Search (11 results, page 1 of 1)

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Abstract

Web authoring and publishing in organizations / enterprises has become a more and more important and complex task. It has become a distributed, collaborative process performed by many users with less or even no HTML knowledge. In order to support this process, i.e., to facilitate the creation of websites and to maintain their usability and consistency, Web authoring and publishing systems are frequently asked for. In this paper, a Web authoring and publishing system, the DAPHNE (Distributed Authoring and Publishing in a Hypertext and Networked Environment) system, will be introduced. Based on metadata management and standard Internet technologies, DAPHNE offers many interesting features. Most importantly, DAPHNE allows a high openness from the system viewpoint as well as a high flexibility for authors: no HTML knowledge will be required - users can edit documents by using the editing tools they are familiar with and which exist in their current computing environment. DAPHNE will automatically generate a dynamic version of a well-structured website as well as a set of well-structured static HTML files. By employing subject headings - the structural element of DAPHNE- DAPHNE allows to support a navigation structure of high usability and a standard layout of the website as well. In this paper, following a brief introduction to DAPHNE's system architecture and design principles, DAPHNE'S key features, new developments of DAPHNE, especially those regarding integration of a workflow for management of multilingual websites and the possibility of building a document/knowledge management system using DAPHNE, will be addressed. A discussion on Web authoring and publishing in organizations / enterprises will be given as well

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Abstract

Relation extraction is the process of scanning text for relationships between named entities. Recently, significant studies have focused on automatically extracting relations from biomedical corpora. Most existing biomedical relation extractors require manual creation of biomedical lexicons or parsing templates based on domain knowledge. In this study, we propose to use kernel-based learning methods to automatically extract biomedical relations from literature text. We develop a framework of kernel-based learning for biomedical relation extraction. In particular, we modified the standard tree kernel function by incorporating a trace kernel to capture richer contextual information. In our experiments on a biomedical corpus, we compare different kernel functions for biomedical relation detection and classification. The experimental results show that a tree kernel outperforms word and sequence kernels for relation detection, our trace-tree kernel outperforms the standard tree kernel, and a composite kernel outperforms individual kernels for relation extraction.

Type

a

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Abstract

The effective use of corporate memory is becoming increasingly important because every aspect of e-business requires access to information repositories. Unfortunately, less-than-satisfying effectiveness in state-of-the-art information-retrieval techniques is well known, even for some of the best search engines such as Google. In this study, the authors resolve this retrieval ineffectiveness problem by developing a new framework for predicting query performance, which is the first step toward better retrieval effectiveness. Specifically, they examine the relationship between query performance and query context. A query context consists of the query itself, the document collection, and the interaction between the two. The authors first analyze the characteristics of query context and develop various features for predicting query performance. Then, they propose a context-sensitive model for predicting query performance based on the characteristics of the query and the document collection. Finally, they validate this model with respect to five real-world collections of documents and demonstrate its utility in routing queries to the correct repository with high accuracy.

Type

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Abstract

Query classification is an important part of exploring the characteristics of web queries. Existing studies are mainly based on Broder's classification scheme and classify user queries into navigational, informational, and transactional categories according to users' information needs. In this article, we present a novel classification scheme from the perspective of queries' temporal patterns. Queries' temporal patterns are inherent time series patterns of the search volumes of queries that reflect the evolution of the popularity of a query over time. By analyzing the temporal patterns of queries, search engines can more deeply understand the users' search intents and thus improve performance. Furthermore, we extract three groups of features based on the queries' search volume time series and use a support vector machine (SVM) to automatically detect the temporal patterns of user queries. Extensive experiments on the Million Query Track data sets of the Text REtrieval Conference (TREC) demonstrate the effectiveness of our approach.

Type

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Abstract

Am Beispiel des Online-Redaktionssystems DAPHNE (Distributed Authoring, and Publishing of Hypertext in a Network Environment) lässt sich zeigen, dass ein Online-Redaktionssystem aufgrund seiner Funktionalitäten durchaus auch zum Aufbau und zur Pflege von Digitalen Bibliotheken geeignet ist. Das Redaktionssystem ermöglicht es, den Datenbestand nach unterschiedlichen Strukturen hierarchisch zu gliedern, Verwaltungsfunktion ebenso wie die Betreuung des Layout zu zentralisieren bzw. zu delegieren und den Bearbeitern und Nutzern sehr differenzierte Änderungs- und Zugriffsrechte einzuräumen. Dieser Funktionsumfang kann sich mit dem Anforderungsprofil an eine Plattform zur Erstellung und Pflege Digitaler Bibliotheken decken

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Abstract

This research explores the interaction of textual and photographic information in multimodal documents. The World Wide Web (WWW) may be viewed as the ultimate, large-scale, dynamically changing, multimedia database. Finding useful information from the WWW without encountering numerous false positives (the current case) poses a challenge to multimedia information retrieval systems (MMIR). The fact that images do not appear in isolation, but rather with accompanying collateral text, is exploited. Taken independently, existing techniques for picture retrieval using collateral text-based methods and image-based methods have several limitations. Text-based methods, while very powerful in matching context, do not have access to image content. Image-based methods compute general similarity between images and provide limited semantics. This research focuses on improving precision and recall in an MMIR system by interactively combining text processing with image processing (IP) in both the indexing and retrieval phases. A picture search engine is demonstrated as an application.

