ClusterBook and the WebCluster project

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Second Level

Third Level

Fourth Level

Fifth Level

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12/2/2006

‹#›=

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Mediated Information Retrieval
– The WebCluster and MIR Projects –

Gheorghe Muresan= 3;

Schoo= l of Communication, Information and Library&nb= sp; Sciences

Rutgers University

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Structure of the talk

ØThe WebCluster project

ØDesign decis= ions in WebCluster

ØThe MIR project

ØIntegrated approach to interaction modeling, logging and analysis

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WebCluster - Motivation

= Information

<= b>Need

Query

Search engine

(within some subject domain)

WWW_SearchEngine

Domain

ØGulfs

–information need = « <= /i>query

–structured subject domain = « <= /i>unstructured target collection (WWW) <= /span>

Ø

In searching the Web, there are 2 gulfs that we are addressing:

*. The user has problems in formulating a query that perfectly reflects her information need, especially for exploratory searches, when the informati= on need is not clearly cut/defined. If unfamiliar with the domain, the user = also has problems finding the right terms for the query. Even if the user has = no problems formulating the query, the query terms he uses may not match the keywords used for indexing the documents, or may be ambiguous.=

There is, therefore, a need to assist the user in clarifying and possibly refin= ing her information need, in using the right vocabulary for expressing this n= eed, and in conveying the right precision, granularity or level of abstraction= for the need. Such assistance has traditionally been offered by librarians or search professionals. We want to investigate if a system could help simil= ar levels of support.

*. Typically, a domain or universe of discourse has a certain structure, covering different concepts and topics, having different sub-domains; this structure can support exploration through a variety of search strategies,= and learning. On the other hand, the Web has no topical structure. It is distributed, anarchic and its sheer size makes it impossible to cluster or classify it in order to impose or discover the structure of different pro= blem domains. Search engines do not help much, as they return a linear list of hits, with no structural hints. Browsing may reveal some structure, by wa= y of hyperlinks, but this is only local and limited to the domain known by the page authors. Portal offer a better alternative for exploratory searches = but building and updating them manually can be prohibitively expensive.

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=

Information

need<= /div>

1. = Select library&= #13;

2. Consult catalog

3. Browse

  &n= bsp; shelves

4. = Use inter-library scheme

Information Need

  &n= bsp; Formulation

Interaction in the library

1. In the first stage the user needs to do a minimum analysis and clarification= of the info need and to decide which library is more likely to solve the need (Computer Science, Business School, City Lib).

2. Once in the chosen library, the user has several tools at his disposal. If looking for a certain title or author, he can consult the index. Computer= ized libraries also offer keyword searches, if the user doesn’t have an exact title. The catalog offers various types of meta-information (editio= n, date, publisher, language), helping if the user needs to select from a few items. It also indicates the shelf code, so that the user can locate the physical item. If the user’s need is more general, he can consult t= he topics index, indicating the shelves that hold the topics of interest.

For complex information need, the librarian is available for suggesting a sea= rch strategy and keywords for generating queries, or for guessing the topic of interest for the user. Novice users may also need the librarian to show t= hem how to use the index or how to find their way through the physical librar= y.

3. The search doesn’t need to end with locating on the shelf the item indicated by the catalog. Possibly under the librarian’s advice, the user can follow a browsing strategy at the shelf. The physical organisation of the library mirrors a semantic, topical organisation. Therefore, once at the shelf, the user is very likely to find other books= of interest. Titles are not always good indicatives of content, so the catal= og search may not have hit the most useful item. By looking at the table of contents, the publication date, the publisher, the font, the depth of sub= ject treatment, …, the user can decide the most useful item.<= br>

During the stages 2 and 3, the analytical process that takes place for identifying the best items for a certain task or purpose has a side effec= t: it clarifies and refines the information need. This will support the known item strategy in the future, if the user remembers which items, topics or shelves have been useful.

4. If the items found in the library are insufficient, the inter-library loan system can be used. By now the information need is clarified and refined,= so a precise query can be sent out.

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=

1. Select source

  &n= bsp; collection

Information Need

  &n= bsp; Formulation

2. Explore

source collection

with ClusterBook

Resu= lts

Resu= lts

Information

     need=

3. = Search WWW

Can we simulate the library interaction ?

=

Structured

source

collections

=

1. The user is offered a set of document collections, covering various domai= ns. In principle, the structure of each collection can be obtained either by manual or automatic classification, but our work concentrates on the use = of document clustering for grouping together similar documents and revealing= the inherent structure of a collection. The user can select a collection, bas= ed on a description of the topic or domain addressed, or can be recommended a subset of collections to explore, based on a vaguely expressed information need.

