Matches in ScholarlyData for { ?s ?p ?o. }
- 1 abstract "Efficiently detecting near duplicate resources is an important task when integrating information from various sources and applications. Once detected, near duplicate resources can be grouped together, merged, or removed, in order to avoid repetition and redundancy, and to increase the diversity in the information provided to the user. In this paper, we introduce an approach for efficient semantic-aware near duplicate detection, by combining indexing schemes for similarity search with the RDF representations of the resources. We provide a probabilistic analysis for the correctness of the suggested approach, which allows applications to configure it for satisfying their specific quality requirements. Our experimental evaluation on the RDF descriptions of real-world news articles from various news agencies, demonstrates the efficiency and the effectiveness of our approach.".
- 1 hasAuthorList authorList.
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- 1 keyword "data integration".
- 1 keyword "near duplicate detection".
- 1 title "Efficient Semantic-Aware Detection of Near Duplicate Resources".
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- item-1 hasContent ekaterini-ioannou.
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- item-2 hasContent odysseas-papapetrou.
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- item-3 hasContent dimitrios-skoutas.
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- item-4 hasContent wolfgang-nejdl.
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- 29 creator paola-monachesi.
- 29 creator thomas-markus.
- 29 type InProceedings.
- 29 label "Using social media for ontology enrichment".
- 29 sameAs 29.
- 29 abstract "In order to support informal learning, we complement the formal knowledge represented by ontologies developed by domain experts with the informal knowledge emerging from social tagging. To this end, we have developed an ontology enrichment pipeline that can automatically enrich a domain ontology using: data extracted by a crawler from social media applications, similarity measures, the DBpedia knowledge base, a disambiguation algorithm and several heuristics. The main goal is to provide dynamic and personalized domain ontologies that include the knowledge of the community of users. They will support a more personalized learning experience able to fulfill the needs of different types of learners.".
- 29 hasAuthorList authorList.
- 29 isPartOf proceedings.
- 29 keyword "eLearning".
- 29 keyword "ontologies".
- 29 keyword "similarity measures".
- 29 keyword "social media applications".
- 29 keyword "tag disambiguation".
- 29 title "Using social media for ontology enrichment".
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- item-1 hasContent paola-monachesi.
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- 30 creator philipp-kaerger.
- 30 creator wolf-siberski.
- 30 type InProceedings.
- 30 label "Guarding a Walled Garden - semantic privacy preferences for the social web".
- 30 sameAs 30.
- 30 abstract "With increasing usage of Social Networks, the ability of users to establish access restrictions on their data and resources becomes more and more important. However, privacy preferences in nowadays Social Network applications, are rather limited and do not allow to define policies with fine-grained concept definitions. Moreover, due to the walled garden structure of the Social Web, current privacy settings for one platform cannot to refer to information about people on other platforms. In addition, although most of the Social Network''s privacy setting share the same nature, users are forced to define and maintain their privacy settings separately for each platform. In this paper, we present a semantic model for privacy preferences on Social Web applications that overcomes those problems. Our model extends the current privacy model for Social Platforms by semantic concept definitions. By means of those concepts, users are enabled to exactly define what portion of their profile or which resources they want to protect and which user category is allowed to see those parts. Such category definitions are not limited to one single platform but can refer to information from other platforms as well. We show how this model can be implemented as extension of the OpenSocial standard, to enable advanced privacy settings that can be exchanged among OpenSocial platforms.".
- 30 hasAuthorList authorList.
- 30 isPartOf proceedings.
- 30 keyword "OpenSocial".
- 30 keyword "Privacy preferences".
- 30 keyword "Semantic Web policies".
- 30 keyword "Social Web".
- 30 keyword "privacy".
- 30 title "Guarding a Walled Garden - semantic privacy preferences for the social web".
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- item-1 hasContent philipp-kaerger.
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- 33 creator aldo-gangemi.
- 33 creator alfio-massimiliano-gliozzo.
- 33 creator claudio-giuliano.
- 33 creator katerina-tymoshenko.
- 33 type InProceedings.
- 33 label "Acquiring Thesauri from Wikis by Exploiting Domain Models and Lexical Substitution".
- 33 sameAs 33.
- 33 abstract "Acquiring structured data from wikis is a problem of increasing interest in knowledge engineering and semantic web. In fact, collaboratively developed resources are growing in time, have high quality and are constantly updated. A very promising approach to this aim is extracting thesauri from wikis. A thesaurus is a work that lists words grouped together according to similarity of meaning, generally organized by synonyms. Thesauri are very useful for a large variety of applications, including information retrieval and knowledge engineering. Most information in wikis is expressed by means of natural language texts and internal links among web pages, the so called wikilinks. In this paper, an innovative method for inducing thesauri from Wikipedia is presented. It leverages on the Wikipedia structure to extract concepts and terms denoting them, obtaining a thesaurus that can be profitably used into applications. To boost precision and avoid noise, we apply word sense disambiguation techniques for lexical substitution and latent semantic analysis. In the paper, we show how to represent the extracted results following an RDF/OWL schema that can be published in the semantic web.".
- 33 hasAuthorList authorList.
- 33 isPartOf proceedings.
- 33 keyword "Natural Language Processing".
- 33 keyword "Thesauri".
- 33 keyword "Wikipedia Mining".
- 33 keyword "Word Sense Disambiguation".
- 33 title "Acquiring Thesauri from Wikis by Exploiting Domain Models and Lexical Substitution".
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