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- item-1 hasContent isabelle-augenstein.
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- item-2 hasContent sebastian-pado.
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- item-3 hasContent sebastian-rudolph.
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- 78 creator gregoire-burel.
- 78 creator harith-alani.
- 78 creator yulan-he.
- 78 type InProceedings.
- 78 label "Automatic Identification of Best Answers in Online Enquiry Communities".
- 78 sameAs 78.
- 78 abstract "Online communities are prime sources of information. The Web is rich with forums and Question Answering (Q&A) communities where people go to seek answers to all kinds of questions. Most systems employ manual answer-rating procedures to encourage people to provide quality answers and to help users locate the best answers in a given thread. However, in the datasets we collected from three online communities, we found that half their threads lacked best answer markings. This stresses the need for methods to assess the quality of available answers to: 1) provide automated ratings to fill in for, or support, manu- ally assigned ones, and; 2) to assist users when browsing such answers by filtering in potential best answers. In this paper, we collected data from three online communities and converted it to RDF based on the SIOC ontology. We then explored an approach for predicting best answers us- ing a combination of content, user, and thread features. We show how the influence of such features on predicting best answers differs across communities. Further we demonstrate how certain features unique to some of our community systems can boost predictability of best answers.".
- 78 hasAuthorList authorList.
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- 78 keyword "Community Question Answering".
- 78 keyword "Content Quality".
- 78 keyword "Online Communities".
- 78 keyword "Social Semantic Web".
- 78 title "Automatic Identification of Best Answers in Online Enquiry Communities".
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- item-1 hasContent gregoire-burel.
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- 84 creator fadi-maali.
- 84 creator richard-cyganiak.
- 84 creator vassilios-peristeras.
- 84 type InProceedings.
- 84 label "A publishing pipeline for Linked Government Data".
- 84 sameAs 84.
- 84 abstract "We tackle the problem of high cost associated with generating Linked Government Data (LGD). Our approach is centered around the idea of "self-service LGD". The self-service approach is enabled through an end-to-end publishing pipeline that enables transforming raw government data into interlinked RDF. The cost reduction involved is achieved through: (i) shifting the burden of Linked Data conversion towards the data consumer (ii) integrating all the steps involved into a single workbench (iii) providing graphical user interfaces (iv) enabling result sharing with sufficient provenance information. We present the implementation of the publishing pipeline and describe its application to a local government catalogue in Ireland resulting in a significant amount of Linked Data published.".
- 84 hasAuthorList authorList.
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- 84 keyword "Google Refine".
- 84 keyword "LGD".
- 84 keyword "Linked Data".
- 84 keyword "Linked Government Data".
- 84 keyword "dcat".
- 84 title "A publishing pipeline for Linked Government Data".
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- item-1 hasContent fadi-maali.
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- 85 creator andriy-nikolov.
- 85 creator enrico-motta.
- 85 creator mathieu-daquin.
- 85 type InProceedings.
- 85 label "Unsupervised Learning of Data Linking Configuration".
- 85 sameAs 85.
- 85 abstract "As commonly accepted identifiers for data instances in semantic datasets (such as ISBN codes or DOI identifiers) are often not available, discovering links between overlapping datasets on the Web is generally realised through the use of fuzzy similarity measures. Configuring such measures, i.e. deciding which similarity function to apply to which data properties with which parameters, is often a non-trivial task that depends on the domain, ontological schemas, and formatting conventions in data. Existing solutions either rely on the user's knowledge of the data and the domain or on the use of machine learning to discover these parameters based on training data. In this paper, we present a novel approach to tackle the issue of data linking which relies on the unsupervised discovery of the required similarity parameters. Instead of using labeled training data, the method takes into account several desired properties which the distribution of output similarity values should satisfy. The method includes these features into a fitness criterion used in a genetic algorithm to establish similarity parameters that maximise the quality of the resulting linkset according to the considered properties. We show in experiments using benchmarks as well as real-world datasets that such an unsupervised method can reach the same levels of performance as manually engineered methods, and how the different parameters of the genetic algorithm and the fitness criterion affect the results for different datasets.".
- 85 hasAuthorList authorList.
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- 85 keyword "data linking".
- 85 keyword "genetic algorithm".
- 85 keyword "instance matching".
- 85 title "Unsupervised Learning of Data Linking Configuration".
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- item-1 hasContent andriy-nikolov.
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- item-2 hasContent mathieu-daquin.
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- item-3 hasContent enrico-motta.