A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case
| Autor: | Antonis Litke, Nikolaos Papadakis, Dimitris Papadopoulos |
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| Rok vydání: | 2020 |
| Předmět: |
Information extraction
Bioinformatics Computer science Triple extraction 02 engineering and technology Ontology (information science) computer.software_genre lcsh:Technology lcsh:Chemistry Set (abstract data type) 03 medical and health sciences 0202 electrical engineering electronic engineering information engineering Redundancy (engineering) General Materials Science information extraction Representation (mathematics) Data mining lcsh:QH301-705.5 Instrumentation 030304 developmental biology Fluid Flow and Transfer Processes 0303 health sciences Coreference triple extraction lcsh:T business.industry Process Chemistry and Technology General Engineering bioinformatics data mining Pipeline (software) Automatic summarization lcsh:QC1-999 Computer Science Applications lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 020201 artificial intelligence & image processing Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business computer lcsh:Physics Natural language processing |
| Zdroj: | Applied Sciences Volume 10 Issue 16 Applied Sciences, Vol 10, Iss 5630, p 5630 (2020) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app10165630 |
| Popis: | The usefulness of automated information extraction tools in generating structured knowledge from unstructured and semi-structured machine-readable documents is limited by challenges related to the variety and intricacy of the targeted entities, the complex linguistic features of heterogeneous corpora, and the computational availability for readily scaling to large amounts of text. In this paper, we argue that the redundancy and ambiguity of subject&ndash predicate&ndash object (SPO) triples in open information extraction systems has to be treated as an equally important step in order to ensure the quality and preciseness of generated triples. To this end, we propose a pipeline approach for information extraction from large corpora, encompassing a series of natural language processing tasks. Our methodology consists of four steps: i. in-place coreference resolution, ii. extractive text summarization, iii. parallel triple extraction, and iv. entity enrichment and graph representation. We manifest our methodology on a large medical dataset (CORD-19), relying on state-of-the-art tools to fulfil the aforementioned steps and extract triples that are subsequently mapped to a comprehensive ontology of biomedical concepts. We evaluate the effectiveness of our information extraction method by comparing it in terms of precision, recall, and F1-score with state-of-the-art OIE engines and demonstrate its capabilities on a set of data exploration tasks. |
| Databáze: | OpenAIRE |
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