Towards Knowledge Handling in Ontology-Based Information Extraction Systems

Autor: Konys, Agnieszka

Publikováno v: In Procedia Computer Science 2018 126:2208-2218

ONTOPRIMA:APROTOTYPE FOR AUTOMATING ONTOLOGY POPULATION

Autor: Jawad Makki

Ontology Population supports the process of building ontologies in the complex task of instantiating ontology. Performing this process manually is both expensive and time consuming; this logically leads to attempts of fully or partially automating th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2659::a6c596f0214e72a67b197761df5c7c2b
https://zenodo.org/record/4022142

Towards Learning from User Feedback for Ontology-based Information Extraction

Autor: Opasjumruskit, Kobkaew, Schindler, Sirko, Schäfer, Philipp Matthias, Thiele, Laura

Many engineering projects involve the integration of various hardware parts from different suppliers. In preparation, parts that are best suited for the project requirements have to be selected. Information on these parts' characteristics is publishe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1640::85968a3e1848606bda7ae4e0e1fbc3f3
https://elib.dlr.de/128779/

Cold-Start Knowledge Base Population Using Ontology-Based Information Extraction with Conditional Random Fields

Autor: ter Horst, Hendrik, Hartung, Matthias, Cimiano, Philipp, d'Amato, Claudia, Theobald, Martin

Publikováno v: PUB-Publications at Bielefeld University

In this tutorial we discuss how Conditional Random Fields can be applied to knowledge base population tasks. We are in particular interested in the cold-start setting which assumes as given an ontology that models classes and properties relevant for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::04962ae4746a01973942500c9abcc4d5
https://pub.uni-bielefeld.de/record/2930952

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

Autor: Bernard Espinasse, Fred Freitas, Rinaldo Lima

Publikováno v: Knowledge and Information Systems (KAIS)
Knowledge and Information Systems (KAIS), Springer, 2017, 52 (2), pp.291-339. ⟨10.1007/s10115-017-1108-3⟩
Knowledge and Information Systems (KAIS), 2017, 52 (2), pp.291-339. ⟨10.1007/s10115-017-1108-3⟩

International audience; Named Entity Recognition (NER) and Relation Extraction (RE) are two important subtasks in Information Extraction (IE). Most of the current learning methods for NER and RE rely on supervised machine learning techniques with mor

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67509ff87d959e07f2eee3f6eb6b3a02
https://hal-amu.archives-ouvertes.fr/hal-01794571/document