Knowledge Management: Retrieve, Visualize and Communicate!

As noted by Swedish daily paper Metro, Findwise is working with JCDEC, the Joint Concept Development & Experimentation Centre at Swedish Military Headquarters. In Metro’s words the project aims at developing a knowledge management system for the headquarters of tomorrow. The system is expected to be up and running in time for the international military exercise VIKING 11, to be executed in April of 2011.

Good decisions stem from good information; this is true for both military and civilian enterprises. Vast amounts of time and resources are being invested in order to collect information. But to what end? Granted, somewhere among that information there is probably something you will find useful. But large amounts of information quickly become incomprehensible. In order to combat information overload you need a select-and-filter tool such as Search, and that’s where Findwise comes in.

However, for JCDEC it is not enough to simply locate the information they have available. Captain Alexandra Larsson, Concept Development Lead for Knowledge Support, makes this fact very clear. It is just as important to get an idea of what information is not there. In essence, JCDEC is in the process of creating information from information. This is also one of the great differences between the kind of web-based search and retrieval systems we have come to depend on and a state of the art knowledge management system. The latter is not just a retrieval tool; it is an information workbench where the user can select, retrieve, examine and manipulate information.

The key to finding information gaps is to study patterns. For example, consider the trivial problem of birthday distributions. Without any prior knowledge one would probably expect there to be roughly as many births in May as in August or November. This is not always the case. Depending on where you are in the world birth figures may actually be skewed so that one month has significantly more births than other months do. Why does this happen? Being able to pose that exact question may in turn teach us a lot about the mysterious workings of the Stork.

In military intelligence the filling of information gaps may mean the difference between victory and defeat. Why is there an increase in partisan activity in that district? Why were eight weapons silos raided over the course of two days? Why at this moment in time? These questions are expected to lead to insights into the plans and activities of suspects and to notify those in command of looming threats.

Retrieve, Visualize, Communicate

Retrieval

The envisioned work-flow for JCDEC information operators is threefold: retrieval, visualization and communication. Each research session will typically be initiated through a keyword search interface, much like you would issue a web query on Google. Just like its online counterpart the system would present the results ordered according to their expected relevancy to the operator’s query. Using facets and query refinement the result set can be narrowed down until the information in front of the operator is expected to contain that which is being sought for.

Captain Alexandra Larsson hints at another strategy for getting to information. Facets are so speedy these days that they can be applied on the full document set without any delays. Clearly, JCDEC is using search technology to provide directory listings much like websites such as the Open Directory Project, although completely dynamic. The option of simply browsing these directories is also available to operators.

Visualization

The next step, visualization, employs an array of tools for visually displaying the results. These include plotting objects on maps and timelines and looking for groupings where objects have a disproportionately dense distribution, so called cluster analysis, among others. This is where clues are uncovered and questions posed: why there, at that time, with those people? In some cases a field investigation is necessary in order to answer these questions. Other times the answers can be deduced from the tools themselves. The tools also allow the operator to formulate new search queries based on the visual information. The operator may choose to limit the scope of the search to one or more of the clusters in the timeline or map, for example.

Communication

If or when the operator finds something interesting this should be recorded. But to JCDEC it is not necessarily the results themselves that are important. The act of getting to the information is valuable in itself. The reason for this is that different operators have different backgrounds and possess different types of information. Where one operator filters or deduces information from a search result in one way, another operator might choose a completely different approach and unveil other clues.

According to Captain Alexandra Larsson it is absolutely necessary that operators share knowledge as well as refinement strategies as part of their work. One of the paradigms that JCDEC is looking to experiment with is social bookmarking along with the ability to search through sets of bookmarked objects. Objects can be both tagged and commented on, useful for conveying meta-information to fellow operators. It is likely that there will be custom-based filters, where an operator can inform the system of types of objects that do and do not interest him or her and have the system automatically filter the result sets based on this information. These filters can of course also be shared with other operators.

An evolving system

The process of retrieval, visualization and communication is only one, albeit the most prominent, feature of the JCDEC knowledge management system. The system itself will be put to use in the spring of 2011 and development will surely continue beyond that point. The ideas and concepts at work today will most likely be refined over time as Captain Alexandra Larsson and JCDEC learn from hands-on experience with working with information. And as evolution progresses I hope to be able to go into more detail on some of the other tidbits.

Query Suggestions Help Users Get Unstuck

Several papers at the HCIR09 workshop touched on the topic of query suggestions. Chirag Shah and Gary Marchionini presented a poster about query reuse in exploratory search tasks and Diane Kelly presented results from two different studies that examined people’s use of query suggestions and how usage varied depending on topic difficulty. (Their papers are available for download as part of the proceedings from the workshop.)

According to Shah and Marchionini users often search for the same things. They reuse their previous queries e.g. search for the same things multiple times. Users use their previous searches to refind information and also to expand or further filter their previous searches by adding one or more keywords. There is also a significant overlap between what different users search for suggesting that users have a tendency to express their information needs in similar ways. These results support the idea that query suggestions can be used to help users formulate their query.  Yahoo and YouTube  are two of the systems that uses this technique, where users get suggestions of queries and how they can add more words to their query based on what other users have searched for.

Diane Kelly concludes that users use query suggestion both by typing in the same thing as shown in the suggestion and by clicking on it. Users also tend to use more query suggestions when searching for difficult topics. Query suggestions help users get “unstuck” when they are searching for information.  It is however hard to know whether query suggestions actually return better results. The users expectation and preferences do have an effect on user satisfaction as well. User generated query suggestions are also found to be better than query suggestions generated by the search system. So the mere expectation that the query suggestions will help a user could have an positive effect on his or hers experience…

Query suggestions are meant to help the users formulate a good query that will provide them with relevant results. Query suggestions can also work as with yahoo search where query suggestions both suggest more words to add to the query but also provides the users with suggestions for other related concepts to search for. So searching for Britney Spears will for example suggest the related search for Kevin Federline (even though they are now divorced) and searching for enterprise search will suggest concepts such as relevance, information management and off course the names of the different search vendors.

If you apply this to the enterprise search setting the query suggestion could provide the user with several different kinds of help. Combining the user’s previous searches with things other users searched for but also providing suggestions for recommended queries or concepts. The concepts will be high quality information and suggestions controlled by the team managing the search application. It is a way of combining quick links or best bets with query suggestions and a way to hopefully improve the experienced value of the query suggestions. The next step then is to work with these common queries that users search for and make sure that they return relevant results, but that is an entirely different topic…