40,936 research outputs found
Using Search Engine Technology to Improve Library Catalogs
This chapter outlines how search engine technology can be used in online public access library
catalogs (OPACs) to help improve users’ experiences, to identify users’ intentions, and to indicate
how it can be applied in the library context, along with how sophisticated ranking criteria can be
applied to the online library catalog. A review of the literature and current OPAC developments
form the basis of recommendations on how to improve OPACs. Findings were that the major
shortcomings of current OPACs are that they are not sufficiently user-centered and that their results
presentations lack sophistication. Further, these shortcomings are not addressed in current 2.0
developments. It is argued that OPAC development should be made search-centered before
additional features are applied. While the recommendations on ranking functionality and the use of
user intentions are only conceptual and not yet applied to a library catalogue, practitioners will find
recommendations for developing better OPACs in this chapter. In short, readers will find a
systematic view on how the search engines’ strengths can be applied to improving libraries’ online
catalogs
Keyword Search on RDF Graphs - A Query Graph Assembly Approach
Keyword search provides ordinary users an easy-to-use interface for querying
RDF data. Given the input keywords, in this paper, we study how to assemble a
query graph that is to represent user's query intention accurately and
efficiently. Based on the input keywords, we first obtain the elementary query
graph building blocks, such as entity/class vertices and predicate edges. Then,
we formally define the query graph assembly (QGA) problem. Unfortunately, we
prove theoretically that QGA is a NP-complete problem. In order to solve that,
we design some heuristic lower bounds and propose a bipartite graph
matching-based best-first search algorithm. The algorithm's time complexity is
, where is the number of the keywords and is a
tunable parameter, i.e., the maximum number of candidate entity/class vertices
and predicate edges allowed to match each keyword. Although QGA is intractable,
both and are small in practice. Furthermore, the algorithm's time
complexity does not depend on the RDF graph size, which guarantees the good
scalability of our system in large RDF graphs. Experiments on DBpedia and
Freebase confirm the superiority of our system on both effectiveness and
efficiency
Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes
In this paper we present an evaluation of techniques that are designed to encourage web searchers to interact more with the results of a web search. Two specific techniques are examined: the presentation of sentences that highly match the searcher's query and the use of implicit evidence. Implicit evidence is evidence captured from the searcher's interaction with the retrieval results and is used to automatically update the display. Our evaluation concentrates on the effectiveness and subject perception of these techniques. The results show, with statistical significance, that the techniques are effective and efficient for information seeking
The Influence of Commercial Intent of Search Results on Their Perceived Relevance
We carried out a retrieval effectiveness test on the three major web search engines (i.e., Google, Microsoft and Yahoo). In addition to relevance judgments, we classified the results according to their commercial intent and whether or not they carried any advertising. We found that all search engines provide a large number of results with a commercial intent. Google provides significantly more commercial results than the other search engines do. However, the commercial intent of a result did not influence jurors in their relevance judgments
Understanding Mobile Search Task Relevance and User Behaviour in Context
Improvements in mobile technologies have led to a dramatic change in how and
when people access and use information, and is having a profound impact on how
users address their daily information needs. Smart phones are rapidly becoming
our main method of accessing information and are frequently used to perform
`on-the-go' search tasks. As research into information retrieval continues to
evolve, evaluating search behaviour in context is relatively new. Previous
research has studied the effects of context through either self-reported diary
studies or quantitative log analysis; however, neither approach is able to
accurately capture context of use at the time of searching. In this study, we
aim to gain a better understanding of task relevance and search behaviour via a
task-based user study (n=31) employing a bespoke Android app. The app allowed
us to accurately capture the user's context when completing tasks at different
times of the day over the period of a week. Through analysis of the collected
data, we gain a better understanding of how using smart phones on the go
impacts search behaviour, search performance and task relevance and whether or
not the actual context is an important factor.Comment: To appear in CHIIR 2019 in Glasgow, U
Behavioural Evidence Analysis Applied to Digital Forensics: An Empirical Analysis of Child Pornography Cases using P2P Networks
The utility of Behavioural Evidence Analysis (BEA) has gained attention in the field of Digital Forensics in recent years. It has been recognized that, along with technical examination of digital evidence, it is important to learn as much as possible about the individuals behind an offence, the victim(s) and the dynamics of a crime. This can assist the investigator in producing a more accurate and complete reconstruction of the crime, in interpreting associated digital evidence, and with the description of investigative findings. Despite these potential benefits, the literature shows limited use of BEA for the investigation of cases of the possession and dissemination of Sexually Exploitative Imagery of Children (SEIC). This paper represents a step towards filling this gap. It reports on the forensic analysis of 15 SEIC cases involving P2P filesharing networks, obtained from the Dubai Police. Results confirmed the predicted benefits and indicate that BEA can assist digital forensic practitioners and prosecutors
Why People Search for Images using Web Search Engines
What are the intents or goals behind human interactions with image search
engines? Knowing why people search for images is of major concern to Web image
search engines because user satisfaction may vary as intent varies. Previous
analyses of image search behavior have mostly been query-based, focusing on
what images people search for, rather than intent-based, that is, why people
search for images. To date, there is no thorough investigation of how different
image search intents affect users' search behavior.
In this paper, we address the following questions: (1)Why do people search
for images in text-based Web image search systems? (2)How does image search
behavior change with user intent? (3)Can we predict user intent effectively
from interactions during the early stages of a search session? To this end, we
conduct both a lab-based user study and a commercial search log analysis.
We show that user intents in image search can be grouped into three classes:
Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals
different user behavior patterns under these three intents, such as first click
time, query reformulation, dwell time and mouse movement on the result page.
Based on user interaction features during the early stages of an image search
session, that is, before mouse scroll, we develop an intent classifier that is
able to achieve promising results for classifying intents into our three intent
classes. Given that all features can be obtained online and unobtrusively, the
predicted intents can provide guidance for choosing ranking methods immediately
after scrolling
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