Linyun Fu

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Linyun Fu is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Bricking Semantic Wikipedia by relation population and predicate suggestion Predicate suggestion
Relation classification
Relation population
Semantic Wikipedia
Web Intelligence and Agent Systems English 2012 Semantic Wikipedia aims to enhance Wikipedia by adding explicit semantics to links between Wikipedia entities. However, we have observed that it currently suffers the following limitations: lack of semantic annotations and lack of semantic annotators. In this paper, we resort to relation population to automatically extract relations between any entity pair to enrich semantic data, and predicate suggestion to recommend proper relation labels to facilitate semantic annotating. Both tasks leverage relation classification which tries to classify extracted relation instances into predefined relations. However, due to the lack of labeled data and the excessiveness of noise in Semantic Wikipedia, existing approaches cannot be directly applied to these tasks to obtain high-quality annotations. In this paper, to tackle the above problems brought by Semantic Wikipedia, we use a label propagation algorithm and exploit semantic features like domain and range constraints on categories as well as linguistic features such as dependency trees of context sentences in Wikipedia articles. The experimental results on 7 typical relation types show the effectiveness and efficiency of our approach in dealing with both tasks. © 2012-IOS Press and the authors. All rights reserved. 0 0
EachWiki: Facilitating wiki authoring by annotation suggestion Category suggestion
Link suggestion
Semantic relation suggestion
ACM Transactions on Intelligent Systems and Technology English 2012 Wikipedia, one of the best-known wikis and the world's largest free online encyclopedia, has embraced the power of collaborative editing to harness collective intelligence. However, using such a wiki to create high-quality articles is not as easy as people imagine, given for instance the difficulty of reusing knowledge already available in Wikipedia. As a result, the heavy burden of upbuilding and maintaining the evergrowing online encyclopedia still rests on a small group of people. In this article, we aim at facilitating wiki authoring by providing annotation recommendations, thus lightening the burden of both contributors and administrators. We leverage the collective wisdom of the users by exploiting Semantic Web technologies with Wikipedia data and adopt a unified algorithm to support link, category, and semantic relation recommendation. A prototype system named EachWiki is proposed and evaluated. The experimental results show that it has achieved considerable improvements in terms of effectiveness, efficiency and usability. The proposed approach can also be applied to other wiki-based collaborative editing systems. 0 0
Towards better understanding and utilizing relations in DBpedia DBpedia
Relation understanding
Relation utilization
Web Intelligence and Agent Systems English 2012 This paper is concerned with the problems of understanding the relations in automatically extracted semantic datasets such as DBpedia and utilizing them in semantic queries such as SPARQL. Although DBpedia has achieved a great success in supporting convenient navigation and complex queries over the extracted semantic data from Wikipedia, the browsing mechanism and the organization of the relations in the extracted data are far from satisfactory. Some relations have anomalous names and are hard to be understood even by experts if looking at the relation names only; there exist synonymous and polysemous relations which may cause incomplete or noisy query results. In this paper, we propose to solve these problems by 1) exploiting the Wikipedia category system to facilitate relation understanding and query constraint selection, 2) exploring various relation representation models for similar/super-/sub-relation detection to help the users select proper relations in their queries. A prototype system has been implemented and extensive experiments are performed to illustrate the effectiveness of the proposed approach. © 2012-IOS Press and the authors. All rights reserved. 0 0
Lightweight integration of IR and DB for scalable hybrid search with integrated ranking support Hybrid search
Inverted index
IR and DB integration
Ranking
Scalable query processing
Journal of Web Semantics English 2011 The Web contains a large amount of documents and an increasing quantity of structured data in the form of RDF triples. Many of these triples are annotations associated with documents. While structured queries constitute the principal means to retrieve structured data, keyword queries are typically used for document retrieval. Clearly, a form of hybrid search that seamlessly integrates these formalisms to query both textual and structured data can address more complex information needs. However, hybrid search on the large scale Web environment faces several challenges. First, there is a need for repositories that can store and index a large amount of semantic data as well as textual data in documents, and manage them in an integrated way. Second, methods for hybrid query answering are needed to exploit the data from such an integrated repository. These methods should be fast and scalable, and in particular, they shall support flexible ranking schemes to return not all but only the most relevant results. In this paper, we present CE2, an integrated solution that leverages mature information retrieval and database technologies to support large scale hybrid search. For scalable and integrated management of data, CE2 integrates off-the-shelf database solutions with inverted indexes. Efficient hybrid query processing is supported through novel data structures and algorithms which allow advanced ranking schemes to be tightly integrated. Furthermore, a concrete ranking scheme is proposed to take features from both textual and structured data into account. Experiments conducted on DBpedia and Wikipedia show that CE2 can provide good performance in terms of both effectiveness and efficiency. © 2011 Elsevier B.V. All rights reserved. 0 0
Making More Wikipedians: Facilitating Semantics Reuse for Wikipedia Authoring The Semantic Web English 2008 Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It can also serve as an ideal Semantic Web data source due to its abundance, influence, high quality and well-structuring. However, the heavy burden of up-building and maintaining such an enormous and ever-growing online encyclopedic knowledge base still rests on a very small group of people. Many casual users may still feel difficulties in writing high quality Wikipedia articles. In this paper, we use RDF graphs to model the key elements in Wikipedia authoring, and propose an integrated solution to make Wikipedia authoring easier based on RDF graph matching, expecting making more Wikipedians. Our solution facilitates semantics reuse and provides users with: 1) a link suggestion module that suggests and auto-completes internal links between Wikipedia articles for the user; 2) a category suggestion module that helps the user place her articles in correct categories. A prototype system is implemented and experimental results show significant improvements over existing solutions to link and category suggestion tasks. The proposed enhancements can be applied to attract more contributors and relieve the burden of professional editors, thus enhancing the current Wikipedia to make it an even better Semantic Web data source. 0 0
EachWiki: Suggest to be an easy-to-edit wiki interface for everyone CEUR Workshop Proceedings English 2007 In this paper, we present EachWiki, an extension of Semantic MediaWiki characterized by an intelligent suggestion mechanism. It aims to facilitate the wiki authoring by recommending the following elements: links, categories, and properties. We exploit the semantics of Wikipedia data and leverage the collective wisdom of web users to provide high quality annotation suggestions. The proposed mechanism not only improves the usability of Semantic MediaWiki but also speeds up its converging use of terminology. The suggestions are applied to relieve the burden of wiki authoring and attract more inexperienced contributors, thus making Semantic MediaWiki even better Semantic Web proto types and data source. 0 0