Learning relation axioms from text: An automatic Web-based approach

Sánchez, David; Moreno, Antonio; Vasto-Terrientes, Luis Del

doi:10.1016/j.eswa.2011.11.088

Cited by 28 publications

(7 citation statements)

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“…To further determine the relation type, a set of linguistic patterns along with the semantic role labelling information are used. Other similar work can be seen in the literature [12–20].…”

Section: Related Workmentioning

confidence: 54%

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Learning time-sensitive domain ontology from scientific papers with a hybrid learning method

Ren

2014

Journal of Information Science

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Large numbers of available scientific papers makes the research of ontology construction an attractive application area. However, there are two shortcomings for most current ontology construction approaches. First, implicit time properties of domain concepts are rarely taken into account in current approaches. Second, current automatic concept relation extraction methods mainly rely on the local context information that surrounds current considered concepts. These two problems prevent most current ontology construction methods from being employed to their full potential. To tackle these problems, we propose a hybrid learning method to integrate concepts’ global information and human experts’ knowledge together into ontology construction, among which concepts’ temporal attributes are taken into account. Our method first divides each concept into four time periods according to their attribution distribution on a time axis. Then global time-related attributions are collected for each concept. Finally, concept relations are extracted with a hybrid learning method. We evaluated our method by testing it on Chinese academic papers. It outperformed a baseline system based on only hierarchical concept relations, showing the effectiveness of our approach.

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Section: Related Workmentioning

confidence: 54%

“…The development of the Semantic Web makes ontology construction a hot issue. Until now, many researchers have proposed many efficient automatic ontology construction methods [1–43]. According to the concept relation assignment methods used, most of the current automatic ontology construction methods can be classified into three types.…”

Section: Related Workmentioning

confidence: 99%

Learning time-sensitive domain ontology from scientific papers with a hybrid learning method

Ren

2014

Journal of Information Science

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“…However, their approach needs a syntactic parser, which is costly and make the method less robust especially in ambiguity language like Arabic language. 28,29 Table 1 shows a summary of some works on relation extraction from Arabic texts. Based on the conducted studies, existing works of Arabic relation extraction can be classified into the following approaches: Pattern based approach, statistical approach and hybrid approach.…”

Section: Hybrid Approachmentioning

confidence: 99%

Towards relation extraction from Arabic text: a review

Al-Arfaj¹

2019

IRATJ

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Semantic relation extraction is an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. However, extracting semantic relations between concepts is not trivial and one of the main challenges in Natural Language Processing (NLP) Field. The Arabic language has complex morphological, grammatical, and semantic aspects since it is a highly inflectional and derivational language, which makes task even more challenging. In this paper, we present a review of the state of the art for relation extraction from texts, addressing the progress and difficulties in this field. We discuss several aspects related to this task, considering the taxonomic and non-taxonomic relation extraction methods. Majority of relation extraction approaches implement a combination of statistical and linguistic techniques to extract semantic relations from text. We also give special attention to the state of the work on relation extraction from Arabic texts, which need further progress.

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“…Classes that represent the concepts and described hierarchical relations that are taxonomical and nontaxonomical are the important components of such systems [ 97 , 98 ]. Apart from these axioms and specific objects, other components are usually considered [ 99 ].…”

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confidence: 99%

A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users

Ravi

Subramaniyaswamy

2016

Computational Intelligence and Neuroscience

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Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented.

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Learning relation axioms from text: An automatic Web-based approach

Cited by 28 publications

References 28 publications

Learning time-sensitive domain ontology from scientific papers with a hybrid learning method

Learning time-sensitive domain ontology from scientific papers with a hybrid learning method

Towards relation extraction from Arabic text: a review

A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users

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