Inductive Logic Programming in an Agent System forOntological Relation Extraction

Seneviratne, Mohan; Ranasinghe, D. N.

doi:10.7763/ijmlc.2011.v1.51

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…Seneviratne and Ranasinghe ( 85 ) described the use of ILP as a learning approach for the acquisition of ontological relations in a multi-agent system. In this multi-agent system, one agent used ILP for rule learning process while another agent used these rules to identify new relations.…”

Section: Ontology Learning Techniquesmentioning

confidence: 99%

A survey of ontology learning techniques and applications

et al. 2018

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Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.

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Section: Ontology Learning Techniquesmentioning

confidence: 99%

A survey of ontology learning techniques and applications

et al. 2018

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“…ILPN2. Seneviratne and Ranasinghe [3] illustrated the benefit of ILP which is a knowledge method to obtain biological contacts in a multi-agent scheme. Within this multi-agent scheme, an agent employs IT.…”

Section: Related Workmentioning

confidence: 99%

Development of a Machine Learning model for knowledge acquisition, relationship extraction and discovery in Domain Ontology Engineering

Guruvayur*¹,

Suchithra²

2019

IJRTE

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Creating a fast domain independent ontology through knowledge acquisition is a key problem to be addressed in the domain of knowledge engineering. Updating and validation is impossible without the intervention of domain experts, which is an expensive and tedious process. Thereby, an automatic system to model the ontology has become essential. This manuscript presents a machine learning model based on heterogeneous data from multiple domains including agriculture, health care, food and banking, etc. The proposed model creates a complete domain independent process that helps in populating the ontology automatically by extracting the text from multiple sources by applying natural language processing and various techniques of data extraction. The ontology instances are classified based on the domain. A Jaccord Relationship extraction process and the Neural Network Approval for Automated Theory is used for retrieval of data, automated indexing, mapping and knowledge discovery and rule generation. The results and solutions show the proposed model can automatically and efficiently construct automated Ontology

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“…Our objective is to represent the documents with these domain specific entities and relations for the purpose of classifying the documents by using such relationextraction-rules. In proposed method a set of rules is generated by Inductive logic programming (ILP) for each relation [22] from the typed dependencies of the sentences annotated with the two or more entities. Since there are well established natural language parsers, obtaining typed dependencies of a sentence is not a complicated task.…”

Section: *Author For Correspondencementioning

confidence: 99%

Generation of relation-extraction-rules based on Markov logic network for document classification

Seneviratne¹,

Fernando²,

Karunaratne³

2019

IJACR

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Classifying documents into predefined classes is a very necessary task, especially in extracting information from huge resources such as web. Although a considerable amount of work has been carried out to classify documents into groups according to the subject domain or according to the other attributes. It still prevails as a big challenge in large scale, high dimensional document space. A number of techniques have been presented and proceeded with suggested improvements in order to achieve a higher degree of success in the document class. In this paper, a novel rule-based method for document classification with a combination of relation extraction techniques have been proposed. It is possible to replace overwhelming text classification techniques which involve thousands of words, document features or numerous patterns of word combinations by a set of rules which involves a much smaller number of entities and relations. We further discuss the effectiveness of relation extraction rules in document classification with the use of Markov logic networks for learning the weights of rules efficiently. Our experimental results show that the use of relation extraction rules on document classification yields a very high precision in the selected domain.We also demonstrate the applicability of our method on a benchmark text corpus with good performance measures.

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Inductive Logic Programming in an Agent System forOntological Relation Extraction

Cited by 8 publications

References 19 publications

A survey of ontology learning techniques and applications

A survey of ontology learning techniques and applications

Development of a Machine Learning model for knowledge acquisition, relationship extraction and discovery in Domain Ontology Engineering

Generation of relation-extraction-rules based on Markov logic network for document classification

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