Modern Information Retrieval Chapter 10: User Interfaces and Visualization

Contents

   
4. Graphical Approaches to Query Specification

Boolean query specification!graphical approaches|(

Boolean query specification!direct manipulation direct manipulation

Direct manipulation interfaces provide an alternative to command-line syntax. The properties of direct manipulation are [#!shneiderman97!#, p.205]: (1) continuous representation of the object of interest, (2) physical actions or button presses instead of complex syntax, and (3) rapid incremental reversible operations whose impact on the object of interest is immediately visible. Direct manipulation interfaces often evoke enthusiasm from users, and for this reason alone it is worth exploring their use. Although they are not without drawbacks, they are easier to use than other methods for many users in many contexts.

Several variations of graphical interfaces, both directly manipulable and static, have been developed for simplifying the specification of Boolean syntax. User studies tend to reveal that these graphical interfaces are more effective in terms of accuracy and speed than command-language counterparts. Three such approaches are described below.

  

Figure: The VQuery Venn diagram visualization for Boolean query specification [#!jones98!#].

Boolean query specification!Venn diagrams Venn diagram, query specification with

Graphical depictions of Venn diagrams  have been proposed several times as a way to improve Boolean query specification. A query term is associated with a ring or circle and intersection of rings indicates conjunction of terms. Typically the number of documents that satisfy the various conjuncts are displayed within the appropriate segments of the diagram. Several studies have found such interfaces more effective than their command-language-based syntax [#!jones98!#,#!hertzum96!#,#!michard82!#]. Hertzum and Frokjaer found that a simple Venn diagram representation produced faster and more accurate results than a Boolean query syntax. However, a problem with this format is the limitations on the complexity of the expression. For example, a maximum of three query terms can be ANDed together in a standard Venn diagram. Innovations have been designed to get around this problem, as seen in the VQuery system  [#!jones98!#] (see Figure ). In VQuery, a direct manipulation interface allows users to assign any number of query terms to ovals. If two or more ovals are placed such that they overlap with one another, and if the user selects the area of their intersection, an AND is implied among those terms. (In Figure , the term `Query' is conjoined with `Boolean'.) If the user selects outside the area of intersection but within the ovals, an OR is implied among the corresponding terms. A NOT operation is associated with any term whose oval appears in the active area of the display but which remains unselected (in the figure, NOT `Ranking' has been specified). An active area indicates the current query; all groups of ovals within the active area are considered part of a conjunction. Ovals containing query terms can be moved out of the active area for later use.=-1

  

Figure: The filter-flow visualization for Boolean query specification [#!young93!#].

Boolean query specification!filter-flow model filter-flow model, query specification with

Young and Shneiderman [#!young93!#] found improvements over standard Boolean syntax by providing users with a direct manipulation filter-flow model . The user is shown a scrollable list of attribute types on the left-hand side and selects attributes from another list of attribute types shown across the top of the screen. Clicking on an attribute name causes a listbox containing values for those attributes to be displayed in the main portion of the screen. The user then selects which values of the attributes to let the flow go through. Placing two or more of these attributes in sequence creates the semantics of a conjunct over the selected values. Placing two or more of these in parallel creates the semantics of a disjunct. The number of documents that match the query at each point is indicated by the width of the `water' flowing from one attribute to the next. (See Figure .) A conjunct can reduce the amount of flow. The items that match the full query are shown on the far right-hand side. A user study found that fewer errors were made using the filter flow model than a standard SQL database query. However, the examples and study pertain only to database querying rather than information access, since the possible query terms for information access cannot be represented realistically in a scrollable list. This interface could perhaps be modified to better suit information access applications by having the user supply initial query terms, and using the attribute selection facility to show those terms that are conceptually related to the query terms. Another alternative is to use this display as a category metadata selection interface (see Section ).

Boolean query specification!graphical blocks graphical blocks, query specification with

Anick et al. [#!anick90b!#] describe another innovative direct manipulation interface for Boolean queries. Initially the user types a natural language query which is automatically converted to a representation in which each query term is represented within a block. The blocks are arranged into rows and columns (See Figure ). If two or more blocks appear along the same row they are considered to be ANDed together. Two or more blocks within the same column are ORed. Thus the user can represent a technical term in multiple ways within the same query, providing a kind of faceted query interface. For example, the terms `version 5', `version 5.0', and `v5' might be shown in the same column. Users can quickly experiment with different combinations of terms within Boolean queries simply by activating and deactivating blocks. This facility also allows users to have multiple representations of the same term in different places throughout the display, thus allowing rapid feedback on the consequences of specifying various combinations of query terms. Informal evaluation of the system found that users were able to learn to manipulate the interface quickly and enjoyed using it. It was not formally compared to other interaction techniques [#!anick90b!#].=-1

  

Figure: A block-oriented diagram visualization for Boolean query specification [#!anick90b!#].

Boolean query specification!query preview query previews

This interface provides a kind of query preview : a low cost, rapid turnaround visualization of the results of many variations on a query [#!plaisant97!#]. Another example of query previewing can be found in some help systems, which show all the words in the index whose first letters match the characters that the user has typed so far. The more characters typed, the fewer possible matches become available. The HiBrowse  system described above [#!pollitt97!#] also provides a kind of preview for viewing category hierarchies and facets, showing how many documents would be matched if a category one level below the current one were selected. It perhaps could be improved by showing the consequences of more combinations of categories in an animated manner. If based on prior action and interests of the user, query previewing may become more generally applicable for information access interfaces.=-1 Boolean query specification!magic lenses magic lenses, query specification with

A final example of a graphical approach to query specification is the use of magic lenses. Fishkin and Stone have suggested an extension to the usage of this visualization tool for the specification of Boolean queries [#!fishkin95!#]. Information is represented as lists or icons within a 2D space. Lenses act as filters on the document set. (See Figure .) For example, a word can be associated with a transparent lens. When this lens is placed over an iconic representation of a set of documents, it can cause all documents that do not contain a given word to disappear. If a second lens representing another word is then laid over the first, the lenses combine to act as a conjunction of the two words with the document set, hiding any documents that do not contain both words. Additional information can be adjusted dynamically, such as a minimum threshold for how often the term occurs in the documents, or an on-off switch for word stemming. For example, Figure shows a disjunctive query that finds cities with relatively low housing prices or high annual salaries. One lens `calls out' a clump of southern California cities, labeling each. Above that is a lens screening for cities with average house price below $194,321 (the data is from 1990), and above this one is a lens screening for cities with average annual pay above $28,477. This approach, while promising, has not been evaluated in an information access setting.

  

Figure: A magic lens interface for query specification (courtesy of Ken Fishkin).

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