Matches in ScholarlyData for { ?s ?p ?o. }
- 547 hasTopic Information_integration.
- 547 hasTopic Peer-to-peer.
- 547 hasTopic Semantic_Web.
- 547 isPartOf proceedings.
- 547 title "Sindice.com: Weaving the Open Linked Data".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent giovanni-tummarello.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent eyal-oren.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent renaud-delbru.
- item-3 previous item-2.
- 561 creator chutiporn-anutariya.
- 561 creator rachanee-ungrangsi.
- 561 creator vilas-wuwongse.
- 561 type InProceedings.
- 561 label "combiSQORE: An Ontology Combination Algorithm".
- 561 sameAs 561.
- 561 abstract "Automatic knowledge reuse for Semantic Web applications imposes several challenges on ontology search. Existing ontology retrieval systems merely return a lengthy list of relevant single ontologies, which may not completely cover the specified user requirements. Therefore, there arises an increasing demand for a tool or algorithm with a mechanism to check concept adequacy of existing ontologies with respect to a user query, and then recommend a single or combination of ontologies which can entirely fulfill the requirements. Thus, this paper develops an algorithm, namely combiSQORE to determine whether the available collection of ontologies is able to completely satisfy a submitted query and return a single or combinative ontology that guarantees query coverage. In addition, it ranks the returned answers based on their conceptual closeness and query coverage. The experimental results show that the proposed algorithm is simple, efficient and effective.".
- 561 hasAuthorList authorList.
- 561 hasTopic Data_management.
- 561 hasTopic Ontology_%28computer_science%29.
- 561 hasTopic Ontology_alignment.
- 561 hasTopic Semantic_Web.
- 561 isPartOf proceedings.
- 561 title "combiSQORE: An Ontology Combination Algorithm".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent rachanee-ungrangsi.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent chutiporn-anutariya.
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- item-3 type ListItem.
- item-3 hasContent vilas-wuwongse.
- item-3 previous item-2.
- 57 creator stephan-bloehdorn.
- 57 creator york-sure.
- 57 type InProceedings.
- 57 label "Kernel Methods for Mining Instance Data in Ontologies".
- 57 sameAs 57.
- 57 abstract "The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.".
- 57 hasAuthorList authorList.
- 57 hasTopic Application_software.
- 57 hasTopic Data_management.
- 57 hasTopic Semantic_Web.
- 57 isPartOf proceedings.
- 57 title "Kernel Methods for Mining Instance Data in Ontologies".
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- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
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- item-1 type ListItem.
- item-1 hasContent stephan-bloehdorn.
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- item-2 type ListItem.
- item-2 hasContent york-sure.
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- 575 creator gang-wang.
- 575 creator haiping-zhu.
- 575 creator yong-yu.
- 575 type InProceedings.
- 575 label "PORE: Positive-Only Relation Extraction from Wikipedia Text".
- 575 sameAs 575.
- 575 abstract "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.".
- 575 hasAuthorList authorList.
- 575 hasTopic Information_extraction.
- 575 hasTopic Natural_language_processing.
- 575 hasTopic Semantic_Web.
- 575 hasTopic Web_annotation.
- 575 isPartOf proceedings.
- 575 title "PORE: Positive-Only Relation Extraction from Wikipedia Text".
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- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
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- item-1 type ListItem.
- item-1 hasContent gang-wang.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent yong-yu.
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