Difference between revisions of "Semantic Content Filtering with Wikipedia and Ontologies"
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| − | '''Semantic Content Filtering with Wikipedia and Ontologies''' - scientific work related to Wikipedia quality published in 2010, written by Pekka Malo, Pyry-Antti Siitari, Oskar Ahlgren, Jyrki Wallenius and Pekka Korhonen. | + | '''Semantic Content Filtering with Wikipedia and Ontologies''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Pekka Malo]], [[Pyry-Antti Siitari]], [[Oskar Ahlgren]], [[Jyrki Wallenius]] and [[Pekka Korhonen]]. |
== Overview == | == Overview == | ||
| − | The use of domain knowledge is generally found to improve query efficiency in content filtering applications. In particular, tangible benefits have been achieved when using knowledge-based approaches within more specialized fields, such as medical free texts or legal documents. However, the problem is that sources of domain knowledge are time consuming to build and equally costly to maintain. As a potential remedy, recent studies on Wikipedia suggest that this large body of socially constructed knowledge can be effectively harnessed to provide not only facts but also accurate information about semantic concept-similarities. This paper describes a ramework for document filtering, where Wikipedia’s concept relatedness information is combined with a domain ontology to produce semantic content classifiers. The approach is evaluated using Reuters RCV1 corpus and TREC-11 filtering task definitions. In a comparative study, the approach shows robust performance and appears to outperform content classifiers based on Support Vector Machines (SVM) and C4.5 algorithm. | + | The use of domain knowledge is generally found to improve query efficiency in content filtering applications. In particular, tangible benefits have been achieved when using knowledge-based approaches within more specialized fields, such as medical free texts or legal documents. However, the problem is that sources of domain knowledge are time consuming to build and equally costly to maintain. As a potential remedy, recent studies on [[Wikipedia]] suggest that this large body of socially constructed knowledge can be effectively harnessed to provide not only facts but also accurate information about semantic concept-similarities. This paper describes a ramework for document filtering, where Wikipedia’s concept [[relatedness]] information is combined with a domain [[ontology]] to produce semantic content classifiers. The approach is evaluated using Reuters RCV1 corpus and TREC-11 filtering task definitions. In a comparative study, the approach shows robust performance and appears to outperform content classifiers based on Support Vector Machines (SVM) and C4.5 algorithm. |
Revision as of 08:01, 6 June 2019
Semantic Content Filtering with Wikipedia and Ontologies - scientific work related to Wikipedia quality published in 2010, written by Pekka Malo, Pyry-Antti Siitari, Oskar Ahlgren, Jyrki Wallenius and Pekka Korhonen.
Overview
The use of domain knowledge is generally found to improve query efficiency in content filtering applications. In particular, tangible benefits have been achieved when using knowledge-based approaches within more specialized fields, such as medical free texts or legal documents. However, the problem is that sources of domain knowledge are time consuming to build and equally costly to maintain. As a potential remedy, recent studies on Wikipedia suggest that this large body of socially constructed knowledge can be effectively harnessed to provide not only facts but also accurate information about semantic concept-similarities. This paper describes a ramework for document filtering, where Wikipedia’s concept relatedness information is combined with a domain ontology to produce semantic content classifiers. The approach is evaluated using Reuters RCV1 corpus and TREC-11 filtering task definitions. In a comparative study, the approach shows robust performance and appears to outperform content classifiers based on Support Vector Machines (SVM) and C4.5 algorithm.
