An Inductive Logic Programming-Based Approach for Ontology Population from the Web

Lima, Rinaldo; Espinasse, Bernard; Oliveira, Hilário; Ferreira, Rafael; Cabral, Luciano de Souza; Filho, Dimas Melo; Freitas, Fred; Gadelha, Renê

doi:10.1007/978-3-642-40285-2_28

Cited by 6 publications

(5 citation statements)

References 5 publications

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“…More specifically, 9 participants finished one topic, 2 completed two topics, and 24 answered the questions of all three topics. Figure 6 illustrates the semantic relations identified by the game participants compared to the ground truth (GT) 11 . As shown in Figure 6, the semantic relation of 30 out of 36 label pairs was identified through the use of the gamified system (i.e., |Π| = 30 in Equation 6).…”

Section: Resultsmentioning

confidence: 99%

“…The method used for ontology learning can be automatic [7], [8] or it can involve human intervention. Asim et al [13] review the automatic ontology learning approaches and classify the techniques they use into three main categories: (1) linguistic methods [9], based on language characteristics such as part-of-speech-tagging; (2) statistical methods [10], based on probability and frequency measures; and (3) logical methods [11], based on inductive logic programming techniques to derive hypotheses.…”

Section: Related Workmentioning

confidence: 99%

“…While automatic approaches to ontology learning from general data sources have been proposed (e.g. [7]- [11]), there has been limited attention paid to ontology learning from process data. As argued by Mendling et al [12], learning ontologies from process-related data sources is one of the challenges that semantic process mining is facing.…”

Section: Introductionmentioning

confidence: 99%

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Process Activity Ontology Learning From Event Logs Through Gamification

et al. 2021

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The quality of event log data is a constraining factor in achieving reliable insights in process mining. Particular quality problems are posed by activity labels which are meant to be representative of organisational activities, but may take different manifestations (e.g. as a result of manual entry synonyms may be introduced). Ideally, such problems are remedied by domain experts, but they are time-poor and data cleaning is a time-consuming and tedious task. Ontologies provide a means to formalise domain knowledge and their use can provide a scalable solution to fixing activity label similarity problems, as they can be extended and reused over time. Existing approaches to activity label quality improvement use manuallygenerated ontologies or ontologies that are too general (e.g. WordNet). Limited attention has been paid to facilitating the development of purposeful ontologies in the field of process mining. This paper is concerned with the creation of activity ontologies by domain experts. For the first time in the field of process mining, their participation is facilitated and motivated through the application of techniques from crowdsourcing and gamification. Evaluation of our approach to the construction of activity ontologies by 35 participants shows that they found the method engaging and that its application results in high-quality ontologies.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Process Activity Ontology Learning From Event Logs Through Gamification

et al. 2021

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“…In a next step, relations between these extracted entities are extracted using techniques such as association rule learning 28,29 and inductive logic programming. 30,31 These literature-driven approaches however suffer from one common drawback: they assume that the literature reects a general consensus amongst experts on the conceptualisation of their domain. Such a consensus however oen does not exist, and building a consensus is oen what makes the ontology development time-consuming in the rst place.…”

Section: Related Workmentioning

confidence: 99%

Chebifier: automating semantic classification in ChEBI to accelerate data-driven discovery

Glauer,

Neuhaus,

Flügel

et al. 2024

Digital Discovery

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“…In the field of science, ILP is employed in the last stages, where the universalsolution is derived from the schematic idioms. Lima et al [1] employed IT techniques to dericeontology online. In their job, they employed two sources of evidence: WordNet in addition to an independent language model for the domain.…”

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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An Inductive Logic Programming-Based Approach for Ontology Population from the Web

Cited by 6 publications

References 5 publications

Process Activity Ontology Learning From Event Logs Through Gamification

Process Activity Ontology Learning From Event Logs Through Gamification

Chebifier: automating semantic classification in ChEBI to accelerate data-driven discovery

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

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