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aria-current="page"> Neuro – Symbolic AI </li> </ol> </nav> </div> </div> </div> </header> <main id="main" tabindex="-1"> <div class="M-Stage-Two" data-js-component="stage-two"> <div class="e-container"> <div class="M-Stage-Two__header"> <div class="M-Stage-Two__topbar"> <div class="M-Stage-Two__kicker">Our Fields Of Expertise </div> </div> <div class="M-Stage-Two__headline"> <div class="M-Stage-Two__headline-container a-text"> <h1>Neuro - Symbolic AI </h1> </div> </div> </div> </div> </div> <section class="M-TextTwo" aria-label="What Motivates Us"> <div class="e-container"> <div class="a-component-header -small"> <div class="a-text"> <h2>What Motivates Us</h2> </div> </div> <div class="M-TextTwo__body"> <div class="M-TextTwo__description"> <div class="A-Text-RichText"> <p>While deep learning and neural networks have achieved remarkable success in many domains, they often struggle to incorporate structured domain knowledge and reasoning into their predictions and outputs. This has led to growing interest in so-called neuro-symbolic AI, which aims at combining elements from both deep learning and automated reasoning, planning, and knowledge representation, in order to achieve the best of both worlds: the flexibility and data-driven performance of deep learning as well as the logical consistency and grounded reasoning of symbolic methods.</p> </div> </div> </div> </div> </section> <section class="M-TextTwo" aria-label="Our Approach"> <div class="e-container"> <div class="a-component-header -small"> <div class="a-text"> <h2>Our Approach</h2> </div> </div> <div class="M-TextTwo__body"> <div class="M-TextTwo__description"> <div class="A-Text-RichText"> <p>Neuro-symbolic AI approaches are ideal to exploit the knowledge of domain experts for industrial AI solutions. We develop methods that help leveraging domain experts' knowledge in a way that makes it machine-accessible (e.g., as knowledge graphs), to gain further knowledge via reasoning and prediction, and exploit knowledge for search, exploration, machine learning and problem solving.</p> </div> </div> </div> </div> </section> <section class="M-TextTwo" aria-label="Application"> <div class="e-container"> <div class="a-component-header -small"> <div class="a-text"> <h2>Application</h2> </div> </div> <div class="M-TextTwo__body"> <div class="M-TextTwo__description"> <div class="A-Text-RichText"> <p>We are particularly focusing on applications that leverage Bosch’s existing strength in industrial knowledge graphs. By integrating graph neural network approaches with industrial knowledge graphs, we can develop analytical tools that can answer queries requiring both observational data and explicit rules provided by Bosch domain experts.</p> </div> </div> </div> </div> </section> <section aria-label="M_Navigation_Tabs"> <div class="m-frok-tab-navigation" data-js-component="tab-navigation"> <div class="e-container"> <div class="a-tab-navigation__wrapper" data-js-component="tab-buttons"> <div class="a-tab-navigation__gradients"></div> <ol class="a-tab-navigation" data-active="#tab-5033871-1" role="tablist"> <li class="a-tab-navigation__item" role="none"> <button aria-label="tab-5033871-1" id="tab-5033871-1" type="button" role="tab" class="a-tab-navigation__tab" data-frok-tab-identifier="tab-5033871-1" aria-controls="tab-5033871-1"> <span class="a-tab-navigation__tab-content"> <span class="a-tab-navigation__label">Specific Focus Area - Machine Learning on Knowledge Graphs</span> </span> </button> </li> <li class="a-tab-navigation__item" role="none"> <button aria-label="tab-5033871-2" id="tab-5033871-2" type="button" role="tab" class="a-tab-navigation__tab" data-frok-tab-identifier="tab-5033871-2" aria-controls="tab-5033871-2"> <span class="a-tab-navigation__tab-content"> <span class="a-tab-navigation__label">Specific Focus Area - Knowledge-Driven Problem Solving</span> </span> </button> </li> </ol> </div> <div class="e-container"> <div class="T-TabNavigationPage"> <div id="TAB-PANEL-5033871-tab-5033871-1" aria-labelledby="tab-5033871-1" role="tabpanel" class="a-tab-navigation-pane a-tab-navigation-pane--active" data-tab-name="tab-5033871-1" tabindex="0"> <section class="M-TextImageTwo" aria-label="Machine Learning on Knowledge Graphs"> <div class="e-container"> <div class="a-component-header"> <div class="a-text"> <h2>Machine Learning on Knowledge Graphs</h2> </div> </div> <div class="M-TextImageTwo__body -ratio-4-to-8" data="rightAlignedSmall"> <div class="M-TextImageTwo__imageWrapper"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/bild4_res_400x225-2.