2,142 research outputs found
Multimedia search without visual analysis: the value of linguistic and contextual information
This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features
Agents in Bioinformatics
The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarise and reflect on the presentations and discussions
Ranking for Web Data Search Using On-The-Fly Data Integration
Ranking - the algorithmic decision on how relevant an information artifact is for a given information need and the sorting of artifacts by their concluded relevancy - is an integral part of every search engine. In this book we investigate how structured Web data can be leveraged for ranking with the goal to improve the effectiveness of search. We propose new solutions for ranking using on-the-fly data integration and experimentally analyze and evaluate them against the latest baselines
EntiTables: Smart Assistance for Entity-Focused Tables
Tables are among the most powerful and practical tools for organizing and
working with data. Our motivation is to equip spreadsheet programs with smart
assistance capabilities. We concentrate on one particular family of tables,
namely, tables with an entity focus. We introduce and focus on two specific
tasks: populating rows with additional instances (entities) and populating
columns with new headings. We develop generative probabilistic models for both
tasks. For estimating the components of these models, we consider a knowledge
base as well as a large table corpus. Our experimental evaluation simulates the
various stages of the user entering content into an actual table. A detailed
analysis of the results shows that the models' components are complimentary and
that our methods outperform existing approaches from the literature.Comment: Proceedings of the 40th International ACM SIGIR Conference on
Research and Development in Information Retrieval (SIGIR '17), 201
A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web
Over the past decade, rapid advances in web technologies, coupled with
innovative models of spatial data collection and consumption, have generated a
robust growth in geo-referenced information, resulting in spatial information
overload. Increasing 'geographic intelligence' in traditional text-based
information retrieval has become a prominent approach to respond to this issue
and to fulfill users' spatial information needs. Numerous efforts in the
Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the
Linking Open Data initiative have converged in a constellation of open
knowledge bases, freely available online. In this article, we survey these open
knowledge bases, focusing on their geospatial dimension. Particular attention
is devoted to the crucial issue of the quality of geo-knowledge bases, as well
as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic
Network, is outlined as our contribution to this area. Research directions in
information integration and Geographic Information Retrieval (GIR) are then
reviewed, with a critical discussion of their current limitations and future
prospects
Collaborating with the Crowd for Software Requirements Engineering: A Literature Review
Requirements engineering (RE) represents a decisive success factor in software development. The novel approach of crowd-based RE seeks to overcome shortcomings of traditional RE practices such as the resource intensiveness and selection bias of stakeholder workshops or interviews. Two streams of research on crowd-based RE can be observed in literature: data-driven approaches that extract requirements from user feedback or analytics data and collaborative approaches in which requirements are collectively developed by a crowd of software users. As yet, research surveying the state of crowd-based RE does not put particular emphasis on collaborative approaches, despite collaborative crowdsourcing being particularly suited for joint ideation and complex problem-solving tasks. Addressing this gap, we conduct a structured literature review to identify the RE activities supported by collaborative crowd-based approaches. Our research provides a systematic overview of the domain of collaborative crowd-based RE and guides researchers and practitioners in increasing user involvement in RE
The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing: Extended Survey
Graph processing is becoming increasingly prevalent across many application
domains. In spite of this prevalence, there is little research about how graphs
are actually used in practice. We performed an extensive study that consisted
of an online survey of 89 users, a review of the mailing lists, source
repositories, and whitepapers of a large suite of graph software products, and
in-person interviews with 6 users and 2 developers of these products. Our
online survey aimed at understanding: (i) the types of graphs users have; (ii)
the graph computations users run; (iii) the types of graph software users use;
and (iv) the major challenges users face when processing their graphs. We
describe the participants' responses to our questions highlighting common
patterns and challenges. Based on our interviews and survey of the rest of our
sources, we were able to answer some new questions that were raised by
participants' responses to our online survey and understand the specific
applications that use graph data and software. Our study revealed surprising
facts about graph processing in practice. In particular, real-world graphs
represent a very diverse range of entities and are often very large,
scalability and visualization are undeniably the most pressing challenges faced
by participants, and data integration, recommendations, and fraud detection are
very popular applications supported by existing graph software. We hope these
findings can guide future research
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