Lightweight predicate extraction for patient-level cancer information and ontology development

Abstract

BackgroundKnowledge engineering for ontological knowledgebases is resource and time intensive. To alleviate these issues, especially for novices, automated tools from the natural language domain can assist in the development process of ontologies. We focus towards the development of ontologies for the public health domain and use patient-centric sources from MedlinePlus related to HPV-causing cancers.MethodsThis paper demonstrates the use of a lightweight open information extraction (OIE) tool to derive accurate knowledge triples that can lead to the seeding of an ontological knowledgebase. We developed a custom application, which interfaced with an information extraction software library, to help facilitate the tasks towards producing knowledge triples from textual sources.ResultsThe results of our efforts generated accurate extractions ranging from 80–89% precision. These triples can later be transformed to OWL/RDF representation for our planned ontological knowledgebase.ConclusionsOIE delivers an effective and accessible method towards the development ontologies.

A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case

“…However, especially in the case of OIE approaches and due to concerns on scaling, the use of syntactic or semantic relation extraction techniques has been relatively sparse, with the exception of a few recent examples aiming at domain-specific knowledge extraction [5][6][7][8][9]. Most domain-specific information extraction approaches are focused primarily on evaluating the efficiency of different triple extraction tools on raw data, not taking useful pre-processing and post-processing strategies into account, thus resulting in a large number of potentially uninformative triples [10][11][12]. There exist a few systems that go beyond triple extraction by implementing a more thorough preprocessing strategy, including coreference resolution or discourse analysis to improve the quality of the extracted triples; however, these do not address the scalability issues that arise from processing large corpora [13,14].…”

“…The most likely reservoir was bats, with evidence that the virus was transmitted to a human through an intermediate host, probably a palm civet or raccoon dog (8,9). In less than a year, SARS-CoV infected 8098 people in 26 countries, of whom 774 died (10,11). Approximately 25% of the patients developed organ failure, most often acute respiratory distress syndrome (ARDS), requiring admission to an intensive care unit (ICU), while the case fatality rate (CFR) was 9.6%.…”

Extended Analysis of Topological-Pattern-Based Ontology Enrichment

“…As early as 2004, Smith and Fellbaum used online health information sources to build the Medical WordNet from two corpora, namely Medical FactNet and Medical BeliefNet [35]. Recent work on semi/fully-automated ontology learning focuses on the extraction of terms and predicates (i.e., triples) from a text corpus [12, 36, 37]. For example, our group recently developed an open source text mining tool called simiTerm to identify the terms in a text corpus that are contextually and syntactically similar to existing terms in an ontology [11].…”

Introduction: the International Conference on Intelligent Biology and Medicine (ICIBM) 2016: special focus on medical informatics and big data

“…While Ru et al focused on clinician users, Amith et al focused on patients. Amith et al developed an ontology-based approach for representing knowledge about HPV-causing cancer for patients [2]. Although focusing on cancer related knowledge, their approach can presumably be applied to any other domain for creating ontologies.…”

“…These papers cover a wide range of topics including Knowledge and Data Personalization [1,2], Social Media Applications to Healthcare [3,4], Clinical Natural Language Processing [5,6], Patient Safety Analyses [7,8], and Data Mining Using Electronic Health Records [9,10].…”