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/bild4_res_400x225-2.webp, /media/reserach/research_fields/bild4_res_800x450-2.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/bild4_res_400x225-2.webp, /media/reserach/research_fields/bild4_res_800x450-2.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/bild4_res_400x225-2.webp, /media/reserach/research_fields/bild4_res_800x450-2.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/bild4_res_400x225-2.webp, /media/reserach/research_fields/bild4_res_800x450-2.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/bild4_res_400x225-2.webp, /media/reserach/research_fields/bild4_res_800x450-2.webp 2x" width="800" height="450" alt="Machine Learning on Knowledge Graphs"> </picture> </div> </figure> </div> <div class="M-TextImageTwo__textWrapper"> <div class="M-TextImageTwo__body--description"> <div class="A-Text-RichText"> <p>Knowledge graphs (KGs) are powerful for search and exploration applications. Reasoning can lead to further knowledge and KG validation and thus improved KG quality. With Bosch having lots of know-how in various domains, knowledge needs to be machine accessible (e.g., as KG) to allow for industrial AI solutions. Therefore, neuro-symbolic AI is ideal to exploit the knowledge of domain experts for AI solutions at Bosch. With our work on neuro-symbolic AI we aim to combine symbolic and subsymbolic methods to incorporate (Bosch) domain knowledge into ML models, verify results of ML models, reduce the amounts of training data, and exploit knowledge for search, exploration, and problem solving. </p> </div> </div> </div> </div> </div> </section> <section class="M-TextTwo" aria-label="Use Case"> <div class="e-container"> <div class="a-component-header -small"> <div class="a-text"> <h2>Use Case</h2> </div> </div> <div class="M-TextTwo__body"> <p class="M-TextTwo__intro-text"> Machine Learning on Knowledge Graphs </p> </div> </div> </section> <ul class="M-ImageGroup__list"> <li class="M-ImageGroup__item"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/1-s2-0-s266732582100159x-gr1_res_400x225.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/1-s2-0-s266732582100159x-gr1_res_400x225.webp, /media/reserach/research_fields/1-s2-0-s266732582100159x-gr1_res_800x450.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/1-s2-0-s266732582100159x-gr1_res_400x225.webp, /media/reserach/research_fields/1-s2-0-s266732582100159x-gr1_res_800x450.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/1-s2-0-s266732582100159x-gr1_res_400x225.webp, /media/reserach/research_fields/1-s2-0-s266732582100159x-gr1_res_800x450.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/1-s2-0-s266732582100159x-gr1_res_400x225.webp, /media/reserach/research_fields/1-s2-0-s266732582100159x-gr1_res_800x450.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/1-s2-0-s266732582100159x-gr1_res_400x225.webp, /media/reserach/research_fields/1-s2-0-s266732582100159x-gr1_res_800x450.webp 2x" width="800" height="450" alt="Machine Learning on Knowledge Graphs"> </picture> </div> <figcaption class="A-Image__caption">Figure 1</figcaption> </figure> </li> </ul> <section class="M-TextTwo" aria-label="Our Research "> <div class="e-container"> <div class="a-component-header -small"> <div class="a-text"> <h2>Our Research </h2> </div> </div> </div> </section> <section class="M-TextImageTwo" aria-label="M_Navigation_Tabs"> <div class="e-container"> <div class="a-component-header"> <div class="a-text"> <p>Exploit Domain Knowledge</p> </div> </div> <div class="M-TextImageTwo__body -ratio-8-to-4" data="leftAlignedSmall"> <div class="M-TextImageTwo__textWrapper"> <div class="M-TextImageTwo__body--description"> <div class="A-Text-RichText"> <p>We develop methods that allow for integration of rules and structured domain knowledge into machine learning models. These include, for example, ontology-enhanced knowledge graph embedding techniques.</p> </div> </div> </div> <div class="M-TextImageTwo__imageWrapper"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/gluehbirne_res_400x225.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/gluehbirne_res_400x225.webp, /media/reserach/research_fields/gluehbirne_res_800x450.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/gluehbirne_res_400x225.webp, /media/reserach/research_fields/gluehbirne_res_800x450.