Jinghua Zhang

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Jinghua Zhang is an author.

Publications

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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Entity ranking based on Wikipedia for related entity finding Entity ranking
Entity-relation relevancy
Entity-type relevancy
Related entity finding
Wikipedia
Jisuanji Yanjiu yu Fazhan/Computer Research and Development Chinese 2014 Entity ranking is a very important step for related entity finding (REF). Although researchers have done many works about "entity ranking based on Wikipedia for REF", there still exists some issues: the semi-automatic acquirement of target-type, the coarse-grained target-type, the binary judgment of entity-type relevancy and ignoring the effects of stop words in calculation of entity-relation relevancy. This paper designs a framework, which ranks entities through the calculation of a triple-combination (including entity relevancy, entity-type relevancy and entity-relation relevancy) and acquires the best combination-method through the comparisons of experimental results. A novel approach is proposed to calculate the entity-type relevancy. It can automatically acquire the fine-grained target-type and the discriminative rules of its hyponym Wikipedia-categories through inductive learning, and calculate entity-type relevancy through counting the number of categories which meet the discriminative rules. Also, this paper proposes a "cut stop words to rebuild relation" approach to calculate the entity-relation relevancy between candidate entity and source entity. Experiment results demonstrate that the proposed approaches can effectively improve the entity-ranking results and reduce the time consumed in calculating. 0 0
An approach of filtering wrong-type entities for entity ranking Entity ranking
Related entity finding
Type filtering
Wikipedia
IEICE Transactions on Information and Systems English 2013 Entity is an important information carrier in Web pages. Users would like to directly get a list of relevant entities instead of a list of documents when they submit a query to the search engine. So the research of related entity finding (REF) is a meaningful work. In this paper we investigate the most important task of REF: Entity Ranking. The wrong-type entities which don't belong to the target-entity type will pollute the ranking result. We propose a novel method to filter wrong-type entities. We focus on the acquisition of seed entities and automatically extracting the common Wikipedia categories of target-entity type. Also we demonstrate how to filter wrong-type entities using the proposed model. The experimental results show our method can filter wrong-type entities effectively and improve the results of entity ranking. 0 0
Towards accurate distant supervision for relational facts extraction ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference English 2013 Distant supervision (DS) is an appealing learning method which learns from existing relational facts to extract more from a text corpus. However, the accuracy is still not satisfying. In this paper, we point out and analyze some critical factors in DS which have great impact on accuracy, including valid entity type detection, negative training examples construction and ensembles. We propose an approach to handle these factors. By experimenting on Wikipedia articles to extract the facts in Freebase (the top 92 relations), we show the impact of these three factors on the accuracy of DS and the remarkable improvement led by the proposed approach. 0 0
Using WebQuest as scaffolding in the wiki for collaborative learning Collaborative learning
Cscl
Environmental education
Web 2.0
Web Quest
Wiki
International Journal of Continuing Engineering Education and Life-Long Learning English 2013 The wiki is a suitable tool for online collaborative learning. In this study, WebQuest was used as scaffolding in the wiki to support students' collaborative learning. This research aimed to investigate learners' acceptance of the new learning approach and their learning outcomes. The results show that learners maintained more goal-oriented when they utilised WebQuest as scaffolding in the wiki, and kept better interactions among members, and reduced navigational disorientation. Also, the students in the experimental groups achieved better learning outcomes. © 2013 Inderscience Enterprises Ltd. 0 0
Type expansion based on Wikipedia for related entity finding Entity
Entity ranking
Related entity finding
Type expansion
International Journal of Advancements in Computing Technology English 2012 Entity is an important information carrier in Web pages. Searchers often want a ranked list of relevant entities directly rather a list of documents. So the research of related entity finding (REF) is very meaningful. In this paper we investigate the most important task of REF: Entity Ranking. To address the issue of wrong entity type in entity ranking: some retrieved entities don't belong to the target entity type. We make use of type expansion to deal with the issue of wrong entity type polluting entity ranking. To address the issue of some topics' recall and precision are low: we propose a approach to parse the syntactic structure of topics' narrative and modify the query, so complementing more relevant documents and improving the recall and precision. We use Wikipedia and Dbpedia as data sources in the experiment. We found type expansion based on basic type achieves a better result in recall and precision proved by experiment. 0 0