2. Using ClusterBook, the user explores the so-called source collection. It attempts to offer the functionality offered by the library: It combines rank-based retrieval, similar with the catalog search, with cluster-based retrieval, similar to the librarian’s suggestion of topics.

One outcome of this exploration is a set of relevant documents. Another is a better formulation of the information need: clearer, more refined, more precise. This is recorded either explicitly, in the user’s notes, or implicitly, in the actions, and especially selections, made by the user.<= /i>

3. If not satisfied with the results of the search, the user may choose to exte= nd it to a target collection, such as the Web, for which the better formulat= ion of the information need is used.

It is significant that the characteristics of the target collection (size, heterogeneity, lack of overall structure,…) impose searching as the main or sole means of exploration. On the Web, for example, structure that affords browsing is only local and limited to manually placed hyperlinks. Clear and precise queries are essential in order to get a high number of relevant documents and a low number of non-relevant documents.=

ClusterBook/WebCluster helps the user generate good formulations of their information needs, bas= ed on the exploration of small, homogeneous, well structured source collecti= ons, and mediates access to large, heterogeneous, structure-less target collec= tions.

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The mediated access interaction

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Information

     need=

Web search engine

    WebCluster=

Query

Specialised

     source

Target collection

   (WWW)

Topical

documents

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Content-Location: file:///C:/D0A7BD72/gmMIR_files/slide0055.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" ClusterBook and the WebCluster project

Interaction model vs. prototype

ØStructuring the source collection

wDocument clustering

wSupervised classification

wManual (intellectual) classification

Ø

ØExploring the structured source collection&#= 13;

wMetaphor R= 11; Library, book, encyclopaedia

wVisual= ization tool – Folder metaphor, hyperbolic tree, themescape, cone tre= es, thematic maps

wSearch strategies supported – Best match or cluster-based searching, browsing

*) We chose document clustering because it is fully automatic and domain independent, it allows variability in choosing parameters such as the indexing method, the weighting scheme and the clustering algorithm, plus = it gave us the opportunity to conduct experiments on the cluster hypothesis. However, any classification method, manual, semi-automatic (supervised) or fully automatic (un-supervi= sed), can be employed as long as it reveals the topical structure of the source collection and thus supports the exploration of the problem domain.

*) We chose the folder metaphor for representing the structured source collecti= on in the user interface because of its simple implementation and the comput= er users' familiarity with it. We also combined this structured view of the domain with a linear view, obtained through query-based ranked searching,= in order to offer a rich set of retrieval strategies. However, alternative visualization tools such as hyperbolic trees, thematic maps, tree maps or cone tree scan be used instead and support for other search strategies ca= n be offered. The constraint is that the user interface should support concept learning and the identification of exemplary relevant documents.

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Model vs. prototype

ØInteraction model

wExpl= icit (the user ma= rks relevant documents) vs. implicit (cues on relevance are derived based on user behavior/actions)
w= Transparent (the user is aware) vs. = = opaque (the user is happy to see effect of ‘magic’) <= /span>

wAuto= matic vs. manua= l/intellectual generation of the mediated query

ØQuery model

wLanguage models (generative, Kullback-Leibler)

wProbabi= listic models

wRocchio= or other RF-specific formulae

*) We chose explicit relevance feedback because, despite the user interface complexity that it introduces and the need for user cooperation, it indic= ates the user's preferences more clearly. Implicit feedback, which relies on c= ues from the user's behavior and actions, are currently unreliable in conveyi= ng the user's interest. However,when technology that supports it matures, th= is approach will also be applicable to mediation.

Explicit – users marking relevant (exemplary) docs

Implicit – cues from user behavior / actions

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ClusterBook - Source collection

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the WebCluster project

ClusterBook - Target collection

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	In searching the Web, there are 2 gulfs that we are addressing:
	*. The user has problems in formulating a query that perfectly reflects her information need, especially for exploratory searches, when the informati= on need is not clearly cut/defined. If unfamiliar with the domain, the user = also has problems finding the right terms for the query. Even if the user has = no problems formulating the query, the query terms he uses may not match the keywords used for indexing the documents, or may be ambiguous.=
	There is, therefore, a need to assist the user in clarifying and possibly refin= ing her information need, in using the right vocabulary for expressing this n= eed, and in conveying the right precision, granularity or level of abstraction= for the need. Such assistance has traditionally been offered by librarians or search professionals. We want to investigate if a system could help simil= ar levels of support.

	*. Typically, a domain or universe of discourse has a certain structure, covering different concepts and topics, having different sub-domains; this structure can support exploration through a variety of search strategies,= and learning. On the other hand, the Web has no topical structure. It is distributed, anarchic and its sheer size makes it impossible to cluster or classify it in order to impose or discover the structure of different pro= blem domains. Search engines do not help much, as they return a linear list of hits, with no structural hints. Browsing may reveal some structure, by wa= y of hyperlinks, but this is only local and limited to the domain known by the page authors. Portal offer a better alternative for exploratory searches = but building and updating them manually can be prohibitively expensive.


