4,845 research outputs found
Concept-based Interactive Query Expansion Support Tool (CIQUEST)
This report describes a three-year project (2000-03) undertaken in the Information Studies
Department at The University of Sheffield and funded by Resource, The Council for
Museums, Archives and Libraries. The overall aim of the research was to provide user
support for query formulation and reformulation in searching large-scale textual resources
including those of the World Wide Web. More specifically the objectives were: to investigate
and evaluate methods for the automatic generation and organisation of concepts derived from
retrieved document sets, based on statistical methods for term weighting; and to conduct
user-based evaluations on the understanding, presentation and retrieval effectiveness of
concept structures in selecting candidate terms for interactive query expansion.
The TREC test collection formed the basis for the seven evaluative experiments conducted in
the course of the project. These formed four distinct phases in the project plan. In the first
phase, a series of experiments was conducted to investigate further techniques for concept
derivation and hierarchical organisation and structure. The second phase was concerned with
user-based validation of the concept structures. Results of phases 1 and 2 informed on the
design of the test system and the user interface was developed in phase 3. The final phase
entailed a user-based summative evaluation of the CiQuest system.
The main findings demonstrate that concept hierarchies can effectively be generated from
sets of retrieved documents and displayed to searchers in a meaningful way. The approach
provides the searcher with an overview of the contents of the retrieved documents, which in
turn facilitates the viewing of documents and selection of the most relevant ones. Concept
hierarchies are a good source of terms for query expansion and can improve precision. The
extraction of descriptive phrases as an alternative source of terms was also effective. With
respect to presentation, cascading menus were easy to browse for selecting terms and for
viewing documents. In conclusion the project dissemination programme and future work are
outlined
Entity Query Feature Expansion Using Knowledge Base Links
Recent advances in automatic entity linking and knowledge base
construction have resulted in entity annotations for document and
query collections. For example, annotations of entities from large
general purpose knowledge bases, such as Freebase and the Google
Knowledge Graph. Understanding how to leverage these entity
annotations of text to improve ad hoc document retrieval is an open
research area. Query expansion is a commonly used technique to
improve retrieval effectiveness. Most previous query expansion
approaches focus on text, mainly using unigram concepts. In this
paper, we propose a new technique, called entity query feature
expansion (EQFE) which enriches the query with features from
entities and their links to knowledge bases, including structured
attributes and text. We experiment using both explicit query entity
annotations and latent entities. We evaluate our technique on TREC
text collections automatically annotated with knowledge base entity
links, including the Google Freebase Annotations (FACC1) data.
We find that entity-based feature expansion results in significant
improvements in retrieval effectiveness over state-of-the-art text
expansion approaches
Scientific Table Search Using Keyword Queries
Tables are common and important in scientific documents, yet most text-based
document search systems do not capture structures and semantics specific to
tables. How to bridge different types of mismatch between keywords queries and
scientific tables and what influences ranking quality needs to be carefully
investigated. This paper considers the structure of tables and gives different
emphasis to table components. On the query side, thanks to external knowledge
such as knowledge bases and ontologies, key concepts are extracted and used to
build structured queries, and target quantity types are identified and used to
expand original queries. A probabilistic framework is proposed to incorporate
structural and semantic information from both query and table sides. We also
construct and release TableArXiv, a high quality dataset with 105 queries and
corresponding relevance judgements for scientific table search. Experiments
demonstrate significantly higher accuracy overall and at the top of the
rankings than several baseline methods
Revisiting Iterative Relevance Feedback for Document and Passage Retrieval
As more and more search traffic comes from mobile phones, intelligent
assistants, and smart-home devices, new challenges (e.g., limited presentation
space) and opportunities come up in information retrieval. Previously, an
effective technique, relevance feedback (RF), has rarely been used in real
search scenarios due to the overhead of collecting users' relevance judgments.
