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Matches in ScholarlyData for { ?s ?p ?o. }

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• item-1 hasContent sebastian-krause.
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• item-2 hasContent hong-li.
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• item-3 hasContent hans-uszkoreit.
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• item-4 hasContent feiyu-xu.
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• paper-39 type InProceedings.
• paper-39 label "Mining Semantic Relations between Research Areas".
• paper-39 sameAs paper-39.
• paper-39 abstract "For a number of years now we have seen the emergence of repositories of research data specified using OWL/RDF as representation languages, and conceptualized according to a variety of ontologies. This class of solutions promises both to facilitate the integration of research data with other relevant sources of information and also to support more intelligent forms of querying and exploration. However, an issue which has only been partially addressed is that of generating and characterizing semantically the relations that exist between research areas. This problem has been traditionally addressed by manually creating taxonomies, such as the ACM classification of research topics. However, this manual approach is inadequate for a number of reasons: these taxonomies are very coarse-grained and they do not cater for the finegrained research topics, which define the level at which typically researchers (and even more so, PhD students) operate. Moreover, they evolve slowly, and therefore they tend not to cover the most recent research trends. In addition, as we move towards a semantic characterization of these relations, there is arguably a need for a more sophisticated characterization than a homogeneous taxonomy, to reflect the different ways in which research areas can be related. In this paper we propose Klink, a new approach to i) automatically generating relations between research areas and ii) populating a bibliographic ontology, which combines both machine learning methods and external knowledge, which is drawn from a number of resources, including Google Scholar and Wikipedia. We have tested a number of alternative algorithms and our evaluation shows that a method relying on both external knowledge and the ability to detect temporal relations between research areas performs best with respect to a manually constructed standard.".
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• paper-39 title "Mining Semantic Relations between Research Areas".
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• item-1 hasContent francesco-osborne.
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• item-2 hasContent enrico-motta.
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• paper-40 type InProceedings.
• paper-40 label "Semantic Sentiment Analysis of Twitter".
• paper-40 sameAs paper-40.
• paper-40 abstract "Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. ''Apple product'') as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.".
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• paper-40 title "Semantic Sentiment Analysis of Twitter".
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• item-1 hasContent hassan-saif.
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• item-2 hasContent yulan-he.
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• item-3 type ListItem.
• item-3 hasContent harith-alani.
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• paper-41 type InProceedings.
• paper-41 label "CrowdMAP: Crowdsourcing Ontology Alignment with Microtasks".
• paper-41 sameAs paper-41.
• paper-41 abstract "The last decade of research in ontology alignment has brought a variety of computational techniques to discover correspondences between ontologies. While the accuracy of automatic approaches has continuously improved, human contributions remain a key ingredient of the process: this input serves as a valuable source of domain knowledge that is used to train the algorithms and to validate and augment automatically computed alignments. In this paper, we introduce CROWDMAP, a model to acquire such human contributions via microtask crowdsourcing. For a given pair of ontologies, CROWDMAP translates the alignment problem into microtasks that address individual alignment questions, publishes the microtasks on an online labor market, and evaluates the quality of the results obtained from the crowd. We evaluated the current implementation of CROWDMAP in a series of experiments using ontologies and reference alignments from the Ontology Alignment Evaluation Initiative and the crowdsourcing platform CrowdFlower. The experiments clearly demonstrated that the overall approach is feasible, and can improve the accuracy of existing ontology alignment solutions in a fast, scalable, and cost-effective manner.".
• paper-41 hasAuthorList authorList.
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• paper-41 title "CrowdMAP: Crowdsourcing Ontology Alignment with Microtasks".
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• item-1 hasContent cristina-sarasua.
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• item-2 hasContent elena-simperl.
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• item-3 type ListItem.
• item-3 hasContent natasha-f-noy.
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• paper-01 type InProceedings.
• paper-01 label "On-the-fly Integration of Static and Dynamic Linked Data".
• paper-01 sameAs paper-01.
• paper-01 hasAuthorList authorList.
• paper-01 isPartOf proceedings.
• paper-01 title "On-the-fly Integration of Static and Dynamic Linked Data".
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• item-1 type ListItem.
• item-1 hasContent andreas-harth.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent craig-a-knoblock.

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