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检索条件"机构=APEX Data and Knowledge Management Lab"

共 73 条记录，以下是51-60 订阅

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PORE: Positive-only relation extraction from Wikipedia text

引用

6th International Semantic Web Conference/2nd Asian Semantic Web Conference (ISWC 2007/ASWC 2007)

作者： Wang, Gang Yu, Yong Zhu, Haiping Shanghai Jiao Tong Univ Dept Comp Sci & Engn Apex Data & Knowledge Management Lab Shanghai 200240 Peoples R China

ISBN: (纸本)9783540762973

Extracting semantic relations is of great importance for the creation of the Semantic Web content. It is of great benefit to semi-automatically extract relations from the free text of Wikipedia using the structured content readily available in it. Pattern matching methods that employ information redundancy cannot work well since there is not much redundancy information in Wikipedia, compared to the Web. Multi-class classification methods are not reasonable since no classification of relation types is available in Wikipedia. In this paper, we propose PORE (Positive-Only Relation Extraction), for relation extraction from Wikipedia text. The core algorithm B-POL extends a state-of-the-art positive-only learning algorithm using bootstrapping, strong negative identification, and transductive inference to work with fewer positive training examples. We conducted experiments on several relations with different amount of training data. The experimental results show that B-POL can work effectively given only a small amount of positive training examples and it significantly outperforms the original positive learning approaches and a multi-class SVM. Furthermore, although PORE is applied in the context of Wikipedia, the core algorithm B-POL is a general approach for Ontology Population and can be adapted to other domains.

关键词： relation extraction ontology population positive-only learning

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An unsupervised model for exploring hierarchical semantics from social annotations

引用

6th International Semantic Web Conference/2nd Asian Semantic Web Conference (ISWC 2007/ASWC 2007)

作者： Zhou, Mianwei Bao, Shenghua Wu, Xian Yu, Yong Shanghai Jiao Tong Univ Dept Comp Sci & Engn APEX Data & Knowledge Management Lab Shanghai 200240 Peoples R China

ISBN: (纸本)9783540762973

This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations, for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service *** as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model's applicability on different environments. The experimental results demonstrate our model's efficiency.

关键词： Semantic Web

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SPARK: Adapting keyword query to semantic search

引用

6th International Semantic Web Conference/2nd Asian Semantic Web Conference (ISWC 2007/ASWC 2007)

作者： Zhou, Qi Wang, Chong Xiong, Miao Wang, Haofen Yu, Yong Shanghai Jiao Tong Univ Dept Comp Sci & Engn Apex Data & Knowledge Management Lab Shanghai 200240 Peoples R China

ISBN: (纸本)9783540762973

Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named 'SPARK' has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.

关键词： Query languages

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PANTO: A Portable nAtural laNguage inTerface to Ontologies

PANTO: A Portable nAtural laNguage inTerface to Ontologies

引用

4th European Semantic Web Conference

作者： Wang, Chong Xiong, Miao Zhou, Qi Yu, Yong Shanghai Jiao Tong Univ APEX Data & Knowledge Management Lab Dept Comp Sci & Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9783540726661

Providing a natural language interface to ontologies will not only offer ordinary users the convenience of acquiring needed information from ontologies, but also expand the influence of ontologies and the semantic web consequently. This paper presents PANTO, a Portable nAtural laNguage inTerface to Ontologies, which accepts generic natural language queries and outputs SPARQL queries. Based on a special consideration on nominal phrases, it adopts a triple-based data model to interpret the parse trees output by an off-the-shelf parser. Complex modifications in natural language queries such as negations, superlative and comparative are investigated. The experiments have shown that PANTO provides state-of-the-art results.

关键词： Natural language processing systems

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Using social annotations to smooth the language model for IR

Using social annotations to smooth the language model for IR

引用

11th Pacific-Asia Conference on knowledge Discovery and data Mining

作者： Xu, Shengliang Bao, Shenghua Yu, Yong Cao, Yunbo Shanghai Jiao Tong Univ APEX data & Knowledge Management Lab Shanghai Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9783540717003

In the paper, we present an exploration of using social annotations provided by the Web 2.0 sites (such as ***) in helping web search. More specifically, we consider using the social annotations as an additional resource to strengthen existing smoothing methods for the language model for IR. The social annotations can benefit the smoothing of language model in two aspects: 1) the annotations themselves can serve as the summaries of the web pages given by the users;2) the annotations can be seen as the links of the web pages sharing the same annotations. We propose three smoothing methods, addressing the two aspects and their combination, respectively. We call the new language model of using the proposed smoothing methods 'Language Annotation Model (LAM). Preliminary experimental results show that LAM significantly outperforms the traditional language models.

