Unlike some taxonomies that have a more “academic” (read: stodgy) approach to what is considered a synonym, we put real-world equivalents in our thesaurus because we want it to work for real-world users. Many users of our health care information sites are pressed for time and are looking for an answer to a specific question. They shouldn’t have to think very hard about how to structure a query so that a search engine can understand it. It’s our job to be knowledgeable about both their language and their lingo. At Silverchair, we believe the searcher is never wrong (our version of “the customer is always right”).

Bob Wachter, with whom we’re privileged to work on two sites sponsored by the Agency for Healthcare Research and Quality (PSNet and WebM&M), recently wrote a humorous blog post about the way his hospital colleagues at UCSF (and other hospitals) commonly turn the nouns of their everyday work life into verbs as a shorthand way of communicating. For example, a resident might report that she “heparinized” her patient, or that a patient ready for discharge has been “housed and spoused,” meaning it had been determined that the patient had somewhere to go and someone to care for him. In addition, he reports the creation of new terms based on healthcare IT functionality, based, for example, on the way buttons are named in an EHR (“I done-ed it”).

That post is a fun reminder of the many ways medical lingo—and language—evolve, and the importance of attentive, systematic approaches to managing and supporting the information needs of those who invent the common parlance of their specialty in the course of doing their work (we hope, while using the sites we develop for them).

Location of Jupiter in Palm Beach County, Florida (image via Wikipedia)

The names of clinical trials are often long and unwieldy, as they try to describe the focus and mission of the trial in their name—for example, a clinical trial studying drug treatment of high cholesterol is “Arterial Biology for the Investigation of the Treatment Effects of Reducing Cholesterol 6–HDL and LDL Treatment Strategies.” Because of these long names, trials are more commonly known by their acronyms—in this case, “ARBITER 6–HALTS” trial—and no doubt their full names are being crafted to result in a catchy or apropos—or hopeful—acronym. For example, the acronym for the trial studying the effect of the drug Vytorin on cholesterol levels is “IMPROVE-IT.” (See this blogpost for some humorous trial names and acronyms.)

One of my pet peeves is the incorrect use of the word “acronym” to mean any abbreviation for a term. Actually an abbreviation is also an acronym only when the abbreviation spells a word or is a combination of letters that people can pronounce as a word. So yes—abbreviations of clinical trials are acronyms, and ah, there’s the rub for commonly used full-text nonsemantic search engines. A full-text search engine treats them like any other word.

So yikes—a PubMed search for “JUPITER” (the acronym for the trial “Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin”) delivers the first two results correctly, but the third result appears because the name of the institution that issued the paper is in Jupiter, Florida! OK so yes—the PubMed search box tries to help you by suggesting “Jupiter trial” (98 results) … but it also suggests “Jupiter study” (257 results). People—the JUPITER trial and the JUPITER study are exactly the same thing to any searcher wanting to know about JUPITER. The number of results should be the same for both searches. And nobody searching PubMed for JUPITER wants to know more about Jupiter, Florida. Trust me.

We can do better. At Silverchair, our Cortex taxonomy contains a list of clinical trials and the accompanying thesaurus includes their acronyms, so when our tagging and retrieval systems encounter those concepts, we’re able to separate them from their normal English language counterparts and tag them correctly.  Yet another benefit of an automated tagging system supported by a robust and up-to-date medical thesaurus. It understands medical information and the health care professionals who depend on it so that we can give them results, not guesses.

I’m sure you’ve experienced Google Suggest in action: as you type into the search box, Google offers suggestions that change dynamically as you type each letter of your query. The suggestions are sometimes spookily on target but many times flat-out inappropriate.

You’ll find many examples of Google Suggest inappropriateness documented online. But the Slate contest took a different angle, challenging readers to explore the different suggestions made in response to a “less intelligent” Google query versus a “more intelligent” one.” The winners are in:

The winning entry … follows Google Suggest into the realm of moral inquiry. It doesn’t neatly divide into “less intelligent” and “more intelligent,” but it’s the best example I received of how one word can make all the difference. [Is it wrong to…] involves love affairs, God, and younger men. [Is it ethical to…] puts us on the plane of animal research, privacy concerns, and cooking the books.

Putting aside the entertainment and cultural value of Google Suggest, how does it work? Like most things Google, those details are vague:

Our algorithms use a wide range of information to predict the queries users are most likely to want to see. For example, Google Suggest uses data about the overall popularity of various searches to help rank the refinements it offers.

On the Silverchair SCM web content management platform, we also use autosuggest to aid searchers. But there’s no mystery about how it works. Once three characters have been typed into the search box, our search engine starts matching the query against the index of semantic tags that have been applied to that specific content set from our Cortex biomedical taxonomy. Suggestions become more precise with each query character typed, and because we are matching against only those semantic tags applied to the content, the search results set is always targeted and relevant. Our search engine also checks each query against a database of taxonomy equivalents—synonyms, abbreviations, jargon—to normalize the search query and expand it to cover all possible matches.

Silverchair search autosuggest

Because the content in the products Silverchair builds is tagged so granularly, we can often suggest a more precise term than many searchers start with. Our goals for autosuggest are to save time for users, speed them to the most relevant possible query, and return the most precise answer to their question. Try autosuggest for yourself on the AccessSurgery site we built for McGraw-Hill. Search autosuggest is just one of the many ways a robust taxonomy can promote content discovery.

(I wonder how many of you are running off to play with Google Suggest—a perfect Friday afternoon time-waster…)

Google Suggest

So—why’d we tackle search first? In a nutshell, search equals money. Search that works makes your website more useful. More useful sites get more usage. And more usage translates to more revenue, or whatever metric you use to measure success.

