{"id":25,"date":"2026-01-28T14:54:00","date_gmt":"2026-01-28T14:54:00","guid":{"rendered":"https:\/\/dorset-project.org\/?page_id=25"},"modified":"2026-02-09T21:10:12","modified_gmt":"2026-02-09T21:10:12","slug":"work-packages","status":"publish","type":"page","link":"https:\/\/dorset-project.org\/index.php\/work-packages\/","title":{"rendered":"Work Packages"},"content":{"rendered":"\n<p><\/p>\n\n\n\n<p class=\"has-medium-font-size\"><br>The project is structured into four work packages (WPs), as illustrated in Figure 1, which together address the project objectives ranging from understanding the impact of Knowledge Graph (KG) characteristics on KG Embedding (KGE) methods to empirical evaluation, engineering of characteristic-aware KGE methods and benchmarking frameworks. The work packages are designed to progress from conceptual analysis to empirical validation and practical engineering, ensuring both scientific rigor and real-world impact.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img fetchpriority=\"high\" decoding=\"async\" width=\"441\" height=\"329\" src=\"http:\/\/dorset-project.org\/wp-content\/uploads\/2026\/02\/dorset_wp_output.png\" alt=\"\" class=\"wp-image-123\" style=\"aspect-ratio:1.3404301354450647;width:570px;height:auto\" srcset=\"https:\/\/dorset-project.org\/wp-content\/uploads\/2026\/02\/dorset_wp_output.png 441w, https:\/\/dorset-project.org\/wp-content\/uploads\/2026\/02\/dorset_wp_output-300x224.png 300w\" sizes=\"(max-width: 441px) 100vw, 441px\" \/><figcaption class=\"wp-element-caption\">Figure 1. Overview of Work Packages<\/figcaption><\/figure>\n\n\n\n<p class=\"has-medium-font-size\"><strong>WP1<\/strong>&nbsp;establishes the conceptual foundation through a systematic literature review of Knowledge Graph characteristics and their influence on Knowledge Graph Embeddings. Building on this,&nbsp;<strong>WP2<\/strong>&nbsp;develops an empirical evaluation framework, including controlled experiments, benchmarks, and KG profiling to quantify the impact of KG characteristics.&nbsp;<strong>WP3<\/strong> investigates and advances KGE methods by analysing their behaviour under different graph conditions and proposing characteristic-aware embedding approaches.&nbsp;<strong>WP4<\/strong>&nbsp;focuses on knowledge engineering and algorithm design, translating research findings into practical guidelines for knowledge engineers. <\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The project is structured into four work packages (WPs), as illustrated in Figure 1, which together address the project objectives ranging from understanding the impact of Knowledge Graph (KG) characteristics on KG Embedding (KGE) methods to empirical evaluation, engineering of characteristic-aware KGE methods and benchmarking frameworks. The work packages are designed to progress from conceptual [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-25","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/dorset-project.org\/index.php\/wp-json\/wp\/v2\/pages\/25","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dorset-project.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/dorset-project.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/dorset-project.org\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dorset-project.org\/index.php\/wp-json\/wp\/v2\/comments?post=25"}],"version-history":[{"count":4,"href":"https:\/\/dorset-project.org\/index.php\/wp-json\/wp\/v2\/pages\/25\/revisions"}],"predecessor-version":[{"id":147,"href":"https:\/\/dorset-project.org\/index.php\/wp-json\/wp\/v2\/pages\/25\/revisions\/147"}],"wp:attachment":[{"href":"https:\/\/dorset-project.org\/index.php\/wp-json\/wp\/v2\/media?parent=25"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}