关键词： Computer programming languages

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Making more Wikipedians: Facilitating semantics reuse for Wikipedia authoring

引用

6th International Semantic Web Conference/2nd Asian Semantic Web Conference (ISWC 2007/ASWC 2007)

作者： Fu, Linyun Wang, Haofen Zhu, Haiping Zhang, Huajie Wang, Yang Yu, Yong Shanghai Jiao Tong Univ Dept Comp Sci & Engn Apex Data & Knowledge Management Lab 800 Dongchuan Rd Shanghai 200240 Peoples R China

ISBN: (纸本)9783540762973

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.

关键词： knowledge based systems

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Research on enterprise track of TREC 2007 at SJTU apex lab

Research on enterprise track of TREC 2007 at SJTU APEX Lab

引用

16th Text REtrieval Conference, TREC 2007

作者： Duan, Huizhong Zhou, Qi Lu, Zhen Jin, Ou Bao, Shenghua Cao, Yunbo Yu, Yong Apex Knowledge and Data Management Lab. 800 Dongchuan Road Shanghai China Microsoft Research Asia No. 49 Zhichun Road Beijing China

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Towards quick understanding and analysis of large-scale ontologies

引用

1st Asian Semantic Web Conference

作者： Xiong, Miao Chen, Yifan Zheng, Hao Yu, Yong Shanghai Jiao Tong Univ APEX Data & Knowledge Management Lab Dept Comp Sci & Engn Shanghai 200240 Peoples R China

ISBN: (纸本)3540383298

With the development of semantic web technologies, large and complex ontologies are constructed and applied to many practical applications. In order for users to quickly understand and acquire information from these huge information "oceans", we propose a novel ontology visualization approach accompanied by "anatomies" of classes and properties. With the holistic "imaging", users can both quickly locate the interesting "hot" classes or properties and understand the evolution of the ontology;with the anatomies, they can acquire more detailed information of classes or properties that is arduous to collect by browsing and navigation. Specifically, we produce the ontology's holistic "imaging" which contains a semantic layout on classes and distributions of instances. Additionally, the evolution of the ontology is illustrated by the changes on the "imaging". Furthermore, detailed anatomies of classes and properties, which are enhanced by techniques in database field (e.g. data mining), are ready for users.

关键词： Large scale systems

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Mining query log to assist ontology learning from relational database

Mining query log to assist ontology learning from relational...

引用

8th Asia-Pacific Web Conference and Workshops (APWeb 2006)

作者： Zhang, J Xiong, M Yu, Y Shanghai Jiao Tong Univ Dept Comp Sci & Engn APEX Data & Knowledge Management Lab Shanghai 200030 Peoples R China

ISBN: (纸本)3540311424

Ontology learning plays a significant role in migrating legacy knowledge base into the Semantic Web. Relational database is the vital source that stores the structured knowledge today. Some prior work has contributed to the learning process from relational database to ontology. However, a majority of the existing methods focus on the schema dimension, leaving the data dimension not well exploited. In this paper we present a novel approach that exploits the data dimension by mining user query log to glorify the ontology learning process. In addition, we propose a set of rules for schema extraction which serves as the basis of our theme. The presented approach can be applied to a broad range of today's relational data warehouse.

关键词： data mining

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Cuckoo ring: Balancing workload for Locality Sensitive Hash 6

Cuckoo ring: Balancing workload for Locality Sensitive Hash

引用

6th International Conference on Peer-to-Peer Computing (P2P 2006)

作者： Han, Dingyi Shen, Ting Meng, Shicong Yu, Yong Shanghai Jiao Tong Univ Dept Comp Sci & Engn Apex Data & Knowledge Management Lab Shanghai 200240 Peoples R China

ISBN: (纸本)0769526799

Locality Sensitive Hash (LSH) is widely used in peer-to-peer (P2P) systems. Although it can support range or similarity queries, it breaks the load balance mechanism of traditional Distributed Hash Table (DHT) based system by replacing consistent hash with LSH. To solve the imbalance problem, current systems either weaken the locality preserve ability from similarity preserved to order preserved or adopt load aware peer join mechanism. The first method does not support similarity query as it loses the similarity information and the second method is greatly affected by the dynamic nature of P2P networks. In this paper, we propose a novel system, cuckoo ring, which can preserve similarity information while load balanced. It does not guide the newly joining peer to the hot areas but move the items in the hot areas to cold areas so that the short life time peers are distributed uniformly across the network instead of being guided to the hot areas. Compared to traditional DHT systems, cuckoo ring only maintains a little more information about the global light load peers and the moved indexed items.

关键词： cuckoo ring load balance consistent hash universal hash Locality Sensitive Hash

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