OntoILPER: an ontology- and inductive logic programming-based system to extract entities and relations from text

Lima, Rinaldo; Espinasse, Bernard; Freitas, Fred

doi:10.1007/s10115-017-1108-3

Cited by 13 publications

(5 citation statements)

References 33 publications

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“…Different from , we propose to revisit the ED task as an ontology learning process, inspired by relation extraction (RE) tasks based on ontology and logic-based learning. Lima et al (2018Lima et al ( , 2019 present a logic-based relational learning approach to RE that uses inductive logic programming for generating information extraction (IE) models in the form of symbolic rules, demonstrating that ontology-based IE approaches are advantageous in capturing correlation among classes, and succeed in symbolic reasoning.…”

Section: Related Workmentioning

confidence: 99%

OntoED: Low-resource Event Detection with Ontology Embedding

Deng¹,

Zhang²,

Li³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations. Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from datarich to data-poor event types. Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios.

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Section: Related Workmentioning

confidence: 99%

OntoED: Low-resource Event Detection with Ontology Embedding

Deng¹,

Zhang²,

Li³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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“…Since concepts are the basic component of a terminology, the first step is to conceive how to automatically learn a concept from available data. A number of algorithms together with implementations have been proposed in the literature to learn concepts in DL (Iannone et al, 2007;Fanizzi et al, 2008;Lehmann, 2009;Lehmann & Hitzler, 2010;Lima et al, 2018;Ozaki, 2020). They are inspired on techniques developed within the field of Inductive Learning Programming (ILP) (De Raedt, 2008), whose general goal is to automatically induce logic programmes from data.…”

Section: Concept Learningmentioning

confidence: 99%

Learning multiple concepts in description logic through three perspectives

Melo¹,

Revoredo²,

Paes³

2021

The Knowledge Engineering Review

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An ontology formalises a number of dependent and related concepts in a domain, encapsulated as a terminology. Manually defining such terminologies is a complex, time-consuming and error-prone task. Thus, there is great interest for strategies to learn terminologies automatically. However, most of the existing approaches induce a single concept definition at a time, disregarding dependencies that may exist among the concepts. As a consequence, terminologies that are difficult to interpret may be induced. Thus, systems capable of learning all concepts within a single task, respecting their dependency, are essential for reaching concise and readable ontologies. In this paper, we tackle this issue presenting three terminology learning strategies that aim at finding dependencies among concepts, before, during or after they have been defined. Experimental results show the advantages of regarding the dependencies among the concepts to achieve readable and concise terminologies, compared to a system that learns a single concept at a time. Moreover, the three strategies are compared and analysed towards discussing the strong and weak points of each one.

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“…The approach proposed by [Lima et al 2018] combines the use of ontologies and logical programming (Prolog) for NER and relation extraction. The approach allows the use of domain ontologies to extract relationships between entities.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Named Entities for Recognizing Family Relationships

Oliveira

Dias

Lima

et al. 2021

Anais Do IX Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2021)

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Named Entity Recognition problem’s objective is to automatically identify and classify entities like persons, places,organizations, and so forth. That is an area in Natural Language Processing and Information Extraction. NamedEntity Recognition is important because it is a fundamental step of preprocessing for several applications like relationextraction. However, it is a hard problem to solve as several categories of named entities are written similarly andthey appear in similar contexts. To accomplish it, we can use some hybrid approaches. Nevertheless, in this presentstudy, we use linguistic flavor by applying Local Grammar and Cascade of Transducers. Local Grammars are used torepresent the rules of a particular linguistic structure. They are often built manually to describe the entities we aimto recognize. In our study, we adapted a Local Grammar to improve the Recognition of Named Entities. The resultsshow an improvement of up to 7% on the F-measure metric in relation to the previous Local Grammar. Also, we builtanother Local Grammar to recognize family relationships from the improved Local Grammar. We present a practicalapplication for the extracted relationships using Prolog.

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OntoILPER: an ontology- and inductive logic programming-based system to extract entities and relations from text

Cited by 13 publications

References 33 publications

OntoED: Low-resource Event Detection with Ontology Embedding

OntoED: Low-resource Event Detection with Ontology Embedding

Learning multiple concepts in description logic through three perspectives

Using Named Entities for Recognizing Family Relationships

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