Exploratory Search: From Finding to Understanding - Leitura de Artigo

Gary Marchionini. 2006. Exploratory search: from finding to understanding. Commun. ACM  49, 4 (April 2006), 41–46. https://doi.org/10.1145/1121949.1121979

 

This article distinguishes exploratory search that blends querying and browsing strategies from retrieval that is best served by analytical strategies ...

 

Exploratory search. Search is a fundamental life activity.

 

A hierarchy of information needs may also be defined that ranges from basic facts that guide short-term actions (for example, the predicted chance for rain today to decide whether to bring an umbrella) to networks of related concepts that help us understand phenomena or execute complex activities (for example, the relationships between bond prices and stock prices to manage a retirement portfolio) to complex networks of tacit and explicit knowledge that accretes as expertise over a lifetime (for example, the most promising paths of investigation for the seasoned scholar or designer). 

 

For these respective layers of information needs, we can define kinds of information-seeking activities, each with associated strategies and tactics that might be supported with computational tools.  

 

 

These activities are represented as overlapping clouds because people may engage in multiple kinds of search in parallel, and some activities may be embedded in others ...

 

Lookup is the most basic kind of sear ch task and has been the focus of development for database management systems and much of what Web search engines support. Lookup tasks return discrete and well-structured objects such as numbers, names, short statements, or specific files of text or other media. 

 

Database management systems support fast and accurate data lookups in business and industry; in journalism, lookups are related to questions of who, when, and where as opposed to what, how, and why questions. In libraries, lookups have been called “known item” searches to distinguish them from subject or topical searches.

 

Most people think of lookup searches as “fact retrieval” or “question answering.”

 

Clearly, lookup tasks have been among the most successful applications of computers and remain an active area of research and development.

 

[Lookup em Data Retrieval, Information Retrieval e Question Answering] 

Learning searches involve multiple iterations and return sets of objects that require cognitive processing and interpretation. Note that “learning” here is used in its general sense of developing new knowledge and thus includes self-directed life-long learning and professional learning as well as the usual directed learning in schools.

 

Much of the search time in learning search tasks is devoted to examining and comparing results and reformulating queries to discover the boundaries of meaning for key concepts.

 

[Esforço humano em análise e interpretação. Seria possível reaproveitar através do histórico de buscas? FAQ]

[Consultar e navegar, avaliar o resultado, refinar a consulta] 

[Exemplo: Realizar uma Revisão Sistemática sobre um potencial problema de pesquisa]

 

Investigative searching may be done to support planning and forecasting, or to transform existing data into new data or knowledge. In addition to finding new information, investigative searches may seek to discover gaps in knowledge (for example, “negative search” [1]) so that new research can begin or dead-end alleys can be avoided.

 

[Identificar o que não se sabe, o que o KB não contempla ou que ainda não foi reconhecido como conhecimento] 

Serendipitous browsing that is done to stimulate analogical thinking is another kind of investigative search. Investigative searching is more concerned with recall  (maximizing the number of possibly relevant objects that are retrieved) than precision (minimizing the number of possibly irrelevant objects that are retrieved) and thus not well supported by today’s Web search engines.

 

[Cobertura apresenta mais alternativas, mais respostas possíveis MAS pode sobrecarregar o usuário com informações para analisar] 

In the database realm, query-by-example (QBE) interfaces were early alternatives to formal language interfaces and QBE-like systems remain the primary method for supporting non-textual queries in multimedia systems. 

 

[QBE x Modelagem de consultas em linguagem do KB em relação a perguntas em NL] 

There is also substantial evidence in the IR literature that relevance feedback—asking information seekers to make relevance judgments about returned objects and then executing a revised query based on those judgments—is a powerful way to improve retrieval. However, practice shows that people are often unwilling to take the added step to provide feedback when the search paradigm is the classic turntaking model. T o engage people more fully in the search process and put them in continuous control, researchers are devising highly interactive user interfaces.   

   

[Dificuldade de obter o feedback do usuário]