9,982 research outputs found
NITELIGHT: A Graphical Tool for Semantic Query Construction
Query formulation is a key aspect of information retrieval, contributing to both the efficiency and usability of many semantic applications. A number of query languages, such as SPARQL, have been developed for the Semantic Web; however, there are, as yet, few tools to support end users with respect to the creation and editing of semantic queries. In this paper we introduce a graphical tool for semantic query construction (NITELIGHT) that is based on the SPARQL query language specification. The tool supports end users by providing a set of graphical notations that represent semantic query language constructs. This language provides a visual query language counterpart to SPARQL that we call vSPARQL. NITELIGHT also provides an interactive graphical editing environment that combines ontology navigation capabilities with graphical query visualization techniques. This paper describes the functionality and user interaction features of the NITELIGHT tool based on our work to date. We also present details of the vSPARQL constructs used to support the graphical representation of SPARQL queries
Social Search: retrieving information in Online Social Platforms -- A Survey
Social Search research deals with studying methodologies exploiting social
information to better satisfy user information needs in Online Social Media
while simplifying the search effort and consequently reducing the time spent
and the computational resources utilized. Starting from previous studies, in
this work, we analyze the current state of the art of the Social Search area,
proposing a new taxonomy and highlighting current limitations and open research
directions. We divide the Social Search area into three subcategories, where
the social aspect plays a pivotal role: Social Question&Answering, Social
Content Search, and Social Collaborative Search. For each subcategory, we
present the key concepts and selected representative approaches in the
literature in greater detail. We found that, up to now, a large body of studies
model users' preferences and their relations by simply combining social
features made available by social platforms. It paves the way for significant
research to exploit more structured information about users' social profiles
and behaviors (as they can be inferred from data available on social platforms)
to optimize their information needs further
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Understanding and exploiting user intent in community question answering
A number of Community Question Answering (CQA) services have emerged
and proliferated in the last decade. Typical examples include Yahoo! Answers,
WikiAnswers, and also domain-specific forums like StackOverflow. These services
help users obtain information from a community - a user can post his or her questions which may then be answered by other users. Such a paradigm of information seeking is particularly appealing when the question cannot be answered directly by Web search engines due to the unavailability of relevant online content. However, question submitted to a CQA service are often colloquial and ambiguous. An accurate understanding of the intent behind a question is important for satisfying the user's information need more effectively and efficiently.
In this thesis, we analyse the intent of each question in CQA by classifying
it into five dimensions, namely: subjectivity, locality, navigationality, procedurality,
and causality. By making use of advanced machine learning techniques, such
as Co-Training and PU-Learning, we are able to attain consistent and significant
classification improvements over the state-of-the-art in this area. In addition to
the textual features, a variety of metadata features (such as the category where
the question was posted to) are used to model a user's intent, which in turn help
the CQA service to perform better in finding similar questions, identifying relevant
answers, and recommending the most relevant answerers.
We validate the usefulness of user intent in two different CQA tasks. Our
first application is question retrieval, where we present a hybrid approach which
blends several language modelling techniques, namely, the classic (query-likelihood)
language model, the state-of-the-art translation-based language model, and our
proposed intent-based language model. Our second application is answer validation, where we present a two-stage model which first ranks similar questions by using
our proposed hybrid approach, and then validates whether the answer of the top
candidate can be served as an answer to a new question by leveraging sentiment
analysis, query quality assessment, and search lists validation
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Response Retrieval in Information-seeking Conversations
The increasing popularity of mobile Internet has led to several crucial changes in the way that people use search engines compared with traditional Web search on desktops. On one hand, there is limited output bandwidth with the small screen sizes of most mobile devices. Mobile Internet users prefer direct answers on the search engine result page (SERP). On the other hand, voice-based / text-based conversational interfaces are becoming increasing popular as shown in the wide adoption of intelligent assistant services and devices such as Amazon Echo, Microsoft Cortana and Google Assistant around the world. These important changes have triggered several new challenges that search engines have had to adapt to in order to better satisfy the information needs of mobile Internet users. In this dissertation, we investigate several aspects of single-turn answer retrieval and multi-turn information-seeking conversations to handle the new challenges of search on the mobile Internet.
We start from the research on single-turn answer retrieval and analyze the weaknesses of existing deep learning architectures for answer ranking. Then we propose an attention based neural matching model with a value-shared weighting scheme and attention mechanism to improve existing deep neural answer ranking models. Our proposed model achieves state-of-the-art performance for answer sentence retrieval compared with both feature engineering based methods and other neural models.
Then we move on to study response retrieval in multi-turn information-seeking conversations beyond single-turn interactions. Much research on response selection in conversation systems is modeling the matching patterns between user input message (either with context or not) and response candidates, which ignores external knowledge beyond the dialog utterances. We propose a learning framework on top of deep neural matching networks that leverages external knowledge with pseudo-relevance feedback and QA correspondence knowledge distillation for response retrieval. We also study how to integrate user intent modeling into neural ranking models to improve response retrieval performance. Finally, hybrid models of response retrieval and generation are investigated in order to combine the merits of these two different paradigms of conversation models.
Our goal is to develop effective learning models for answer retrieval and information-seeking conversations, in order to improve the effectiveness and user experience when accessing information with a touch screen interface or a conversational interface, as commonly adopted by millions of mobile Internet devices
LDA-based Term Profiles for Expert Finding in a Political Setting
A common task in many political institutions (i.e. Parliament) is to find
politicians who are experts in a particular field. In order to tackle this
problem, the first step is to obtain politician profiles which include their
interests, and these can be automatically learned from their speeches. As a
politician may have various areas of expertise, one alternative is to use a set
of subprofiles, each of which covers a different subject. In this study, we
propose a novel approach for this task by using latent Dirichlet allocation
(LDA) to determine the main underlying topics of each political speech, and to
distribute the related terms among the different topic-based subprofiles. With
this objective, we propose the use of fifteen distance and similarity measures
to automatically determine the optimal number of topics discussed in a
document, and to demonstrate that every measure converges into five strategies:
Euclidean, Dice, Sorensen, Cosine and Overlap. Our experimental results showed
that the scores of the different accuracy metrics of the proposed strategies
tended to be higher than those of the baselines for expert recommendation
tasks, and that the use of an appropriate number of topics has proved relevant
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