DBpedia

Mapping queries to the Linking Open Data cloud: A case study using DBpedia

We introduce the task of mapping search engine queries to DBpedia, a major linking hub in the Linking Open Data cloud. We propose and compare various methods for addressing this task, using a mixture of information retrieval and machine learning techniques. Specifically, we present a supervised machine learning-based method to determine which concepts are intended by a user issuing a query. The concepts are obtained from an ontology and may be used to provide contextual information, related concepts, or navigational suggestions to the user submitting the query. Our approach first ranks candidate concepts using a language modeling for information retrieval framework. We then extract query, concept, and search-history feature vectors for these concepts. Using manual annotations we inform a machine learning algorithm that learns how to select concepts from the candidates given an input query. Simply performing a lexical match between the queries and concepts is found to perform poorly and so does using retrieval alone, i.e., omitting the concept selection stage. Our proposed method significantly improves upon these baselines and we find that support vector machines are able to achieve the best performance out of the machine learning algorithms evaluated.

  • [PDF] [DOI] E. Meij, M. Bron, L. Hollink, B. Huurnink, and M. de Rijke, “Mapping queries to the Linking Open Data cloud: a case study using DBpedia,” Web semantics: science, services and agents on the world wide web, vol. 9, iss. 4, pp. 418-433, 2011.
    [Bibtex]
    @article{JWS:2011:meij,
    Abstract = {We introduce the task of mapping search engine queries to DBpedia, a major linking hub in the Linking Open Data cloud. We propose and compare various methods for addressing this task, using a mixture of information retrieval and machine learning techniques. Specifically, we present a supervised machine learning-based method to determine which concepts are intended by a user issuing a query. The concepts are obtained from an ontology and may be used to provide contextual information, related concepts, or navigational suggestions to the user submitting the query. Our approach first ranks candidate concepts using a language modeling for information retrieval framework. We then extract query, concept, and search-history feature vectors for these concepts. Using manual annotations we inform a machine learning algorithm that learns how to select concepts from the candidates given an input query. Simply performing a lexical match between the queries and concepts is found to perform poorly and so does using retrieval alone, i.e., omitting the concept selection stage. Our proposed method significantly improves upon these baselines and we find that support vector machines are able to achieve the best performance out of the machine learning algorithms evaluated.},
    Author = {Edgar Meij and Marc Bron and Laura Hollink and Bouke Huurnink and Maarten de Rijke},
    Date-Added = {2011-11-25 08:45:19 +0100},
    Date-Modified = {2012-10-28 21:59:08 +0000},
    Doi = {10.1016/j.websem.2011.04.001},
    Issn = {1570-8268},
    Journal = {Web Semantics: Science, Services and Agents on the World Wide Web},
    Keywords = {Information retrieval},
    Number = {4},
    Pages = {418 - 433},
    Title = {Mapping queries to the {Linking Open Data} cloud: A case study using {DBpedia}},
    Url = {http://www.sciencedirect.com/science/article/pii/S1570826811000187},
    Volume = {9},
    Year = {2011},
    Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S1570826811000187},
    Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.websem.2011.04.001}}
Distribution of structured data embedded in XHTML

Investigating the Semantic Gap through Query Log Analysis

Significant efforts have focused in the past years on bringing large amounts of metadata online and the success of these efforts can be seen by the impressive number of web sites exposing data in RDFa or RDF/XML. However, little is known about the extent to which this data fits the needs of ordinary web users with everyday information needs. In this paper we study what we perceive as the semantic gap between the supply of data on the Semantic Web and the needs of web users as expressed in the queries submitted to a major Web search engine. We perform our analysis on both the level of instances and ontologies. First, we first look at how much data is actually relevant to Web queries and what kind of data is it. Second, we provide a generic method to extract the attributes that Web users are searching for regarding particular classes of entities. This method allows to contrast class definitions found in Semantic Web vocabularies with the attributes of objects that users are interested in. Our findings are crucial to measuring the potential of semantic search, but also speak to the state of the Semantic Web in general.

  • [PDF] P. Mika, E. Meij, and H. Zaragoza, “Investigating the semantic gap through query log analysis.,” in Proceedings of the 8th international semantic web conference, 2009.
    [Bibtex]
    @inproceedings{ISWC:2009:mika,
    Author = {Peter Mika and Edgar Meij and Hugo Zaragoza},
    Booktitle = {Proceedings of the 8th International Semantic Web Conference},
    Date-Added = {2011-10-12 18:31:55 +0200},
    Date-Modified = {2012-10-30 08:45:11 +0000},
    Series = {ISWC 2009},
    Title = {Investigating the Semantic Gap through Query Log Analysis.},
    Year = {2009},
    Bdsk-Url-1 = {http://dblp.uni-trier.de/db/conf/semweb/iswc2009.html#MikaMZ09}}