posted by user: grupocole || 5545 views || tracked by 17 users: [display]

JIR-FRRA 2009 : Journal of Information Retrieval Special Issue on FOCUSED RETRIEVAL AND RESULT AGGREGATION


When N/A
Where N/A
Submission Deadline May 1, 2009
Notification Due Jul 1, 2009
Categories    information retrieval

Call For Papers

Call for Papers
Journal of Information Retrieval Special Issue on

Andrew Trotman (
University of Otago, Dunedin, New Zealand

Mounia Lalmas (
University of Glasgow, Glasgow, UK

Standard document retrieval finds atomic documents, and leaves to the user
the task of locating relevant information within a document. Focused
retrieval addresses this latter task by providing the user a more direct
access to relevant information. Focused retrieval aims to identify not only
documents relevant to a user information need, but also where within the
document the relevant information is located. It aims to satisfy the user
information need and not to just identify documents that satisfy the
information need.
There are three main forms of focused retrieval: element retrieval, passage
retrieval, and question answering. Element Retrieval (also known as XML-IR)
can be applied when the documents in the collection contain some kind of
markup (such as XML). The retrieval engine will typically exploit the
structure to identify the most relevant paragraphs, sections or documents to
return as answers to a query. With passage retrieval, the retrieval engine
will typically choose the appropriate size of results to return and the
location based mostly on the content of the document (and sometimes its
structure). Whereas element retrieval and passage retrieval are used for
information seeking questions, question answering (QA) aims to answer more
fact seeking questions, and makes use of natural language processing

Question answering has been investigated in TREC, CLEF and NCTIR for many
years, and since 2008 in INEX, and is arguably the ultimate goal of semantic
web search research for interrogative information needs. Passage retrieval
has a long history in information retrieval research, including INEX and the
TREC genomics track, but is also important when searching long documents of
any kind. Element retrieval has been the core task at INEX, and is now being
investigated in the INEX book search track.
The aim of element retrieval (XML-IR) is to identify the most relevant
document components to return as an answer to the query. It has already been
shown that returning several elements together as one answer triggers a
stronger user satisfaction than returning a single element on its own. In
the relevance in context retrieval task at INEX, the aim is to return
documents constructed from their most relevant elements; the elements are
aggregated to form one result. More generally, elements or passages from
different documents might be selected to form an aggregated result. In
Yahoo! Alpha and Google Universal a query already yields results from a
variety of different sources including: images, videos, news, and sponsored
results; all aggregated into a single results page.

Result aggregation is a form of automatic document construction. Given a set
of documents and document components that satisfy a user's information need
(perhaps identified using focused retrieval), an aggregator will combine
these into a single result. Techniques include multiple-component
summarization, meta-search like result presentation, and mixed-media
presentation (when searching over heterogeneous collections of, for example,
text, images, video, and music).
This special issue on Focused Retrieval and Result Aggregation intentionally
covers two topics, but in particular we are interested in examining the
entire search process from user query through to aggregated document
presentation. The aim of this special issue is to present the current
state-of-the-art and the most recent developments in focused retrieval,
result aggregation, and their relationship. It also aims to offer a
thoughtful perspective of the potential and emerging challenges of these two

Prospective authors who aim to contribute to this special issue are
encouraged to submit original and unpublished papers dealing with Focused
Retrieval, Result Aggregation, or the combination of the two. Papers are
solicited in any of (but not limited to) the following areas:

*Algorithmic approaches to focused retrieval and/or result aggregation
*Relationship between focused retrieval and result aggregation
*The effect of media, language and context
*Interface and presentation issues
*Evaluation, e.g. effectiveness, user-centered
*Applications, e.g. web search, mobile search, wiki search, wiki linking,
*Use cases, e.g. education, law, travel, etc

Papers due: 1 May 2009
Review and revision completed: 1 July 2009
Camera ready paper due: 1 September 2009

The guidelines for authors and reviewers are available for download from the
INRT webpage:

Submissions can be uploaded via and
should be indicated for consideration in the Special Issue on Focused
Retrieval and Result Aggregation"

In case you encounter any difficulties while submitting your manuscript
online, please get in touch with the responsible Editorial Assistant by
clicking on "CONTACT US" in the EditorialManager start page.

Authors are encouraged to contact the guest editors with any questions

Related Resources

ECIR 2023   45th European Conference on Information Retrieval
NLTM 2023   3rd International Conference on NLP & Text Mining
ACM-ISIR 2022   2022 3rd International Conference on Information Security and Information Retrieval (ISIR 2022)
AIAA 2022   12th International Conference on Artificial Intelligence, Soft Computing and Applications
AI & FL 2022   10th International Conference of Artificial Intelligence and Fuzzy Logic
SOEA 2023   7th International Conference on Software Engineering and Applications
ITCON 2022   International Conference on Information Technology Converge Services
NLAI 2022   3rd International Conference on NLP & Artificial Intelligence Techniques
NIAI 2023   4th International Conference on Natural Language Processing, Information Retrieval and AI