Resolving Range Violations in DBpedia

Lertvittayakumjorn, Piyawat; Kertkeidkachorn, Natthawut; Ichise, Ryutaro

doi:10.1007/978-3-319-70682-5_8

Cited by 12 publications

(12 citation statements)

References 15 publications

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“…These methods ensure KB consistency, but they can neither correct the knowledge itself nor deal with those wrong assertions that satisfy the constraints. Lertvittayakumjorn et al [20] and Melo et al [21] both correct assertions by replacing the objects or subjects with correct entities. The former found the substitute by either keyword matching or a simple graph structure metric, while the latter first retrieved candidate substitutes from the Wikipedia disambiguation page (which may not exist, especially for KBs that are not based on Wikipedia) and then ranked them by lexical similarity.…”

Section: Assertion Correctionmentioning

confidence: 99%

“…Lertvittayakumjorn et al [20] and Melo et al [21] found that most erroneous assertions are due to confusion or lexical similarity leading to entity misuse; for example confusion between Manchester_United and Manchester_City, two football clubs based in Manchester, UK, can lead to facts about Manchester_United being incorrectly asserted about Manchester_City. Such errors are common not only in general KBs like DBpedia and Wikidata but also in domain KBs like the medical KB used in our evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Correcting Knowledge Base Assertions

Chen

Horrocks

et al. 2020

Proceedings of the Web Conference 2020

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The usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or semantic confusion. We study the problem of correcting such assertions, and present a general correction framework which combines lexical matching, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated using DBpedia and an enterprise medical KB.

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Section: Assertion Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correcting Knowledge Base Assertions

Chen

Horrocks

et al. 2020

Proceedings of the Web Conference 2020

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“…Knowledge Graph assessment can differ along different quality dimensions (Batini et al 2009;Zaveri et al 2013). For example, Paulheim and Bizer (2013) tackle the identification of missing instance assertions, Fürber and Hepp (2010a) identify wrong and missing property value assertions, and Lertvittayakumjorn et al (2017) address the identification of wrong property value assertions. The approach presented by Mendes et al (2012) defines the additional quality assessment methods.…”

Section: Knowledge Assessmentmentioning

confidence: 99%

Semantic Web Empowered E-Tourism

Angele¹,

Fensel²,

Huaman³

et al. 2020

Handbook of E-Tourism

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Smart speakers such as Alexa and later Google Home have introduced Artificial Intelligence (AI) into millions, soon to be billions of households, making AI an everyday experience. These new communication channels present a new challenge for successful e-Marketing and e-Commerce providers. Data, content, and services are becoming semantically annotated, allowing software agents, socalled bots, to search through the web and understand its content. Nowadays, users typically consult their bot to find, aggregate, and personalize information and to reserve, book, or buy products and services. As a consequence, it is becoming increasingly important for touristic providers of information, products, and services to be prominently visible in these new online channels to ensure their future economic maturity. In our chapter, we survey the methods and tools helping to achieve these goals. The core aim is the development and application of machine-processable (semantic) annotations of content, data, and services, as well as their aggregation in large Knowledge Graphs. It is only through these methods bots are able to answer a question in a knowledgeable way and organize a useful dialogue (Knowledge Graphs in Use A significantly extended and generalized version of this article will appear as D.

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“…We select to maintain DBpedia with our platform because DBpedia is widely used in the knowledge graph community. However, DBpedia contains many erroneous triples due to the extraction process and incompleteness of infoboxes [8]. We added a knowledge graph refinement module [8] on UWKGM to clean and maintain DBpedia.…”

Section: Use Casesmentioning

confidence: 99%

“…However, DBpedia contains many erroneous triples due to the extraction process and incompleteness of infoboxes [8]. We added a knowledge graph refinement module [8] on UWKGM to clean and maintain DBpedia. In the future, we could integrate new release DBpedia to our modified DBpedia by comparing data under version control of UWKGM.…”

Section: Use Casesmentioning

confidence: 99%

Uwkgm

Kertkeidkachorn

Nararatwong

Ichise

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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A knowledge graph becomes a central data hub in the enterprise and the research communities. Nevertheless, the development of knowledge graphs is challenging due to the insufficient functionalities of knowledge graph management platforms. In this paper, we develop a knowledge graph management platform (UWKGM). This platform enables users to integrate arbitrary functionalities as RESTful API services in order to facilitate the knowledge graph development process. In the demonstration, we highlight the main features of UWKGM and its use cases on knowledge graph management tasks. CCS CONCEPTS • Information systems → Data management systems; Data management systems; RESTful web services.

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Resolving Range Violations in DBpedia

Cited by 12 publications

References 15 publications

Correcting Knowledge Base Assertions

Correcting Knowledge Base Assertions

Semantic Web Empowered E-Tourism

Uwkgm

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