17,393 research outputs found
The Hidden Web, XML and Semantic Web: A Scientific Data Management Perspective
The World Wide Web no longer consists just of HTML pages. Our work sheds
light on a number of trends on the Internet that go beyond simple Web pages.
The hidden Web provides a wealth of data in semi-structured form, accessible
through Web forms and Web services. These services, as well as numerous other
applications on the Web, commonly use XML, the eXtensible Markup Language. XML
has become the lingua franca of the Internet that allows customized markups to
be defined for specific domains. On top of XML, the Semantic Web grows as a
common structured data source. In this work, we first explain each of these
developments in detail. Using real-world examples from scientific domains of
great interest today, we then demonstrate how these new developments can assist
the managing, harvesting, and organization of data on the Web. On the way, we
also illustrate the current research avenues in these domains. We believe that
this effort would help bridge multiple database tracks, thereby attracting
researchers with a view to extend database technology.Comment: EDBT - Tutorial (2011
A User-Centered Concept Mining System for Query and Document Understanding at Tencent
Concepts embody the knowledge of the world and facilitate the cognitive
processes of human beings. Mining concepts from web documents and constructing
the corresponding taxonomy are core research problems in text understanding and
support many downstream tasks such as query analysis, knowledge base
construction, recommendation, and search. However, we argue that most prior
studies extract formal and overly general concepts from Wikipedia or static web
pages, which are not representing the user perspective. In this paper, we
describe our experience of implementing and deploying ConcepT in Tencent QQ
Browser. It discovers user-centered concepts at the right granularity
conforming to user interests, by mining a large amount of user queries and
interactive search click logs. The extracted concepts have the proper
granularity, are consistent with user language styles and are dynamically
updated. We further present our techniques to tag documents with user-centered
concepts and to construct a topic-concept-instance taxonomy, which has helped
to improve search as well as news feeds recommendation in Tencent QQ Browser.
We performed extensive offline evaluation to demonstrate that our approach
could extract concepts of higher quality compared to several other existing
methods. Our system has been deployed in Tencent QQ Browser. Results from
online A/B testing involving a large number of real users suggest that the
Impression Efficiency of feeds users increased by 6.01% after incorporating the
user-centered concepts into the recommendation framework of Tencent QQ Browser.Comment: Accepted by KDD 201
Population Density-based Hospital Recommendation with Mobile LBS Big Data
The difficulty of getting medical treatment is one of major livelihood issues
in China. Since patients lack prior knowledge about the spatial distribution
and the capacity of hospitals, some hospitals have abnormally high or sporadic
population densities. This paper presents a new model for estimating the
spatiotemporal population density in each hospital based on location-based
service (LBS) big data, which would be beneficial to guiding and dispersing
outpatients. To improve the estimation accuracy, several approaches are
proposed to denoise the LBS data and classify people by detecting their various
behaviors. In addition, a long short-term memory (LSTM) based deep learning is
presented to predict the trend of population density. By using Baidu
large-scale LBS logs database, we apply the proposed model to 113 hospitals in
Beijing, P. R. China, and constructed an online hospital recommendation system
which can provide users with a hospital rank list basing the real-time
population density information and the hospitals' basic information such as
hospitals' levels and their distances. We also mine several interesting
patterns from these LBS logs by using our proposed system
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