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Albeit effective, these efforts are disjoint, and their collective strengths and weaknesses in effective KG completion have not been studied in the literature. We extend<jats:sc>Plumber<\/jats:sc>, a framework that brings together the research community\u2019s disjoint efforts on KG completion. We include more components into the architecture of<jats:sc>Plumber<\/jats:sc>\u00a0 to comprise 40\u00a0reusable components for various KG completion subtasks, such as coreference resolution, entity linking, and relation extraction. Using these components,<jats:sc>Plumber<\/jats:sc>dynamically generates suitable knowledge extraction pipelines and offers overall 432\u00a0distinct pipelines. We study the optimization problem of choosing optimal pipelines based on input sentences. To do so, we train a transformer-based classification model that extracts contextual embeddings from the input and finds an appropriate pipeline. We study the efficacy of<jats:sc>Plumber<\/jats:sc>for extracting the KG triples using standard datasets over three KGs: DBpedia, Wikidata, and Open Research Knowledge Graph. Our results demonstrate the effectiveness of<jats:sc>Plumber<\/jats:sc>in dynamically generating KG completion pipelines, outperforming all baselines agnostic of the underlying KG. Furthermore, we provide an analysis of collective failure cases, study the similarities and synergies among integrated components and discuss their limitations.<\/jats:p>","DOI":"10.1007\/s10115-022-01826-x","type":"journal-article","created":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T06:04:01Z","timestamp":1673071441000},"page":"1989-2016","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Information extraction pipelines for knowledge graphs"],"prefix":"10.1007","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8777-2780","authenticated-orcid":false,"given":"Mohamad Yaser","family":"Jaradeh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuldeep","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Stocker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Both","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00f6ren","family":"Auer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,7]]},"reference":[{"key":"1826_CR1","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-030-03667-6_2","volume-title":"Knowledge engineering and knowledge management","author":"A Alobaid","year":"2018","unstructured":"Alobaid A, Corcho O (2018) Fuzzy semantic labeling of semi-structured numerical datasets. 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