Jake Zarnegar, President and CTO at Silverchair and a blogger here at It’s All Semantics, kicked things off by highlighting the causes and symptoms of bad search and how to treat them. He also alerted us to some things that complicate search but can’t be controlled:

• Search strings are short and primitive.
• No one reads search help (“learns” your search).
• STM user groups are small and highly specialized.
• Professional terminology is rapidly expanding and less standard at the margins.
• Professional users demand high accuracy and will abandon your search if they lose confidence in it.

Search—The Good, The Bad & The Ugly (slide courtesy of Jake Zarnegar)

Jake left us with some great advice:

• Recognize that ambiguity is the mortal enemy of computer logic.
• Don’t keep secrets from your search engine.
• Create a distilled logical layer of your content’s meaning (semantic tagging guided by a taxonomy).
• Improve search every day.

To put Jake’s presentation in real-world perspective, Matthew O’Rourke, Editorial Director of Journal Watch, followed up with a talk about why Silverchair’s semantic search solution was implemented at Journal Watch.

Matthew taught us that the relatively short length of journal articles (compared to War and Peace, for example) offers fewer clues for search engines; add the complexity of STM content into the mix and searching gets especially tricky. Silverchair’s semantic search solves the problem by using semantic tagging to precisely mark and normalize equivalent concepts and make them findable no matter what term the author chose to describe them and searchers used to find them.

For example, in “old” Journal Watch search, the very rare maple syrup urine disease resulted in 14,341 results, misleading users to assume that there is “an absolute epidemic of maple syrup urine disease” (to quote Matthew from one of the funniest moments in the webinar). Problem? The old search engine searched for “disease” separately from the complete phrase. With Silverchair’s semantic search in place, the results set includes 1 article—the only article covering this rare condition in Journal Watch.

Matthew also reminded us that the customer is always right: Users don’t make search mistakes, publishers just give bad results.

_________

That little empty search box sits between your users and your content. You can have the best content in the world behind the search box, but if people can’t find your content with search, they won’t give you their attention—or their business.

[You can view the recorded webinar by going to the Silverchair home page under “The Forum”; see the Click here to download a recording of the webinar link.]

Tech visionary Esther Dyson recently gave some advice to Yahoo that has relevant nuggets of gold for STM content providers striving to deliver accurate, complete, and relevant search results to their customers. She recommended that Yahoo go back to its past to secure its future by offering a hybrid machine-human indexing and categorization solution to the “TMI” (too much information) problem:

You can’t rely on human editors to structure information anymore; you need automated tools, augmented by human expertise and specific domain knowledge. But search alone doesn’t work, either: Search is like a flashlight in a dark room; it pinpoints one or two things but leaves the surrounding space murky. What people really want is a lighted room, with things organized and displayed neatly on labeled shelves.

We couldn’t have said it better ourselves!

Map created by Robert Lewis Stevenson in Treasure Island (image from Wikipedia)

When good authors write, they choose the terminology they want to describe the topics they are addressing and use that terminology consistently throughout the text. This, of course, is good for readers in terms of internal clarity and consistency.

But this authoring strategy is distinctly disadvantageous to discovery (search) and integration (linking) in modern web applications. Why? Because every time an author makes a terminology choice they EXCLUDE other equivalent options. These excluded options could include terminology that other authors have chosen or are the preferred terminology of their potential readers. I’m not blaming the authors, of course—their writing would be nonsense if they included all equivalent choices in their text.

So how do you deal with these missing options? Thesauri to the rescue! Every web search, linking, and categorization system should employ some form of thesaurus behind the scenes. And in specialized areas like medicine, you’ll need a specialized thesaurus rather than a basic broad one. This thesaurus should include synonyms, acronyms, abbreviations, and jargon, and should be based on real-world authoring and searching behavior (rather than academic nit-picking).

In essence, a thesaurus expands the author’s original text into much richer data for automated searching and linking algorithms. Let’s look at an example:

ACTUAL TEXT: Chemoreceptors in the carotid bodies and medulla are activated by hypoxemia, acute hypercapnia, and acidemia.

EXPANDED TEXT¹: Chemoreceptors in the carotid bodies (carotid glomus, glomera carotica, glomus caroticum, glomus caroticus) and medulla (adrenal medulla, medulla oblongata, glandula suprarenalis, suprarenal medulla, adm, metepencephalon, medullary, myelencephalon) are activated by hypoxemia (hypoxaemia, arterial hypoxemia), acute hypercapnia (blood carbon dioxide increased, blood co2 increased, carbon dioxide retention, carbon dioxide, increased level, hypercapnemia, hypercapnaemia, hypercarbia, pco2 increased on arterial blood gas, elevated pco2, retention carbon dioxide, serum carbon dioxide increased), and acidemia (acidaemia).

The actual text as written was 15 words. The expanded text was 71 words, or approximately 4.7 times longer. Humans read the first sentence, and machines read the second.

No matter how a user searches for this text (“hypercapnia” vs. “hypercarbia,” for example) they will match this text with a good thesaurus.

Are readers finding what they want on your web site this easily?

¹Thesaurus Source: Silverchair’s Cortex taxonomy—with references to SNOMED, Read Codes, MeSH, Digital Anatomist, NCI Thesaurus, NeuroNames Brain Hierarchy, MedDRA, WHO Adverse Reaction Terminology, OMIM, DXplain, CRISP Thesaurus, Clinical Problem Statements, and COSTART.