Exploiting Wikipedia for Information Retrieval Tasks

Shapira, Bracha; Ofek, Nir; Makarenkov, Victor

doi:10.1145/2766462.2767879

Cited by 6 publications

(5 citation statements)

References 32 publications

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“…However, Wikipedia, a wide-coverage collaboratively edited encyclopaedia, is utilised in information retrieval, disambiguation, recommender systems, ranking and knowledge acquisition and validation as a knowledge source [13]. Several attempts have been made to filter tags according to the existence of Wikipedia articles [14–16].…”

Section: Related Workmentioning

confidence: 99%

“…In order to overcome the aforementioned problems, various approaches utilising occurrence frequency, probability, statistics or knowledge bases have been proposed for tag recommendations [2,6–20]. The common goal of current works is to increase the understanding of images and reinforce the efficiency and reliability of tag-based image retrieval.…”

Section: Introductionmentioning

confidence: 99%

“…5 Wikipedia is an online encyclopaedia that has a huge number of articles and rich information about the relation structures in the form of categories, infoboxes and interwiki links [21]. Wikipedia is an example of collective intelligence and is used as a knowledge base in many studies [13,22–25]. Therefore, we created a set of associated words that were semantically similar using Wikipedia as a knowledge base, and we exploited associated words based on Wikipedia to more effectively recommend tags.…”

Section: Introductionmentioning

confidence: 99%

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Semantic tag recommendation based on associated words exploiting the interwiki links of Wikipedia

Hong

Kim

Lee

2017

Journal of Information Science

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The volumes of multimedia content and users have increased on social multimedia sites due to the prevalence of smart mobile devices and digital cameras. It is common for users to take pictures and upload them to image-sharing websites using their smartphones. However, the tag characteristics deteriorate the quality of tag-based image retrieval and decrease the reliability of social multimedia sites. In this article, we propose a semantic tag recommendation technique exploiting associated words that are semantically similar or related to each other using the interwiki links of Wikipedia. First, we generate a word relationship graph after extracting meaningful words from each article in Wikipedia. The candidate words are then rearranged according to importance by applying a link-based ranking algorithm and then the top-k words are defined as the associated words for the article. When a user uploads an image, we collect visually similar images from a social image database. After propagating the proper tags from the collected images, we recommend associated words related to the candidate tags. Our experimental results show that the proposed method can improve the accuracy by up to 14% compared with other works and that exploiting associated words makes it possible to perform semantic tag recommendation.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semantic tag recommendation based on associated words exploiting the interwiki links of Wikipedia

Hong

Kim

Lee

2017

Journal of Information Science

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“…Semantic annotation or ontology-based semantic tagging is an important component in Semantic Web that can certainly bring significant benefits to many text mining tasks, such as information retrieval [14] and text classification [15]. Thus, several attempts have been made to address this issue.…”

Section: Related Workmentioning

confidence: 99%

Semantic Tagging Using Topic Models Exploiting Wikipedia Category Network

Allahyari

Kochut

2016

2016 IEEE Tenth International Conference on Semantic Computing (ICSC)

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In this paper we propose a probabilistic topic model that incorporates DBpedia knowledge into the topic model for tagging Web pages and online documents with topics discovered in them. Our method is based on integration of the DBpedia hierarchical category network with statistical topic models where DBpedia categories are considered as topics. We have conducted extensive experiments on two different datasets to demonstrate the effectiveness of our method.

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“…Deveaud et al (2012) proposed two query expansion approaches involving Wikipedia as an external source of information for book search. Shapira et al (2015) created a taxonomy describing the application domains of text analytics in which Wikipedia can be utilized and referenced relevant studies in each domain.…”

Section: Introductionmentioning

confidence: 99%

Improving the visibility of library resources via mapping library subject headings to Wikipedia articles

Joorabchi

Mahdi

2017

LHT

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Purpose-Linking libraries and Wikipedia can significantly improve the quality of services provided by these two major silos of knowledge. Such linkage would enrich the quality of Wikipedia articles and at the same time increase the visibility of library resources. To this end, this work describes the design and development of a software system for automatic mapping of FAST subject headings, used to index library materials, to their corresponding articles in Wikipedia. Design/methodology/approach-The proposed system works by first detecting all the candidate Wikipedia concepts (articles) occurring in the titles of the books and other library materials which are indexed with a given FAST subject heading. This is then followed by training and deploying a Machine Learning (ML) algorithm designed to automatically identify those concepts that correspond to the FAST heading. The ML algorithm used is a binary classifier which classifies the candidate concepts into either "corresponding" or "non-corresponding" categories. The classifier is trained to learn the characteristics of those candidates which have the highest probability of belonging to the "corresponding" category based on a set of fourteen positional, statistical, and semantic features. Findings-We have assessed the performance of the developed system using standard information retrieval measures of precision, recall, and F-score on a dataset containing 200 FAST subject headings manually mapped to their corresponding Wikipedia articles. The evaluation results show that the developed system is capable of achieving Fscores as high as 0.65 and 0.99 in the corresponding and non-corresponding categories respectively. Research limitations/implications-The size of the dataset used to evaluate the performance of the system is rather small. However, we believe the developed dataset is large enough to demonstrate the feasibility and scalability of the proposed approach. Practical implications-The sheer size of English Wikipedia makes the manual mapping of Wikipedia articles to library subject headings a very labour-intensive and time consuming task. Therefore, our aim is to reduce the cost of such mapping and integration. Social Implications-The proposed mapping paves the way for connecting libraries and Wikipedia as two major silos of knowledge, and enables the bi-directional movement of users between the two. Originality/value-To the best of our knowledge, the current work is the first attempt at automatic mapping of Wikipedia to a library controlled vocabulary.

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Exploiting Wikipedia for Information Retrieval Tasks

Cited by 6 publications

References 32 publications

Semantic tag recommendation based on associated words exploiting the interwiki links of Wikipedia

Semantic tag recommendation based on associated words exploiting the interwiki links of Wikipedia

Semantic Tagging Using Topic Models Exploiting Wikipedia Category Network

Improving the visibility of library resources via mapping library subject headings to Wikipedia articles

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