1,136 research outputs found
Semantic Interaction in Web-based Retrieval Systems : Adopting Semantic Web Technologies and Social Networking Paradigms for Interacting with Semi-structured Web Data
Existing web retrieval models for exploration and interaction with web data do not take into account semantic information, nor do they allow for new forms of interaction by employing meaningful interaction and navigation metaphors in 2D/3D. This thesis researches means for introducing a semantic dimension into the search and exploration process of web content to enable a significantly positive user experience. Therefore, an inherently dynamic view beyond single concepts and models from semantic information processing, information extraction and human-machine interaction is adopted. Essential tasks for semantic interaction such as semantic annotation, semantic mediation and semantic human-computer interaction were identified and elaborated for two general application scenarios in web retrieval: Web-based Question Answering in a knowledge-based dialogue system and semantic exploration of information spaces in 2D/3D
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Building conceptual spaces for exploring and linking biomedical resources
The establishment of links between data (e.g., patient records) and Web resources (e.g., literature) and the proper visualization of such discovered knowledge is still a challenge in most Life Science domains (e.g., biomedicine). In this paper we present our contribution to the community in the form of an infrastructure to annotate information resources, to discover relationships among them, and to represent and visualize the new discovered knowledge. Furthermore, we have also implemented a Web-based prototype tool which integrates the proposed infrastructure
Reasoning & Querying – State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
Semantic Co-Browsing System Based on Contextual Synchronization on Peer-to-Peer Environment
In this paper, we focus on a personalized information retrieval system based on multi-agent platform. Especially, they are capable of sharing information between them, for supporting collaborations between people. Personalization module has to be exploited to be aware of the corresponding user's browsing contexts (e.g., purposes, intention, and goals) at the specific moment. We want to recommend as relevant information to the estimated user context as possible, by analyzing the interaction results (e.g., clickstreams or query results). Thereby, we propose a novel approach to self-organizing agent groups based on contextual synchronization. Synchronization is an important requirement for online collaborations among them. This synchronization method exploits contextual information extracted from a set of personal agents in the same group, for real-time information sharing. Through semantically tracking of the users' information searching behaviors, we model the temporal dynamics of personal and group context. More importantly, in a certain moment, the contextual outliers can be detected, so that the groups can be automatically organized again with the same context. The co-browsing system embedding our proposed method was shown 52.7 % and 11.5 % improvements of communication performance, compared to single browsing system and asynchronous collaborative browsing system, respectively
High-level feature detection from video in TRECVid: a 5-year retrospective of achievements
Successful and effective content-based access to digital
video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like
colour, texture, or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one which determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically however, this depends on being able to determine whether each feature is or is not present in a video clip.
The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarise the work
done on the TRECVid high-level feature task, showing the
progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can
achieve large-scale, fast and reliable high-level feature detection on video
Building Data Warehouses with Semantic Web Data
The Semantic Web (SW) deployment is now a realization and the amount of
semantic annotations is ever increasing thanks to several initiatives that promote
a change in the current Web towards the Web of Data, where the semantics of
data become explicit through data representation formats and standards such as
RDF/(S) and OWL. However, such initiatives have not yet been accompanied
by e cient intelligent applications that can exploit the implicit semantics and
thus, provide more insightful analysis. In this paper, we provide the means for
e ciently analyzing and exploring large amounts of semantic data by combining
the inference power from the annotation semantics with the analysis capabilities
provided by OLAP-style aggregations, navigation, and reporting. We formally
present how semantic data should be organized in a well-de ned conceptual
MD schema, so that sophisticated queries can be expressed and evaluated. Our
proposal has been evaluated over a real biomedical scenario, which demonstrates
the scalability and applicability of the proposed approach
XML Matchers: approaches and challenges
Schema Matching, i.e. the process of discovering semantic correspondences
between concepts adopted in different data source schemas, has been a key topic
in Database and Artificial Intelligence research areas for many years. In the
past, it was largely investigated especially for classical database models
(e.g., E/R schemas, relational databases, etc.). However, in the latest years,
the widespread adoption of XML in the most disparate application fields pushed
a growing number of researchers to design XML-specific Schema Matching
approaches, called XML Matchers, aiming at finding semantic matchings between
concepts defined in DTDs and XSDs. XML Matchers do not just take well-known
techniques originally designed for other data models and apply them on
DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical
structure of a DTD/XSD) to improve the performance of the Schema Matching
process. The design of XML Matchers is currently a well-established research
area. The main goal of this paper is to provide a detailed description and
classification of XML Matchers. We first describe to what extent the
specificities of DTDs/XSDs impact on the Schema Matching task. Then we
introduce a template, called XML Matcher Template, that describes the main
components of an XML Matcher, their role and behavior. We illustrate how each
of these components has been implemented in some popular XML Matchers. We
consider our XML Matcher Template as the baseline for objectively comparing
approaches that, at first glance, might appear as unrelated. The introduction
of this template can be useful in the design of future XML Matchers. Finally,
we analyze commercial tools implementing XML Matchers and introduce two
challenging issues strictly related to this topic, namely XML source clustering
and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure
FROM USER-GENERATED-CONTENT TO STRUCTURED KNOWLEDGE EXPLORING MULTI-ASPECT SENTENCE REPRESENTATION AND PROTOTYPE HIERARCHY BASED CATEGORIZATION FOR ORGANIZATION OF TEXT COLLECTIONS
Ph.DDOCTOR OF PHILOSOPH
Mining a Small Medical Data Set by Integrating the Decision Tree and t-test
[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI
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