Difference between revisions of "Contextualization Using Hyperlinks and Internal Hierarchical Structure of Wikipedia Documents"
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| − | '''Contextualization Using Hyperlinks and Internal Hierarchical Structure of Wikipedia Documents''' - scientific work related to Wikipedia quality published in 2012, written by Muhammad Ali Norozi, Paavo Arvola and Arjen P. de Vries. | + | '''Contextualization Using Hyperlinks and Internal Hierarchical Structure of Wikipedia Documents''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Muhammad Ali Norozi]], [[Paavo Arvola]] and [[Arjen P. de Vries]]. |
== Overview == | == Overview == | ||
| − | Context surrounding hyperlinked semi-structured documents, externally in the form of citations and internally in the form of hierarchical structure, contains a wealth of useful but implicit evidence about a document's relevance. These rich sources of information should be exploited as contextual evidence. This paper proposes various methods of accumulating evidence from the context, and measures the effect of contextual evidence on retrieval effectiveness for document and focused retrieval of hyperlinked semi-structured documents. Authors propose a re-weighting model to contextualize (a) evidence from citations in a query-independent and query-dependent fashion (based on Markovian random walks) and (b) evidence accumulated from the internal tree structure of documents. The in-links and out-links of a node in the citation graph are used as external context, while the internal document structure provides internal, within-document context. Authors hypothesize that documents in a good context (having strong contextual evidence) should be good candidates to be relevant to the posed query, and vice versa. Authors tested several variants of contextualization and verified notable improvements in comparison with the baseline system and gold standards in the retrieval of full documents and focused elements. | + | Context surrounding hyperlinked semi-structured documents, externally in the form of citations and internally in the form of hierarchical structure, contains a wealth of useful but implicit evidence about a document's relevance. These rich sources of information should be exploited as contextual evidence. This paper proposes various methods of accumulating evidence from the context, and [[measures]] the effect of contextual evidence on retrieval effectiveness for document and focused retrieval of hyperlinked semi-structured documents. Authors propose a re-weighting model to contextualize (a) evidence from citations in a query-independent and query-dependent fashion (based on Markovian random walks) and (b) evidence accumulated from the internal tree structure of documents. The in-links and out-links of a node in the citation graph are used as external context, while the internal document structure provides internal, within-document context. Authors hypothesize that documents in a good context (having strong contextual evidence) should be good candidates to be relevant to the posed query, and vice versa. Authors tested several variants of contextualization and verified notable improvements in comparison with the baseline system and gold standards in the retrieval of full documents and focused elements. |
Revision as of 23:51, 6 July 2019
Contextualization Using Hyperlinks and Internal Hierarchical Structure of Wikipedia Documents - scientific work related to Wikipedia quality published in 2012, written by Muhammad Ali Norozi, Paavo Arvola and Arjen P. de Vries.
Overview
Context surrounding hyperlinked semi-structured documents, externally in the form of citations and internally in the form of hierarchical structure, contains a wealth of useful but implicit evidence about a document's relevance. These rich sources of information should be exploited as contextual evidence. This paper proposes various methods of accumulating evidence from the context, and measures the effect of contextual evidence on retrieval effectiveness for document and focused retrieval of hyperlinked semi-structured documents. Authors propose a re-weighting model to contextualize (a) evidence from citations in a query-independent and query-dependent fashion (based on Markovian random walks) and (b) evidence accumulated from the internal tree structure of documents. The in-links and out-links of a node in the citation graph are used as external context, while the internal document structure provides internal, within-document context. Authors hypothesize that documents in a good context (having strong contextual evidence) should be good candidates to be relevant to the posed query, and vice versa. Authors tested several variants of contextualization and verified notable improvements in comparison with the baseline system and gold standards in the retrieval of full documents and focused elements.
