic discovery?
 
H: It kind of happened by accident.  We were trying to use some technologies for doing knowledge management and ended up finding out that lawyers needed to do discovery more than they needed to do knowledge management.  We had the technology to do it, so we responded to those two things.  At the time it seemed easy and then we learned what it really was all about.  It hasn’t been easy for at least 10 years. 

K: Okay.  Let’s dive into the topic here.  We’ve heard a lot about concepts searching over the past few years in electronic discovery.  Help us out here (and help the listeners) – what is concept searching?

 H:  Basically, concept searching is using meaning to help find responsive documents.  There are a number of approaches to using that meaning, but they all revolve around the same idea.  Instead of just looking for strings of letters as words, rather, let’s look for words as meaningful things.  We can identify what the words mean using a number of different tools that we can talk about in a bit.  Once we identify that meaning we should be a whole lot better at identifying what the documents are about and which documents are responsive and which ones aren’t.

K: Is the term, “concept searching” overused at this point?  Are there different people attaching different meanings to it?

 H: There are somewhat different meanings attached to “concept searching”.  I don’t know that it’s necessarily being overused so much as there are a variety of tools you can us to get at concept searching.  For example, you could use a thesaurus.  You’re familiar from your intermediate school days (maybe high school) with using Roche’s Thesaurus – other ways of saying things.  In fact, people are very creative in how they say things.  Your job as a searcher is to undo that creativity in the sense of trying to figure out how they could have said something and how you can find it afterwards.  You could also you a taxonomy.  A taxonomy is a hierarchical list of categories.  In a taxonomy, if you’re interested in say, cars – searching for the word “cars”, you might also interested in documents that are in supersets in the category of the word “cars” (such as) documents that talk about vehicles.  If you’re interested in various things, you can move up and down the hierarchy and find things that name something at either a higher or lower level.  A third kind of system for doing concept search involves an ontology.  An ontology is like a taxonomy in that it points to things that are related to one another, but it’s different in that it isn’t required to be just hierarchical.  You can talk about things that are associated, for example, lawyer and attorney are synonym.  You’d find that in a thesaurus, but they’re also related words for a legal professional of the sort.  The legal professional also might be called other things.  There are other words that are associated with “lawyers”, such as “judge” and “case” and “matter”.  You might be interested in documents that talk about those words when you search for a particular words like “lawyer”.  There’s yet another approach to concept searching – this is the one that I’ve tended to follow, and that’s a machine learning kind of approach.  Rather than having somebody sit down and explicitly design a taxonomy or an ontology, you can let the documents tell you what words are related.  In this we follow, say the philosopher Wittgenstein, who argued that the meaning of a word is its use in the language – that’s pretty much right.  Back to our “lawyer” example, any document that has the word “lawyer” in it is also likely to have things like “Esq.” and “judge” and “case” and “matter”.  Conversely, documents that talk about “judge” and “case” and “matter” are likely to be about (the word) “lawyer”, whether “lawyer” appears in it or not.  All of these different approaches try to use meaning to help get at what you’re searching for.  The way they do it is essentially query expansion.  So if you search for lawyer, you can use any of these approaches.  What’s going to happen behind the scenes (sometimes where you can see it and sometimes where you can’t) is going to be a search for “lawyer” + “judge” + “matter” + whatever other words your system tells you are associated.  What that’s going to is bring back documents to focus on the meaning of the word on the very top of your list and it’s going to find documents that you wouldn’t have otherwise thought of.  It’s going to search for these other words in context and using context (even if it doesn’t have that particular word in it) to find the documents that you might not know to look for.

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