A study of category expansion for related entity finding Entity
Entity ranking
Related entity finding
Type filtering
Proceedings - 2011 4th International Symposium on Computational Intelligence and Design, ISCID 2011 English 2011 Entity is an important information carrier in Web pages. Searchers often want a ranked list of relevant entities directly rather a list of documents. So the research of related entity finding (REF) is very meaningful. In this paper we investigate the most important task of REF: Entity Ranking. To address the issue of wrong entity type in entity ranking: some retrieved entities don't belong to the target entity type. We make use of category expansion to deal with the issue of wrong entity type polluting entity ranking. We use Wikipedia and Dbpedia as data sources in the experiment. We found category expansion based on original type achieves a better result in recall and precision proved by experiment. 0 0
A social web approach to managing information and knowledge in the AEC industry AEC industry
Construction
Knowledge management
Publish/subscribe
Social Web
Web 2.0
Wiki
Proceedings - International Conference on Management and Service Science, MASS 2009 English 2009 As Social Web applications have begun to dominate the Internet and changed the way in which people communicate, there is a need to study the application of Social Web concepts in managing information and knowledge. This paper discusses the potential for using four types of social interaction (social tagging, wiki, blogging, and social networking) in the AEC industry. 0 0
Agent simulation of collaborative knowledge processing in Wikipedia Wikipedia
Agent simulation
Collaborative knowledge processing
User innovation community
SpringSim English 2008 0 1
Using and detecting links in Wikipedia Lecture Notes in Computer Science English 2008 In this paper, we document our efforts at INEX 2007 where we participated in the Ad Hoc Track, the Link the Wiki Track, and the Interactive Track that continued from INEX 2006. Our main aims at INEX 2007 were the following. For the Ad Hoc Track, we investigated the effectiveness of incorporating link evidence into the model, and of a CAS filtering method exploiting the structural hints in the INEX topics. For the Link the Wiki Track, we investigated the relative effectiveness of link detection based on retrieving similar documents with the Vector Space Model, and then filter with the names of Wikipedia articles to establish a link. For the Interactive Track, we took part in the interactive experiment comparing an element retrieval system with a passage retrieval system. The main results are the following. For the Ad Hoc Track, we see that link priors improve most of our runs for the Relevant in Context and Best in Context Tasks, and that CAS pool filtering is effective for the Relevant in Context and Best in Context Tasks. For the Link the Wiki Track, the results show that detecting links with name matching works relatively well, though links were generally under-generated, which hurt the performance. For the Interactive Track, our test-persons showed a weak preference for the element retrieval system over the passage retrieval system. 0 0
Semplore: An IR approach to scalable hybrid query of Semantic Web data Lecture Notes in Computer Science English 2007 As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we breifly describe how Semplore is used for searching Wikipedia and an IBM customer's product information. 0 0
Integration of Wikipedia and a geography digital library Geography digital libraries
Integration
Web-based encyclopedia
Lecture Notes in Computer Science English 2006 In this paper, we address the problem of integrating Wikipedia, an online encyclopedia, and G-Portal, a web-based digital library, in the geography domain. The integration facilitates the sharing of data and services between the two web applications that are of great value in learning. We first present an overall system architecture for supporting such an integration and address the metadata extraction problem associated with it. In metadata extraction, we focus on extracting and constructing metadata for geo-political regions namely cities and countries. Some empirical performance results will be presented. The paper will also describe the adaptations of G-Portal and Wikipedia to meet the integration requirements. 0 0
Understanding user perceptions on usefulness and usability of an integrated Wiki-G-Portal Lecture Notes in Computer Science English 2006 This paper describes a pilot study on Wiki-G-Portal, a project integrating Wikipedia, an online encyclopedia, into G-Portal, a Web-based digital library, of geography resources. Initial findings from the pilot study seemed to suggest positive perceptions on usefulness and usability of Wiki-G-Portal, as well as subjects' attitude and intention to use. 0 0