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- item-1 hasContent jorge-martinez-gil.
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- item-2 hasContent enrique-alba.
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- item-3 hasContent jose-f-aldana-montes.
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- 3 creator alexander-hogenboom.
- 3 creator flavius-frasincar.
- 3 creator uzay-kaymak.
- 3 creator viorel-milea.
- 3 type InProceedings.
- 3 label "Genetic Algorithms for RDF Query Path Optimization".
- 3 sameAs 3.
- 3 abstract "In this paper we present an approach based on genetic algorithms for determining optimal RDF query paths. The performance of this approach is benchmarked against the performance of a two-phase optimization algorithm. For more complex queries, the genetic algorithm RDFGA generally outperforms two-phase optimization in solution quality, execution time needed, and consistency in performance. Setting a time limit improves the overall performance of RDFGA compared to two-phase optimization even more.".
- 3 hasAuthorList authorList.
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- 3 keyword "Genetic Algorithm".
- 3 keyword "Query optimization".
- 3 keyword "RDF".
- 3 title "Genetic Algorithms for RDF Query Path Optimization".
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- item-1 hasContent alexander-hogenboom.
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- item-2 hasContent viorel-milea.
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- item-3 hasContent flavius-frasincar.
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- item-4 hasContent uzay-kaymak.
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- 4 creator peyman-nasirifard.
- 4 type InProceedings.
- 4 label "Anatomy of a Semantic Virus".
- 4 sameAs 4.
- 4 abstract "In this position paper, I discuss a piece of malicious automated software that can be used by an individual or a group of users for submitting valid random noisy RDF-based data based on predefined schemas/ontologies to Semantic search engines. The result will undermine the utility of semantic searches. I did not implement the whole virus, but checked its feasibility. The open question is whether nature inspired reasoning can address such problems which are more related to information quality aspects.".
- 4 hasAuthorList authorList.
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- 4 keyword "common sense facts".
- 4 keyword "information quality".
- 4 keyword "reasoning".
- 4 keyword "semantic search engine".
- 4 keyword "trust".
- 4 title "Anatomy of a Semantic Virus".
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- item-1 hasContent peyman-nasirifard.
- 6 creator emil-st-chifu.
- 6 creator ioan-alfred-letia.
- 6 type InProceedings.
- 6 label "Text-Based Ontology Enrichment Using Hierarchical Self-organizing Maps".
- 6 sameAs 6.
- 6 abstract "The success of the Semantic Web research is dependent upon the construction of complete and reliable domain ontologies. In this paper we describe an unsupervised framework for domain ontology enrichment based on mining domain text corpora. Specifically, we enrich the hierarchical backbone of an existing ontology, i.e. its taxonomy, with new domain-specific concepts. The framework is based on an extended model of hierarchical self-organizing maps. As being founded on an unsupervised neural network architecture, the framework can be applied to different languages and domains. Terms extracted by mining a text corpus encode contextual content information, in a distributional vector space. The enrichment behaves like a classification of the extracted terms into the existing taxonomy by attaching them as hyponyms for the nodes of the taxonomy. The experiments reported are in the “Lonely Planet” tourism domain. The taxonomy and the corpus are the ones proposed in the PASCAL ontology learning and population challenge. The experimental results prove that the quality of the enrichment is improved by using semantics based vector representations for the classified (newly added) terms, like the document category histograms (DCH) and the document frequency times inverse term frequency (DF-ITF) weighting scheme.".
- 6 hasAuthorList authorList.
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- 6 keyword "document category histograms (DCH)".
- 6 keyword "document frequency times inverse term frequency (DF-ITF) weighting scheme".
- 6 keyword "extended growing hierarchical self-organizing maps (Enrich-GHSOM)".
- 6 keyword "taxonomy enrichment".
- 6 keyword "unsupervised neural network".
- 6 title "Text-Based Ontology Enrichment Using Hierarchical Self-organizing Maps".
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- item-1 hasContent emil-st-chifu.
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- item-2 hasContent ioan-alfred-letia.
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- 7 creator jose-quesada.
- 7 type InProceedings.
- 7 label "Human Similarity Theories for the Semantic Web".
- 7 sameAs 7.
- 7 abstract "The human mind has been designed to evaluate similarity fast and efficiently. When building/using a data format to make the web content more machine-friendly, can we learn something useful from how the mind represents data? We present four theories psychological theories that tried to solve the problem and how they relate to semantic web practices. Metric models (such as the vector space model and LSA) were the first-comers and still hold important advantages. advances in Bayesian methods pushed Feature models( e.g., Topics model). Structural mapping models propose that for similarity, shared structure matters more, although the formalisms that express these ideas are still developing. Transformational distance models (e.g., SP model) reduce similarity to information transmission. Topics and SP do not require preexisting classes but still have a long way to go; the need of automatically generating structure is less pressing when one of the driving forces of the semantic web is the creation of ontologies.".