Two Is Bigger (and Better) Than One: the Wikipedia Bitaxonomy Project

Flati, Tiziano; Vannella, Daniele; Pasini, Tommaso; Navigli, Roberto

doi:10.3115/v1/p14-1089

Cited by 61 publications

(63 citation statements)

References 27 publications

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“…All annotated resources, demos of the games, and a live version of the top-ranking items for each concept are currently available online. 5 In the future we will apply our video games to the validation of more data, such as the new Wikipedia bitaxonomy (Flati et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

Validating and Extending Semantic Knowledge Bases using Video Games with a Purpose

Vannella

Jurgens

Scarfini

et al. 2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Large-scale knowledge bases are important assets in NLP. Frequently, such resources are constructed through automatic mergers of complementary resources, such as WordNet and Wikipedia. However, manually validating these resources is prohibitively expensive, even when using methods such as crowdsourcing. We propose a cost-effective method of validating and extending knowledge bases using video games with a purpose. Two video games were created to validate conceptconcept and concept-image relations. In experiments comparing with crowdsourcing, we show that video game-based validation consistently leads to higher-quality annotations, even when players are not compensated.

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Section: Resultsmentioning

confidence: 99%

Validating and Extending Semantic Knowledge Bases using Video Games with a Purpose

Vannella

Jurgens

Scarfini

et al. 2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Self Cite

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“…Previous work on the construction of a taxonomy of IsA relations (Flati et al, 2014;de Melo and Weikum, 2010;Kozareva and Hovy, 2010;Ponzetto and Strube, 2007;Ponzetto and Navigli, 2009) considers that task to be different than extracting a flat set of IsA relations from text in practice. Challenges specific to taxonomy construction include overall concept positioning and how to discover whether concepts are unrelated, subordinated or parallel to each other (Kozareva and Hovy, 2010); the need to refine and enrich the taxonomy (Flati et al, 2014); the difficulty in adding relevant IsA relations towards the top of the taxonomy (Ponzetto and Navigli, 2009); eliminating cycles and inconsistencies (Ponzetto and Navigli, 2009;Kozareva and Hovy, 2010 Hoffart et al (2013) rely on data within human-curated resources, our work operates over unstructured text. Resources constructed in Bizer et al (2009); Nastase and Strube (2013); Hoffart et al (2013) contain not just a taxonomy of IsA relations, but also relation types other than IsA.…”

Section: Related Workmentioning

confidence: 99%

Identifying 1950s American Jazz Musicians: Fine-Grained IsA Extraction via Modifier Composition

Pavlick

Paşca²

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

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We present a method for populating fine-grained classes (e.g., "1950s American jazz musicians") with instances (e.g., Charles Mingus). While stateof-the-art methods tend to treat class labels as single lexical units, the proposed method considers each of the individual modifiers in the class label relative to the head. An evaluation on the task of reconstructing Wikipedia category pages demonstrates a >10 point increase in AUC, over a strong baseline relying on widely-used Hearst patterns.

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“…Both BabelNet and Babelfy are available online. 3 -WiBi WiBi (Flati et al 2014), a freely available taxonomy of Wikipedia pages aligned to a taxonomy of categories automatically and iteratively extracted from the text of a Wikipedia page. In WiBi, Wikipedia categories are disambiguated with WordNet synsets, and then pruned and restructured to form a taxonomy of multilingual senses.…”

Section: Resourcesmentioning

confidence: 99%

“…The interested reader is invited to read the WiBi paper(Flati et al 2014) for additional details.Soc. Netw.…”

mentioning

confidence: 99%

Recommendation of microblog users based on hierarchical interest profiles

Faralli

Stilo

Velardi

2015

Soc. Netw. Anal. Min.

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Quite a number of recent works have concentrated on the task of recommending to Twitter users whom they should follow, among which, the WTF (Who To Follow) service provided by Twitter. Recommenders are based, either on the user's network structure, or on some notion of topical similarity with other users, or on both. In this paper, we propose to accomplish the recommendation task in two steps: First, we profile users and classify them as belonging to a target community (depending e.g., on their political affiliation, preferred football team, favorite coffee shop, etc.). Then, we fine-tune recommendations for selected populations. We cast both problems of user classification and recommendation as one of itemset mining, where items are either users' authoritative friends or semantic categories associated to friends, extracted from WiBi, the Wikipedia Bitaxonomy. In addition to evaluating our profiler and recommender on several populations, we also show that semantic categories allow for very finegrained population studies, and make it possible to recommend not only whom to follow, but also topics of interest, users interested in the same topic, and more.

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Two Is Bigger (and Better) Than One: the Wikipedia Bitaxonomy Project

Cited by 61 publications

References 27 publications

Validating and Extending Semantic Knowledge Bases using Video Games with a Purpose

Validating and Extending Semantic Knowledge Bases using Video Games with a Purpose

Identifying 1950s American Jazz Musicians: Fine-Grained IsA Extraction via Modifier Composition

Recommendation of microblog users based on hierarchical interest profiles

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