However, since users tend to interact more with the search results shown on the
new interfaces, it becomes feasible to obtain users' assessments on a few
results during each interaction. This makes iterative relevance feedback (IRF)
techniques look promising today. IRF has not been studied systematically in the
new search scenarios and its effectiveness is mostly unknown. In this paper, we
re-visit IRF and extend it with RF models proposed in recent years. We conduct
extensive experiments to analyze and compare IRF with the standard top-k RF
framework on document and passage retrieval. Experimental results show that IRF
is at least as effective as the standard top-k RF framework for documents and
much more effective for passages. This indicates that IRF for passage retrieval
has huge potential
Graph-Embedding Empowered Entity Retrieval
In this research, we improve upon the current state of the art in entity
retrieval by re-ranking the result list using graph embeddings. The paper shows
that graph embeddings are useful for entity-oriented search tasks. We
demonstrate empirically that encoding information from the knowledge graph into
(graph) embeddings contributes to a higher increase in effectiveness of entity
retrieval results than using plain word embeddings. We analyze the impact of
the accuracy of the entity linker on the overall retrieval effectiveness. Our
analysis further deploys the cluster hypothesis to explain the observed
advantages of graph embeddings over the more widely used word embeddings, for
user tasks involving ranking entities
Cross-concordances: terminology mapping and its effectiveness for information retrieval
The German Federal Ministry for Education and Research funded a major
terminology mapping initiative, which found its conclusion in 2007. The task of
this terminology mapping initiative was to organize, create and manage
'cross-concordances' between controlled vocabularies (thesauri, classification
systems, subject heading lists) centred around the social sciences but quickly
extending to other subject areas. 64 crosswalks with more than 500,000
relations were established. In the final phase of the project, a major
evaluation effort to test and measure the effectiveness of the vocabulary
mappings in an information system environment was conducted. The paper reports
on the cross-concordance work and evaluation results.Comment: 19 pages, 4 figures, 11 tables, IFLA conference 200
Query Expansion with Locally-Trained Word Embeddings
Continuous space word embeddings have received a great deal of attention in
the natural language processing and machine learning communities for their
ability to model term similarity and other relationships. We study the use of
term relatedness in the context of query expansion for ad hoc information
retrieval. We demonstrate that word embeddings such as word2vec and GloVe, when
trained globally, underperform corpus and query specific embeddings for
retrieval tasks. These results suggest that other tasks benefiting from global
embeddings may also benefit from local embeddings
Contextualised Browsing in a Digital Library's Living Lab
Contextualisation has proven to be effective in tailoring \linebreak search
results towards the users' information need. While this is true for a basic
query search, the usage of contextual session information during exploratory
search especially on the level of browsing has so far been underexposed in
research. In this paper, we present two approaches that contextualise browsing
on the level of structured metadata in a Digital Library (DL), (1) one variant
bases on document similarity and (2) one variant utilises implicit session
information, such as queries and different document metadata encountered during
the session of a users. We evaluate our approaches in a living lab environment
using a DL in the social sciences and compare our contextualisation approaches
against a non-contextualised approach. For a period of more than three months
we analysed 47,444 unique retrieval sessions that contain search activities on
the level of browsing. Our results show that a contextualisation of browsing
significantly outperforms our baseline in terms of the position of the first
clicked item in the result set. The mean rank of the first clicked document
(measured as mean first relevant - MFR) was 4.52 using a non-contextualised
ranking compared to 3.04 when re-ranking the result lists based on similarity
to the previously viewed document. Furthermore, we observed that both
contextual approaches show a noticeably higher click-through rate. A
contextualisation based on document similarity leads to almost twice as many
document views compared to the non-contextualised ranking.Comment: 10 pages, 2 figures, paper accepted at JCDL 201
Overview of the ImageCLEFphoto 2008 photographic retrieval task
ImageCLEFphoto 2008 is an ad-hoc photo retrieval task and part of the ImageCLEF
evaluation campaign. This task provides both the resources and the framework
necessary to perform comparative laboratory-style evaluation of visual information
retrieval systems. In 2008, the evaluation task concentrated on promoting diversity
within the top 20 results from a multilingual image collection. This new challenge
attracted a record number of submissions: a total of 24 participating groups
submitting 1,042 system runs. Some of the findings include that the choice of
annotation language is almost negligible and the best runs are by combining concept
and content-based retrieval methods
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