Automatic Acquisition of Attributes for Ontology Construction

Cui, Gaoying; Lu, Qin; Li, Wenjie; Chen, Yirong

doi:10.1007/978-3-642-00831-3_23

Cited by 7 publications

(8 citation statements)

References 7 publications

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“…As a large scale on-line encyclopedia, besides {{Infobox}} Structures used in past research [5] which has low coverage, this paper explores definition sentences and Category Labels in Wiki to identify the concept for a given instance Article page with the help of NLP method. Figure 1 gives an example of a Wiki page containing {{Infobox}} Structure, definition sentence, and Category Labels.…”

Section: Mine Concepts From Wikipediamentioning

confidence: 99%

“…In this paper, the method using {{Infobox}} Structure will be used as the baseline for the comparison to [5]. An {{Infobox}} Structure is a formatted table present in some Article page P and labeled by a common subject concept p in the form of {{Infobox concept p }}to indicate that it is an instance page with reference to the concept p it belongs to.…”

Section: A Using the {{Infobox}} Structuresmentioning

confidence: 99%

“…The {{Infobox}} Structures declared by contributors is an indicator of instance page. However, only about 15% of Wiki pages contain the {{Infobox}} Structure, which leads to a very low coverage for concept acquisition through {{Infobox}} Structure [5]. In this paper, more Wiki resources are exploited for concept acquisition, such as definition sentences reflecting the is-a relation in Article pages and classification information in Category Labels providing topics relevant to Wiki pages.…”

Section: Introduction Ontology Construction By Linguists or Domain Exmentioning

confidence: 99%

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Mining Concepts from Wikipedia for Ontology Construction

Cui

et al. 2009

2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology

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Abstract-An ontology is a structured knowledgebase of concepts organized by relations among them. But concepts are usually mixed with their instances in the corpora for knowledge extraction. Concepts and their corresponding instances share similar features and are difficult to distinguish. In this paper, a novel approach is proposed to comprehensively obtain concepts with the help of definition sentences and Category Labels in Wikipedia pages. N-gram statistics and other NLP knowledge are used to help extracting appropriate concepts. The proposed method identified nearly 50,000 concepts from about 700,000 Wiki pages. The precision reaching 78.5% makes it an effective approach to mine concepts from Wikipedia for ontology construction.

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Section: Mine Concepts From Wikipediamentioning

confidence: 99%

Section: A Using the {{Infobox}} Structuresmentioning

confidence: 99%

Section: Introduction Ontology Construction By Linguists or Domain Exmentioning

confidence: 99%

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Mining Concepts from Wikipedia for Ontology Construction

Cui

et al. 2009

2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology

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“…En el contexto del aprendizaje de elementos ontológicos, el poblado de ontologías hace referencia a extraer y representar, de manera automática o semiautomática, los individuos de clase, relaciones entre los individuos y valores de las propiedades. En la literatura, existen enfoques ligeros para el poblado de ontologías basados en modelos estadísticos-sintácticos ( [4,5,6,7]) y enfoques semánticos basados en reglas ( [8,9,10]), estos no satisfacen el poblado ya que desatienden el conocimiento lingüístico profundo de los textos.…”

Section: Introductionunclassified

Poblado automático de ontologías de perfiles académicos a partir de textos en español

Reyes‐Ortiz¹,

Bravo²,

Herrera³

et al. 2015

RCS

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Resumen. Este artículo propone un enfoque para el poblado automático de ontologías de perfiles académicos a partir de los textos, expedientes curriculares y resúmenes, de publicaciones científicas en español. El enfoque utiliza reglas semánticas y marcadores lingüísticos para extraer los individuos de clase, relaciones y valores de propiedad. Una evaluación ha sido realizada con un conjunto de individuos gold standard, en términos de precisión y exhaustividad para el poblado de elementos ontológicos.Palabras clave: poblado automático de ontologías, reglas semánticas, marcadores lingüísticos, procesamiento de lenguaje natural (PLN).

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“…Ontology can be seen as an organized structure of concepts according to their relations (Cui et al, 2009). Therefore, concept extraction is an important subtask of ontology construction.…”

Section: Introductionmentioning

confidence: 99%

Topical key concept extraction from folksonomy through graph-based ranking

Han

Qin

Liu

2014

Multimed Tools Appl

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Concept extraction is a primary subtask of ontology construction. It is difficult to extract new concepts from traditional text corpus. Moreover, building a single ontology for multiple-topic corpus may lead to misconception. To deal with these problems, this paper proposes a novel framework to extract topical key concepts from folksonomy. Folksonomy is a valuable data source due to real-time update and rich user-generated contents. We first identify topics from folksonomy using topic models. Next the tags are ranked according to their importance for a certain topic by applying topic-specific random walk methods. The top-ranking tags are extracted as topical key concepts. Especially, a novel link weight function which combines the local structure information and global semantic similarity is proposed in importance score propagation. From the perspectives of qualitative and quantitative investigation, our method is feasible and effective.

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Automatic Acquisition of Attributes for Ontology Construction

Cited by 7 publications

References 7 publications

Mining Concepts from Wikipedia for Ontology Construction

Mining Concepts from Wikipedia for Ontology Construction

Poblado automático de ontologías de perfiles académicos a partir de textos en español

Topical key concept extraction from folksonomy through graph-based ranking

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