Matches in ScholarlyData for { ?s ?p ?o. }
- item-3 hasContent nicola-fanizzi.
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- item-4 hasContent floriana-esposito.
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- 191 creator genevieve-gorrell.
- 191 creator johann-petrak.
- 191 creator kalina-bontcheva.
- 191 type InProceedings.
- 191 label "LOD-based Disambiguation of Named Entities in @tweets through Context #enrichment".
- 191 sameAs 191.
- 191 abstract "Social media -- and microblogs in particular -- have emerged as high-value, high-volume content, which organisations increasingly wish to analyse automatically. These short, noisy, context-dependent, stylized and dynamic texts currently present a significant challenge for semantic annotation techniques, such as ontology-based named entity disambiguation. State-of-the-art systems, however, have largely ignored the richer, microblog-specific context, which humans draw upon when interpreting these short messages. This paper focuses specifically on quantifying the impact on entity disambiguation performance when readily available contextual information is included from URL content, hash tag definitions, and Twitter user profiles. In particular, including URL content significant improves the performance of a state-of-the-art DBpedia-based entity linking system. Similarly, user profile information for @mentions improves recall by over 10% with no adverse impact on precision. Another contribution lies in a new, publicly available corpus of tweets, which have been hand-annotated with DBpedia URIs, with high inter-annotator agreement.".
- 191 hasAuthorList authorList.
- 191 isPartOf proceedings.
- 191 keyword "Entity linking".
- 191 keyword "Information Extraction for Social Media Mining".
- 191 keyword "Semantic annotation exploiting linked data".
- 191 keyword "entity disambiguation using Linked Open Data".
- 191 keyword "ontology-based information extraction".
- 191 title "LOD-based Disambiguation of Named Entities in @tweets through Context #enrichment".
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- item-1 hasContent genevieve-gorrell.
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- item-2 hasContent johann-petrak.
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- item-3 hasContent kalina-bontcheva.
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- 20 creator achim-rettinger.
- 20 creator artem-schumilin.
- 20 creator basil-ell.
- 20 creator steffen-thoma.
- 20 type InProceedings.
- 20 label "Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text".
- 20 sameAs 20.
- 20 abstract "Learning cross-lingual semantic representations of relations from textual data is useful for tasks like cross-lingual information retrieval and question answering. So far, research has been mainly focused on cross-lingual entity linking, which is confined to linking between phrases in a text document and their corresponding entities in a knowledge base but cannot link to relations. In this paper, we present an approach for inducing clusters of semantically-related relations expressed in text, where relation clusters i) can be extracted from text of different languages, ii) are embedded in a semantic representation of the context, and iii) can be linked across languages to properties in a knowledge base. This is achieved by combining multi-lingual semantic role labeling (SRL) with cross-lingual entity linking followed by spectral clustering of the annotated SRL graphs. With our initial implementation we learned a cross-lingual library of relations from English and Spanish Wikipedia articles. To demonstrate its usefulness we apply it to cross-lingual question answering over linked data.".
- 20 hasAuthorList authorList.
- 20 isPartOf proceedings.
- 20 keyword "Cross-lingual Relation Clustering".
- 20 keyword "Relation Linking".
- 20 keyword "Unsupervised Relation Extraction".
- 20 title "Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text".
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- item-1 hasContent achim-rettinger.
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- item-2 hasContent artem-schumilin.
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- item-3 hasContent steffen-thoma.
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- item-4 hasContent basil-ell.
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- 26 creator marcin-wylot.
- 26 creator philippe-cudre-mauroux.
- 26 creator ruslan-mavlyutov.
- 26 type InProceedings.
- 26 label "A Comparison of Data Structures to Manage URIs on the Web of Data".
- 26 sameAs 26.
- 26 abstract "Uniform Resource Identifiers (URIs) are one of the corner stones of the Web; They are also exceedingly important on the Web of data, since RDF graphs and Linked Data both heavily rely on URIs to uniquely identify and connect online entities. Due to their hierarchical structure and their string serialization, sets of related URIs typically contain a high degree of redundant information and are systematically dictionary-compressed or encoded at the back-end (e.g., in the triple store). The paper represents, to the best of our knowledge, the first systematic comparison of the most common data structures used to encode URI data. We evaluate a series of data structures in term of their read/write performance and memory consumption.".
- 26 hasAuthorList authorList.
- 26 isPartOf proceedings.
- 26 keyword "Benchmarking".
- 26 keyword "Data Structures".
- 26 keyword "Dictionary Encoding".
- 26 keyword "URIs".
- 26 keyword "Web of Data".
- 26 title "A Comparison of Data Structures to Manage URIs on the Web of Data".
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- item-1 hasContent ruslan-mavlyutov.
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- item-2 hasContent marcin-wylot.
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