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

• authorList hasLastItem item-6.
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• item-1 hasContent kouji-kozaki.
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• item-2 hasContent hiroko-kou.
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• item-3 type ListItem.
• item-3 hasContent yuki-yamagata.
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• item-4 hasContent takeshi-imai.
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• item-5 hasContent kazuhiko-ohe.
• item-5 next item-6.
• item-5 previous item-4.
• item-6 type ListItem.
• item-6 hasContent riichiro-mizoguchi.
• item-6 previous item-5.
• paper-05 type InProceedings.
• paper-05 label "Building Large Scale Relation KB from Text".
• paper-05 sameAs paper-05.
• paper-05 abstract "Recently more and more structured data in form of RDF triples have been published and integrated into Linked Open Data (LOD). While the current LOD contains hundreds of data sources with billions of triples, it has a small number of distinct relations compared with the large number of entities. On the other hand, Web pages are growing rapidly, which results in much larger number of textual contents to be exploited. With the popularity and wide adoption of open information extraction technology, extracting entities and relations among them from text at the Web scale is possible. In this paper, we present an approach to extract the subject individuals and the object counterparts for the relations from text and determine the most appropriate domain and range as well as the most confident dependency path patterns for the given relation based on the EM algorithm. As a preliminary results, we built a knowledge base for relations extracted from Chinese encyclopedias. The experimental results show the effectiveness of our approach to extract relations with reasonable domain, range and path pattern restrictions as well as high-quality triples.".
• paper-05 hasAuthorList authorList.
• paper-05 isPartOf poster-demo-proceedings.
• paper-05 title "Building Large Scale Relation KB from Text".
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• authorList hasLastItem item-3.
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• item-1 hasContent junfeng-pan.
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• item-2 hasContent haofen-wang.
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• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent yong-yu.
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• paper-06 type InProceedings.
• paper-06 label "Creating Enriched YouTube Media Fragments With NERD Using Timed-Text".
• paper-06 sameAs paper-06.
• paper-06 abstract "This demo enables the automatic creation of semantically annotated YouTube media fragments. A video is first ingested in the Synote system and a new method enables to retrieve its associated subtitles or closed captions. Next, NERD is used to extract named entities from the transcripts which are then temporally aligned with the video. The entities are disambiguated in the LOD clound and a user interface enables to browse through the entities detected in a video or get more information. We evaluated our application with 60 videos from 3 YouTube channels.".
• paper-06 hasAuthorList authorList.
• paper-06 isPartOf poster-demo-proceedings.
• paper-06 title "Creating Enriched YouTube Media Fragments With NERD Using Timed-Text".
• authorList type List.
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• item-1 hasContent yunjia-li.
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• item-2 type ListItem.
• item-2 hasContent giuseppe-rizzo.
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• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent raphaeel-troncy.
• item-3 previous item-2.
• paper-07 type InProceedings.
• paper-07 label "Demo: Efficient Human Attention Detection in Museums based on Semantics and Complex Event Processing".
• paper-07 sameAs paper-07.
• paper-07 abstract "In this paper we present a demo for efficient detecting of visitor’s attention in museum environment based on the application of intelligent complex event processing and semantic technologies. The detection takes advantage of semantics: (i) in design time for the correlation of sensors’ data via modeling of the interesting situations and annotation of artworks and their parts and (ii) in real-time for the more accurate and precise detection of the interesting situation. The results of the proposed approach have been applied in the EU project ARtSENSE.".
• paper-07 hasAuthorList authorList.
• paper-07 isPartOf poster-demo-proceedings.
• paper-07 title "Demo: Efficient Human Attention Detection in Museums based on Semantics and Complex Event Processing".
• authorList type List.
• authorList hasFirstItem item-1.
• authorList hasItem item-1.
• authorList hasItem item-2.
• authorList hasItem item-3.
• authorList hasItem item-4.
• authorList hasLastItem item-4.
• item-1 type ListItem.
• item-1 hasContent yongchun-xu.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent nenad-stojanovic.
• item-2 next item-3.
• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent ljiljana-stojanovic.
• item-3 next item-4.
• item-3 previous item-2.
• item-4 type ListItem.
• item-4 hasContent tobias-schuchert.
• item-4 previous item-3.
• paper-08 type InProceedings.
• paper-08 label "Demonstrating Blank Node Matching and RDF/S Comparison Functions".
• paper-08 sameAs paper-08.

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