Items where author is affiliated with IBM China Research Laboratory
Number of items: 2.
and Liu, Qiaoling
and Xue, Gui-Rong
and Yu, Yong
and Zhang, Lei
and Pan, Yue Dataplorer: A Scalable Search Engine for the Data Web.
More and more structured information in the form of semantic data is nowadays available. It offers a wide range of new possibilities especially for semantic search and Web data integration. However, their effective exploitation still brings about a number of challenges, e.g. usability, scalability and uncertainty. In this paper, we present Dataplorer, a solution designed to address these challenges. We consider the usability through the use of hybrid queries and faceted search, while still preserving the scalability thanks to an extension of inverted index to support this type of query. Moreover, Dataplorer deals with uncertainty by means of a powerful ranking scheme to ﬁnd relevant results. Our experimental results show that our proposed approach is promising and it makes us believe that it is possible to extend the current IR infrastructure to query and search the Web of data. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms: Algorithms, Performance, Experimentation Keywords: hybrid query, inverted index, ranking, faceted search sake of the others. The usability challenge is addressed by providing the user with hybrid query capabilities, leveraging the power of structured queries and the ease of use of keyword search. We also propose a faceted search functionality that allows users to progressively compose the structured part of their information need after having started with imprecise keywords. Scalability is one of the main challenges that hybrid queries are facing, due to the large amount of data. Inspired from the cross ﬁeld of DB and IR integration, we make IR compatible with hybrid search through an extension of the inverted index, and thus able to scale as well as to handle structured information. To ensure that uncertainty does not remain as a problem to return relevant results, we provide a powerful ranking scheme that considers structures of both data and hybrid queries for score propagation and aggregation during results ranking. As an improvement of our previous work , we support faceted search with integrated ranking to tackle both usability and uncertainty issues while preserving efficiency.
and Wu, Zhaohui
and Ni, Yuan
and Xie, Guotong
and Zhou, Chunying
and Chen, Huajun sMash: Semantic-based Mashup Navigation for Data API Network.
With the proliferation of data APIs, it is not uncommon that users who have no clear ideas about data APIs will encounter difficulties to build Mashups to satisfy their requirements. In this paper, we present a semantic-based mashup navigation system, sMash that makes mashup building easy by constructing and visualizing a real-life data API network. We build a sample network by gathering more than 300 popular APIs and find that the relationships between them are so complex that our system will play an important role in navigating users and give them inspiration to build interesting mashups easily. The system is accessible at: http://www.dart.zju.edu.cn/mashup.
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We (Southampton EPrints Project) intend to preserve the files and HTML pages of this site for many years, however we will turn it into flat files for long term preservation. This means that at some point in the months after the conference the search, metadata-export, JSON interface, OAI etc. will be disabled as we "fossilize" the site. Please plan accordingly. Feel free to ask nicely for us to keep the dynamic site online longer if there's a rally good (or cool) use for it... [this has now happened, this site is now static]