Type

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Abstract

Social tagging is one of the major phenomena transforming the World Wide Web from a static platform into an actively shared information space. This paper addresses various aspects of social tagging, including different views on the nature of social tagging, how to make use of social tags, and how to bridge social tagging with other Web functionalities; it discusses the use of facets to facilitate browsing and searching of tagging data; and it presents an analogy between bibliometrics and tagometrics, arguing that established bibliometric methodologies can be applied to analyze tagging behavior on the Web. Based on the Upper Tag Ontology (UTO), a Web crawler was built to harvest tag data from Delicious, Flickr, and YouTube in September 2007. In total, 1.8 million objects, including bookmarks, photos, and videos, 3.1 million taggers, and 12.1 million tags were collected and analyzed. Some tagging patterns and variations are identified and discussed.

Type

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Abstract

Information extraction is an important text-mining task that aims at extracting prespecified types of information from large text collections and making them available in structured representations such as databases. In the biomedical domain, information extraction can be applied to help biologists make the most use of their digital-literature archives. Currently, there are large amounts of biomedical literature that contain rich information about biomedical substances. Extracting such knowledge requires a good named entity recognition technique. In this article, we combine conditional random fields (CRFs), a state-of-the-art sequence-labeling algorithm, with two semisupervised learning techniques, bootstrapping and feature sampling, to recognize disease names from biomedical literature. Two data-processing strategies for each technique also were analyzed: one sequentially processing unlabeled data partitions and another one processing unlabeled data partitions in a round-robin fashion. The experimental results showed the advantage of semisupervised learning techniques given limited labeled training data. Specifically, CRFs with bootstrapping implemented in sequential fashion outperformed strictly supervised CRFs for disease name recognition. The project was supported by NIH/NLM Grant R33 LM07299-01, 2002-2005.

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Abstract

With the rapid growth of Web 2.0, community question answering (CQA) has become a prevalent information seeking channel, in which users form interactive communities by posting questions and providing answers. Communities may evolve over time, because of changes in users' interests, activities, and new users joining the network. To better understand user interactions in CQA communities, it is necessary to analyze the community structures and track community evolution over time. Existing work in CQA focuses on question searching or content quality detection, and the important problems of community extraction and evolutionary pattern detection have not been studied. In this article, we propose a probabilistic community model (PCM) to extract overlapping community structures and capture their evolution patterns in CQA. The empirical results show that our algorithm appears to improve the community extraction quality. We show empirically, using the iPhone data set, that interesting community evolution patterns can be discovered, with each evolution pattern reflecting the variation of users' interests over time. Our analysis suggests that individual users could benefit to gain comprehensive information from tracking the transition of products. We also show that the communities provide a decision-making basis for business.

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Abstract

This study examined the relationships among perceived editorial responsiveness, perceived journal quality, and review time of submissions for authors in mainland China. Online review data generated by authors who have experienced the submission process in 10 Chinese academic journals were collected. The results of Spearman correlation analysis show that Chinese authors' perceived responsiveness of an editorial office is positively correlated with perceived quality of the journal, and the total review time does not affect perceptions of the quality of a journal and its editorial responsiveness.

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Abstract

Recently, lecture videos have been widely used in e-learning systems. Envisioning intelligent e-learning systems, this article addresses the challenge of information seeking in lecture videos by retrieving relevant video segments based on user queries, through dynamic segmentation of lecture speech text. In the proposed approach, shallow parsing such as part of-speech tagging and noun phrase chunking are used to parse both questions and Automated Speech Recognition (ASR) transcripts. A sliding-window algorithm is proposed to identify the start and ending boundaries of returned segments. Phonetic and partial matching is utilized to correct the errors from automated speech recognition and noun phrase chunking. Furthermore, extra knowledge such as lecture slides is used to facilitate the ASR transcript error correction. The approach also makes use of proximity to approximate the deep parsing and structure match between question and sentences in ASR transcripts. The experimental results showed that both phonetic and partial matching improved the segmentation performance, slides-based ASR transcript correction improves information coverage, and proximity is also effective in improving the overall performance.

Type

a