Zhishi.me - Weaving Chinese Linking Open Data
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Zhishi.me is a Chinese linking open data hub that extracts semantic data from Chinese web-based encyclopedias like Baidu Baike and Hudong Baike. Over 124 million RDF triples were extracted to populate the hub. The hub provides a SPARQL endpoint and lookup service to access the extracted data. Heuristic strategies like punctuation cleaning and synonym extension are used to discover mappings between equivalent resources from different source datasets.
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The internethas massive amount of information. This information is stored in the form of zillions of
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the ‘surface web’.But the remaining information, which is not indexed by search engines – the ‘deep web’,
is much bigger in size than the ‘surface web’, and remains unexploited yet.
Several machine learning techniques have been commonly employed to access deep web content. Under
machine learning, topic models provide a simple way to analyze large volumes of unlabeled text. A ‘topic’is
a cluster of words that frequently occur together and topic models can connect words with similar
meanings and distinguish between words with multiple meanings. In this paper, we cluster deep web
databases employing several methods, and then perform a comparative study. In the first method, we apply
Latent Semantic Analysis (LSA) over the dataset. In the second method, we use a generative probabilistic
model called Latent Dirichlet Allocation(LDA) for modeling content representative of deep web
databases.Both these techniques are implemented after preprocessing the set of web pages to extract page
contents and form contents.Further, we propose another version of Latent Dirichlet Allocation (LDA) to the
dataset. Experimental results show that the proposed method outperforms the existing clustering methods.
Das Semantische Daten Web für Unternehmen
This document summarizes the vision, technology, and applications of the Semantic Data Web for businesses. It discusses how the Semantic Web can help solve problems of searching for complex information across different data sources by complementing text on web pages with structured linked open data. It provides overviews of RDF standards, vocabularies, and technologies like SPARQL and OntoWiki that allow creating and managing structured knowledge bases. It also presents examples like DBpedia that extract structured data from Wikipedia and make it available on the web as linked open data.
acm_src_grandfinals_thomas_effland
This document describes a novel framework for focused crawling tailored to extracting semantically relevant information from disparate university websites with highly variable structures. It uses machine learning techniques including two random forest regressors to rank page and link relevance in order to efficiently navigate to target pages like course descriptions. Evaluation on 5 university sites found the regressors could be accurately trained with limited data, and the system achieved an 80% harvest rate of relevant pages within 50 crawled pages for 4 out of 5 sites tested.
Semantic Annotation: The Mainstay of Semantic Web
Given that semantic Web realization is based on the critical mass of metadata accessibility and the representation of data with formal
knowledge, it needs to generate metadata that is specific, easy to understand and well-defined. However, semantic annotation of the
web documents is the successful way to make the Semantic Web vision a reality. This paper introduces the Semantic Web and its
vision (stack layers) with regard to some concept definitions that helps the understanding of semantic annotation. Additionally, this
paper introduces the semantic annotation categories, tools, domains and models
What Are Links in Linked Open Data? A Characterization and Evaluation of Link...
Linked Open Data promises to provide guiding principles to publish interlinked knowledge graphs on the Web in the form of findable, accessible, interoperable, and reusable datasets. In this talk I argue that while as such, Linked Data may be viewed as a basis for instantiating the FAIR principles, there are still a number of open issues that cause significant data quality issues even when knowledge graphs are published as Linked Data. In this talk I will first define the boundaries of what constitutes a single coherent knowledge graph within Linked Data, i.e., present a principled notion of what a dataset is and what links within and between datasets are. I will also define different link types for data in Linked datasets and present the results of our empirical analysis of linkage among the datasets of the Linked Open Data cloud. Recent results from our analysis of Wikidata, which has not been part of the Linked Open Data Cloud, will also be presented.
ANALYSIS OF RESEARCH ISSUES IN WEB DATA MINING
ANALYSIS OF RESEARCH ISSUES IN WEB DATA MINING International Journal of Technical Research & Application
In this Research paper, we present an overview of
research issues in web mining. We discuss mining with respect to
web data referred here as web data mining. In particular, our
focus is on web data mining research in context of our web
warehousing project.We have categorized web data mining into
three areas; web content mining, web structure mining and web
usage mining. We have highlighted and discussed various
research issues involved in each of these web data mining
category. We believe that web data mining will be the topic of
exploratory research in near future. A survey of_eigenvector_methods_for_web_information_retrieval
This document provides a survey of three popular eigenvector methods for web information retrieval: HITS, PageRank, and SALSA. It begins with an introduction to the challenges of web information retrieval compared to traditional IR. It then describes the two main theses behind HITS and PageRank - that they exploit the hyperlink structure of the web. The majority of the document focuses on explaining the mathematical implementation of HITS and its convergence properties. It also briefly introduces PageRank and SALSA before concluding.
鏈結資料在圖書館的應用20131107
1) Linked data is a set of best practices for publishing structured data on the web so that both humans and machines can access and link related data across different sources. It realizes Tim Berners-Lee's vision of a Semantic Web.
2) The key principles of linked data are using URIs to identify things, providing HTTP URIs so that URIs can be looked up, and including links to other URIs to allow for discovery of related data on the web.
3) By following these principles, data sources on the web have been connected into a large Web of Data, with over 31 billion RDF triples organized into different domains such as media, geography, life sciences, and libraries. This enables new applications for data
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- 1. Zhishi.me Weaving Chinese Linking Open Data Xing Niu , Xinruo Sun, Haofen Wang, Shu Rong, Guilin Qi and Yong Yu 2011.10.25
- 7. Overall Statistics on Extraction Results Page
- 8. Overall Statistics on Extraction Results Page
- 10. Page Label Abstract Infobox Subjects Thumbnails Other properties
- 12. Page
- 19. Data-level Mapping (Web Access) Page
- 20. Data-level Mapping (con’t) Page Linking Open Data Chinese Linking Open Data