Qiaoling Liu

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Qiaoling Liu 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
Topic modeling approach to named entity linking Named entity linking
Probabilistic topic models
Wikipedia
Ruan Jian Xue Bao/Journal of Software Chinese 2014 Named entity linking (NEL) is an advanced technology which links a given named entity to an unambiguous entity in the knowledge base, and thus plays an important role in a wide range of Internet services, such as online recommender systems and Web search engines. However, with the explosive increasing of online information and applications, traditional solutions of NEL are facing more and more challenges towards linking accuracy due to the large number of online entities. Moreover, the entities are usually associated with different semantic topics (e.g., the entity "Apple" could be either a fruit or a brand) whereas the latent topic distributions of words and entities in same documents should be similar. To address this issue, this paper proposes a novel topic modeling approach to named entity linking. Different from existing works, the new approach provides a comprehensive framework for NEL and can uncover the semantic relationship between documents and named entities. Specifically, it first builds a knowledge base of unambiguous entities with the help of Wikipedia. Then, it proposes a novel bipartite topic model to capture the latent topic distribution between entities and documents. Therefore, given a new named entity, the new approach can link it to the unambiguous entity in the knowledge base by calculating their semantic similarity with respect to latent topics. Finally, the paper conducts extensive experiments on a real-world data set to evaluate our approach for named entity linking. Experimental results clearly show that the proposed approach outperforms other state-of-the-art baselines with a significant margin. 0 0
China physiome project: A comprehensive framework for anatomical and physiological databases from the China digital human and the visible rat China Digital Human
Database
Markup language
Physiome Project
Visible Rat phantom
Wiki
Proceedings of the IEEE English 2009 The connection study between biological structure and function, as well as between anatomical data and mechanical or physiological models, has been of increasing significance with the rapid advancement in experimental physiology and computational physiology. The China Physiome Project (CPP) is dedicated in optimization of the connection exploration based on standardization and integration of the structural datasets and their derivatives of cryosectional images with various standards, collaboration mechanisms, and online services. The CPP framework hereby incorporates the three-dimensional anatomical models of human and rat anatomy, the finite-element models of whole-body human skeleton, and the multiparticle radiological dosimetry data of both the human and rat computational phantoms. The ontology of CPP was defined using MeSH and, with its all standardized models description implemented by M3L, a multiscale modeling language based on XML. Provided services based on Wiki concept include collaboration research, modeling version control, data sharing, online analysis of M3L documents. As a sample case, a multiscale model for human heart modeling, in which familial hypertrophic cardiomyopathy was studied according to the structure-function relations from genetic level to organ level, is integrated into the framework and given for demonstration of the functionality of multiscale physiological modeling based on CPP. 0 0
Catriple: Extracting triples from wikipedia categories Lecture Notes in Computer Science English 2008 As an important step towards bootstrapping the Semantic Web, many efforts have been made to extract triples from Wikipedia because of its wide coverage, good organization and rich knowledge. One kind of important triples is about Wikipedia articles and their non-isa properties, e.g. (Beijing, country, China). Previous work has tried to extract such triples from Wikipedia infoboxes, article text and categories. The infobox-based and text-based extraction methods depend on the infoboxes and suffer from a low article coverage. In contrast, the category-based extraction methods exploit the widespread categories. However, they rely on predefined properties, which is too effort-consuming and explores only very limited knowledge in the categories. This paper automatically extracts properties and triples from the less explored Wikipedia categories so as to achieve a wider article coverage with less manual effort. We manage to realize this goal by utilizing the syntax and semantics brought by super-sub category pairs in Wikipedia. Our prototype implementation outputs about 10M triples with a 12-level confidence ranging from 47.0% to 96.4%, which cover 78.2% of Wikipedia articles. Among them, 1.27M triples have confidence of 96.4%. Applications can on demand use the triples with suitable confidence. 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