Stuart Card PARC (since ’74) Area Manager of the User Interface Research Center Ph.D. in Psychology from Carnegie Mellon Co-authored “The Psychology of Human-Computer Human Interaction”

Why Develop ScentIndex?

Why ScentIndex We are archiving large amounts of existing paper documents that into electronic books. It makes sense to (usually carefully made) subject indexes.

Why not just keyword searches? The function of searching keywords is to find content related to the concept of the keyword. The key word of interest may show up in too high or too low of frequency in a given document. In the former case, a more precise, conceptually related key word may be needed. In the latter case, it may be the reader needs a better key word.

Identifying Conceptual Relatedness

Chi, Hong, Heiser, Park (2006) Flow chart of the ScentIndex algorithm describing how the word semantic association matrix is used.

Word Co-Occurrence Matrix (M) Word 1 Word 2 Word 3 … Word n Word 1 M 2,1 M 3,1 …M n,1 Word 2 M 1,2 M 3,2 … M n,2 Word 3 M 1,3 M 2,3 …M n,3 … …………… Word n M 1,n M 2,n M 3,n … M= M i,j = The number of times word j occurs within a +/- 20 word span of each instance of word i.

From Q to Q’: Expanding user’s key terms ScentIndex takes user’s keyword query (vector Q) and uses spreading activation from the word co- occurrence matrix to identify other conceptually related terms. Q’ is the expanded set of keywords relevant to the user’s original query.

From E(k) to E(k)’: Expanding the subject index E is a vector of all subject entries. E(k) is a single entry in the subject index. Using the same spreading activation equation, keywords that are conceptually related to E(k) can be identified. E(k)’ is the expanded set of keywords relevant to subject index entries.

Putting it together ScentIndex takes the two expanded concept vectors (concepts/words including and related to the user’s query; concepts/words including and related to index entries) and does a cosine similarity comparison to output the most relevant index items.

Customized Subject Index

Usage Scenario (Overview) General User Scenario: 1) Enter key words 2) System narrows down to relevant entries and displays them for the user

Usage Scenario Based on “Biohazard” “What year did Russia open negotiations with Iraq for large fermentation vessels? What year did Vladimir Kryuchkov become chairman of the KGB? Which occurred first?” Steps: 1. Search within Index i.e. “kryuchkov chairman kgb” 2. New single screen index view is created organizes entries (most relevant is on top, exact matches in red), limits the amount the user has to search through 3. Click relevant page words are highlighted

User Study: Comparing ScentIndex and Paper Index Task Type  Retrieving (2 w/ScentIndex, 2 w/Paper Index)- 2 min max  The last natural occuring case of WHICH virus occurred in Somalia in  Comparing (2 w/ScentIndex, 2 w/Paper Index) – 4 min max  What is the death rate of smallpox and tularemia? Which virus has a higher death rate?  Comprehending information (2 w/PaperIndex, 2 w/Book Index) – 6 min  Diseases caused by different ages have different symptoms. Connect the items on the agent list to the symptoms on the right.

Measured  Speed  Accuracy Participant Types  (8) Experts  (8) Novice

Results Participants were faster when using the ScentIndex (M=145) than when using the Paper Subject Index (M=160). There were no interaction effects so:  This was true for both experts and novices.  This was true across all task types (retrieving, comparing and comprehending) Participants were more accurate using ScentIndex (only marginally significant).

Questions? Alternate eBook search techniques?  Keyword search engines (Google, AltaVista)  Cross-referencing table  Natural Language Processing User Study Questions Things good? Is Paper Index v. ScentIndex a valid comparison? What is an alternative? Generalizability of results? Expert v. novice? Other variables?