Treo: Combining Entity-Search, Spreading Activation and Semantic Relatedness for Querying Linked Data. Freitas, A., Gabriel de Oliveira, J., O'Riain, S., Curry, E., & Carlos Pereira da Silva, J. In 1st Workshop on Question Answering over Linked Data (QALD-1), 2011.
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Paper abstract bibtex
Paper abstract bibtex This paper describes Treo, a natural language query mecha- nismfor Linked Data which focuses on the provision of a precise and scal- able semantic matching approach between natural language queries and distributed heterogeneous Linked Datasets. Treo’s semantic matching approach combines three key elements: entity search, a Wikipedia-based semantic relatedness measure and spreading activation search. While en- tity search allows Treo to cope with queries over high volume and dis- tributed data, the combination of entity search and spreading activation search using a Wikipedia-based semantic relatedness measure provides a flexible approach for handling the semantic match between natural language queries and Linked Data. Experimental results using the DB- Pedia QALD training query set showed that this combination represents a promising line of investigation, achieving a mean reciprocal rank of 0.489, precision of 0.395 and recall of 0.451.
@inproceedings{ Freitas2011c,
abstract = {This paper describes Treo, a natural language query mecha- nismfor Linked Data which focuses on the provision of a precise and scal- able semantic matching approach between natural language queries and distributed heterogeneous Linked Datasets. Treo’s semantic matching approach combines three key elements: entity search, a Wikipedia-based semantic relatedness measure and spreading activation search. While en- tity search allows Treo to cope with queries over high volume and dis- tributed data, the combination of entity search and spreading activation search using a Wikipedia-based semantic relatedness measure provides a flexible approach for handling the semantic match between natural language queries and Linked Data. Experimental results using the DB- Pedia QALD training query set showed that this combination represents a promising line of investigation, achieving a mean reciprocal rank of 0.489, precision of 0.395 and recall of 0.451.},
author = {Freitas, André and {Gabriel de Oliveira}, João and O'Riain, Sean and Curry, Edward and {Carlos Pereira da Silva}, João},
booktitle = {1st Workshop on Question Answering over Linked Data (QALD-1)},
file = {:Users/ed/Documents/Mendeley Desktop/2011_Freitas et al(3).pdf:pdf},
keywords = {Linked Data,Natural Language Queries},
title = {{Treo: Combining Entity-Search, Spreading Activation and Semantic Relatedness for Querying Linked Data}},
url = {http://www.edwardcurry.org/publications/Freitas_QALD_2011.pdf},
year = {2011}
}Downloads: 0
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Treo’s semantic matching approach combines three key elements: entity search, a Wikipedia-based semantic relatedness measure and spreading activation search. While en- tity search allows Treo to cope with queries over high volume and dis- tributed data, the combination of entity search and spreading activation search using a Wikipedia-based semantic relatedness measure provides a flexible approach for handling the semantic match between natural language queries and Linked Data. Experimental results using the DB- Pedia QALD training query set showed that this combination represents a promising line of investigation, achieving a mean reciprocal rank of 0.489, precision of 0.395 and recall of 0.451. -->\n<!-- </div> -->\n<!-- -->\n\n</div>\n","downloads":0,"bibbaseid":"freitas-gabrieldeoliveira-oriain-curry-carlospereiradasilva-treocombiningentitysearchspreadingactivationandsemanticrelatednessforqueryinglinkeddata-2011","urls":{"Paper":"http://www.edwardcurry.org/publications/Freitas_QALD_2011.pdf"},"role":"author","year":"2011","url":"http://www.edwardcurry.org/publications/Freitas_QALD_2011.pdf","type":"inproceedings","title":"Treo: Combining Entity-Search, Spreading Activation and Semantic Relatedness for Querying Linked Data","keywords":"Linked Data,Natural Language Queries","key":"Freitas2011c","id":"Freitas2011c","file":":Users/ed/Documents/Mendeley Desktop/2011_Freitas et al(3).pdf:pdf","booktitle":"1st Workshop on Question Answering over Linked Data (QALD-1)","bibtype":"inproceedings","bibtex":"@inproceedings{ Freitas2011c,\n abstract = {This paper describes Treo, a natural language query mecha- nismfor Linked Data which focuses on the provision of a precise and scal- able semantic matching approach between natural language queries and distributed heterogeneous Linked Datasets. Treo’s semantic matching approach combines three key elements: entity search, a Wikipedia-based semantic relatedness measure and spreading activation search. While en- tity search allows Treo to cope with queries over high volume and dis- tributed data, the combination of entity search and spreading activation search using a Wikipedia-based semantic relatedness measure provides a flexible approach for handling the semantic match between natural language queries and Linked Data. Experimental results using the DB- Pedia QALD training query set showed that this combination represents a promising line of investigation, achieving a mean reciprocal rank of 0.489, precision of 0.395 and recall of 0.451.},\n author = {Freitas, André and {Gabriel de Oliveira}, João and O'Riain, Sean and Curry, Edward and {Carlos Pereira da Silva}, João},\n booktitle = {1st Workshop on Question Answering over Linked Data (QALD-1)},\n file = {:Users/ed/Documents/Mendeley Desktop/2011_Freitas et al(3).pdf:pdf},\n keywords = {Linked Data,Natural Language Queries},\n title = {{Treo: Combining Entity-Search, Spreading Activation and Semantic Relatedness for Querying Linked Data}},\n url = {http://www.edwardcurry.org/publications/Freitas_QALD_2011.pdf},\n year = {2011}\n}","author_short":["Freitas, A.","Gabriel de Oliveira, J.","O'Riain, S.","Curry, E.","Carlos Pereira da Silva, J."],"author":["Freitas, André","Gabriel de Oliveira, João","O'Riain, Sean","Curry, Edward","Carlos Pereira da Silva, João"],"abstract":"This paper describes Treo, a natural language query mecha- nismfor Linked Data which focuses on the provision of a precise and scal- able semantic matching approach between natural language queries and distributed heterogeneous Linked Datasets. 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