	1. In the first stage the user needs to do a minimum analysis and clarification= of the info need and to decide which library is more likely to solve the need (Computer Science, Business School, City Lib).
	2. Once in the chosen library, the user has several tools at his disposal. If looking for a certain title or author, he can consult the index. Computer= ized libraries also offer keyword searches, if the user doesn’t have an exact title. The catalog offers various types of meta-information (editio= n, date, publisher, language), helping if the user needs to select from a few items. It also indicates the shelf code, so that the user can locate the physical item. If the user’s need is more general, he can consult t= he topics index, indicating the shelves that hold the topics of interest.
	For complex information need, the librarian is available for suggesting a sea= rch strategy and keywords for generating queries, or for guessing the topic of interest for the user. Novice users may also need the librarian to show t= hem how to use the index or how to find their way through the physical librar= y.
	3. The search doesn’t need to end with locating on the shelf the item indicated by the catalog. Possibly under the librarian’s advice, the user can follow a browsing strategy at the shelf. The physical organisation of the library mirrors a semantic, topical organisation. Therefore, once at the shelf, the user is very likely to find other books= of interest. Titles are not always good indicatives of content, so the catal= og search may not have hit the most useful item. By looking at the table of contents, the publication date, the publisher, the font, the depth of sub= ject treatment, …, the user can decide the most useful item.<= br>
	During the stages 2 and 3, the analytical process that takes place for identifying the best items for a certain task or purpose has a side effec= t: it clarifies and refines the information need. This will support the known item strategy in the future, if the user remembers which items, topics or shelves have been useful.
	4. If the items found in the library are insufficient, the inter-library loan system can be used. By now the information need is clarified and refined,= so a precise query can be sent out.


	1. The user is offered a set of document collections, covering various domai= ns. In principle, the structure of each collection can be obtained either by manual or automatic classification, but our work concentrates on the use = of document clustering for grouping together similar documents and revealing= the inherent structure of a collection. The user can select a collection, bas= ed on a description of the topic or domain addressed, or can be recommended a subset of collections to explore, based on a vaguely expressed information need.
	2. Using ClusterBook, the user explores the so-called source collection. It attempts to offer the functionality offered by the library: It combines rank-based retrieval, similar with the catalog search, with cluster-based retrieval, similar to the librarian’s suggestion of topics.
	One outcome of this exploration is a set of relevant documents. Another is a better formulation of the information need: clearer, more refined, more precise. This is recorded either explicitly, in the user’s notes, or implicitly, in the actions, and especially selections, made by the user.<= /i>
	3. If not satisfied with the results of the search, the user may choose to exte= nd it to a target collection, such as the Web, for which the better formulat= ion of the information need is used.
	It is significant that the characteristics of the target collection (size, heterogeneity, lack of overall structure,…) impose searching as the main or sole means of exploration. On the Web, for example, structure that affords browsing is only local and limited to manually placed hyperlinks. Clear and precise queries are essential in order to get a high number of relevant documents and a low number of non-relevant documents.=
	ClusterBook/WebCluster helps the user generate good formulations of their information needs, bas= ed on the exploration of small, homogeneous, well structured source collecti= ons, and mediates access to large, heterogeneous, structure-less target collec= tions.


	*) We chose document clustering because it is fully automatic and domain independent, it allows variability in choosing parameters such as the indexing method, the weighting scheme and the clustering algorithm, plus = it gave us the opportunity to conduct experiments on the cluster hypothesis. However, any classification method, manual, semi-automatic (supervised) or fully automatic (un-supervi= sed), can be employed as long as it reveals the topical structure of the source collection and thus supports the exploration of the problem domain.

	*) We chose the folder metaphor for representing the structured source collecti= on in the user interface because of its simple implementation and the comput= er users' familiarity with it. We also combined this structured view of the domain with a linear view, obtained through query-based ranked searching,= in order to offer a rich set of retrieval strategies. However, alternative visualization tools such as hyperbolic trees, thematic maps, tree maps or cone tree scan be used instead and support for other search strategies ca= n be offered. The constraint is that the user interface should support concept learning and the identification of exemplary relevant documents.


	*) We chose explicit relevance feedback because, despite the user interface complexity that it introduces and the need for user cooperation, it indic= ates the user's preferences more clearly. Implicit feedback, which relies on c= ues from the user's behavior and actions, are currently unreliable in conveyi= ng the user's interest. However,when technology that supports it matures, th= is approach will also be applicable to mediation.
	Explicit – users marking relevant (exemplary) docs
	Implicit – cues from user behavior / actions