People who visit a toothpaste isle with only 4 products walk away much happier than those who visit the typical supermarket isle crammed with 40 variants of Colgate. Why? Because they don’t get overwhelmed by a tsunami of possibilities that leaves them wondering if they made the wrong choice.

When it comes to a large organization publishing data, perhaps a similar problem arises. Given all the information in the world that we could publish in structured form, how are we to know which important bits to address first?

Hans-Jörg Happel proposed an interesting way to solve this problem in the Social Semantic Web track at ISWC 2010 today. If we can quantify the need for a particular morsel of information, we can prioritize our efforts to structure and publish data. The question, then, becomes how to quantify information need.

Happel’s idea is to do this by examining missing values from query results. When someone performs a query, they’re stating that they need a particular data set. When one of the items in the query result is empty (such as missing 2010 GDP value for Mexico), that’s a known piece of information that someone needed and didn’t get. If we count up the number of times each of these NULL values occurs, we can begin to keep a priority queue of desired, but missing, data.

So if Mexico’s 2010 GDP is missing from WikiPedia, is that a problem? Well, count up the number queries that returned a NULL for this item and judge quantitatively. If the number is comparatively high, maybe we should prioritize the addition of Mexican economic stats.

He’s created a plugin for Semantic MediaWiki, called Semantic Need, which does exactly this. The list of prioritized information is called the “Extended Knowledge Base” — those things that we want to know, but don’t. As a programmer, I find this project very clever. Developers usually think of NULL values in query results as mere annoyances. But this work turns that around and makes them useful.

One of the themes of the Haystack Group is that focusing on user needs can direct research toward results that are immediately useful. On the semantic web, picking an explicit user goal (helping users communicate effectively using data) can be more effective than picking an abstract goal (building a web of linked data). Our project DataPress attempts to follow this philosophy by helping users add interesting visualizations to their blogs, and as a side effect, showing those users the value of structuring their data. Semantic Need follows this philosophy in another way: it attempts to quantify an existing, realized need for pieces of data so that we know which data is actually useful for structuring right now.

While the presentaiton didn’t address it, the idea behind this talk could be incredibly useful for government data. What if governments provided not links to data sets (as data.gov does) but rather some ontology and a query interface. Then it sits back and sees what users query for. Using an approach like this, the “what data should we publish” problem solves itself: the queries people ask will tell you what data to prioritize for publishing.

Here’s a link to the paper: Semantic Need: Guiding metadata annotations by questions people #ASK