Exploiting DBpedia for web search results clustering

Schuhmacher, Michael; Ponzetto, Simone Paolo

doi:10.1145/2509558.2509574

Cited by 12 publications

(4 citation statements)

References 26 publications

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“…Наприклад, [10] пропонують використовувати онтології для інтерпретації ПМзапитів, перетворюючи на основі дескриптивної логіки набір ключових слів на кон'юнкцію понять. Інші дослідника [11] використовують об'єкти та семантичні зв'язки з бази знань DBpedia для групування результатів пошукової системи у більш значущі групи.…”

Section: пошук у природномовних ресурсахunclassified

A three-dimensional model of semantic search: queries, resources, and results

Rogushina

2023

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We propose three-dimensional model of semantic search that analyzes search requests, information resources (IRs) and search results. This model is proposed as an additional tool for describing and comparing information retrieval systems (IRSs) that use various elements of artificial intelligence and knowledge management for more effective and relevant satisfaction of user information needs. In this work we analyze existing approaches to the semanticization of search queries and the use of external knowledge sources for retrieval process. The values of parameters analyzed by this model are not mutually exclusive, that is, the same IRS can support several search options. More over, the representation means of queries and resources are not always comparable. The model makes it possible to identify IRSs with intersected triads «request-IR-result» and to perform their comparison precisely on these subclasses of search problems. This approach allows to select search algorithms that are more pertinent for specific user tasks and to choose on base of this selection appropriate retrieval services that provide information for further processing. An important feature of the proposed model is that it uses only those IRS characteristics that can be directly evaluated by retrieval users.

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Section: пошук у природномовних ресурсахunclassified

A three-dimensional model of semantic search: queries, resources, and results

Rogushina

2023

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“…Here it is clearly concluded that the DBpedia concept representation of clusters are in line with manually assigned cluster labels. Likewise, in (Schuhmacher and Ponzetto, 2013) web search results are processed with DBpedia Spotlight for snippet semantification and topic assignment leading to better quality of clusters formed. Hu et al (2009) present a method to cluster different sets of documents by generating document-category matrix built on top of Wikipedia term-concept matrix.…”

Section: Literature Surveymentioning

confidence: 99%

A Hierarchical Clustering Approach for DBpedia based Contextual Information of Tweets

Maravanthe¹,

Rao²,

Kanavalli³

et al. 2020

Journal of Computer Science

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The past decade has seen a tremendous increase in the adoption of Social Web leading to the generation of enormous amount of user data every day. The constant stream of tweets with an innate complex sentimental and contextual nature makes searching for relevant information a herculean task. Multiple applications use Twitter for various domain sensitive and analytical use-cases. This paper proposes a scalable context modeling framework for a set of tweets for finding two forms of metadata termed as primary and extended contexts. Further, our work presents a hierarchical clustering approach to find hidden patterns by using generated primary and extended contexts. Ontologies from DBpedia are used for generating primary contexts and subsequently to find relevant extended contexts. DBpedia Spotlight in conjunction with DBpedia Ontology forms the backbone for this proposed model. We consider both twitter trend and stream data to demonstrate the application of these contextual parts of information appropriate in clustering. We also discuss the advantages of using hierarchical clustering and information obtained from cutting dendrograms.

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“…One of the most used APIs is DBpedia Spotlight [54,55], which allows for automatically annotating text documents with DBpedia URIs. This tool is used in several LOD enabled data mining approaches, e.g., [56][57][58][59]. Several APIs for extracting semantic richness from text exist, like Alchemy API, 9 OpenCalais API, 10 Textwise 9 http://www.alchemyapi.com/api/.…”

Section: Using Lod To Interpret Unstructured Datamentioning

confidence: 99%

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Ristoski

Paulheim

2016

SSRN Journal

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a b s t r a c tData Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. This survey article gives a comprehensive overview of those approaches in different stages of the knowledge discovery process. As an example, we show how Linked Open Data can be used at various stages for building content-based recommender systems. The survey shows that, while there are numerous interesting research works performed, the full potential of the Semantic Web and Linked Open Data for data mining and KDD is still to be unlocked.

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Exploiting DBpedia for web search results clustering

Cited by 12 publications

References 26 publications

A three-dimensional model of semantic search: queries, resources, and results

A three-dimensional model of semantic search: queries, resources, and results

A Hierarchical Clustering Approach for DBpedia based Contextual Information of Tweets

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

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