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/gluehbirne_res_400x225.webp, /media/reserach/research_fields/gluehbirne_res_800x450.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/gluehbirne_res_400x225.webp, /media/reserach/research_fields/gluehbirne_res_800x450.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/gluehbirne_res_400x225.webp, /media/reserach/research_fields/gluehbirne_res_800x450.webp 2x" width="800" height="450" alt="Icon "> </picture> </div> </figure> </div> </div> </div> </section> <section class="M-TextImageTwo" aria-label="M_Navigation_Tabs"> <div class="e-container"> <div class="a-component-header"> <div class="a-text"> <p>Gaining New Insights</p> </div> </div> <div class="M-TextImageTwo__body -ratio-4-to-8" data="rightAlignedSmall"> <div class="M-TextImageTwo__imageWrapper"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/medaille-2_res_400x225.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/medaille-2_res_400x225.webp, /media/reserach/research_fields/medaille-2_res_800x450.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/medaille-2_res_400x225.webp, /media/reserach/research_fields/medaille-2_res_800x450.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/medaille-2_res_400x225.webp, /media/reserach/research_fields/medaille-2_res_800x450.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/medaille-2_res_400x225.webp, /media/reserach/research_fields/medaille-2_res_800x450.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/medaille-2_res_400x225.webp, /media/reserach/research_fields/medaille-2_res_800x450.webp 2x" width="800" height="450" alt="Icon "> </picture> </div> </figure> </div> <div class="M-TextImageTwo__textWrapper"> <div class="M-TextImageTwo__body--description"> <div class="A-Text-RichText"> <p>Neuro-symbolic AI methods allow to extract new insights from manufacturing data structured in a knowledge graph form. For example, this concerns automatically learning rules and ontologies from the data.</p> </div> </div> </div> </div> </div> </section> <section class="M-TextImageTwo" aria-label="M_Navigation_Tabs"> <div class="e-container"> <div class="a-component-header"> <div class="a-text"> <p>Knowledge Graph Completion and Cleaning</p> </div> </div> <div class="M-TextImageTwo__body -ratio-8-to-4" data="leftAlignedSmall"> <div class="M-TextImageTwo__textWrapper"> <div class="M-TextImageTwo__body--description"> <div class="A-Text-RichText"> <p>Knowledge graphs (KGs) are often incomplete or of mixed quality. Neuro-symbolic AI methods help to increase the KG quality by detecting inconsistencies or by predicting missing links in KGs.</p> </div> </div> </div> <div class="M-TextImageTwo__imageWrapper"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/zahnrad_res_400x225.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/zahnrad_res_400x225.webp, /media/reserach/research_fields/zahnrad_res_800x450.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/zahnrad_res_400x225.webp, /media/reserach/research_fields/zahnrad_res_800x450.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/zahnrad_res_400x225.webp, /media/reserach/research_fields/zahnrad_res_800x450.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/zahnrad_res_400x225.webp, /media/reserach/research_fields/zahnrad_res_800x450.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/zahnrad_res_400x225.webp, /media/reserach/research_fields/zahnrad_res_800x450.webp 2x" width="800" height="450" alt="Icon "> </picture> </div> </figure> </div> </div> </div> </section> <section class="M-TextImageTwo" aria-label="M_Navigation_Tabs"> <div class="e-container"> <div class="a-component-header"> <div class="a-text"> <p>Advanced AI on Top of Knowledge Graphs</p> </div> </div> <div class="M-TextImageTwo__body -ratio-4-to-8" data="rightAlignedSmall"> <div class="M-TextImageTwo__imageWrapper"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/werkzeug_res_400x225.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/werkzeug_res_400x225.webp, /media/reserach/research_fields/werkzeug_res_800x450.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/werkzeug_res_400x225.webp, /media/reserach/research_fields/werkzeug_res_800x450.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/werkzeug_res_400x225.webp, /media/reserach/research_fields/werkzeug_res_800x450.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/werkzeug_res_400x225.webp, /media/reserach/research_fields/werkzeug_res_800x450.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/werkzeug_res_400x225.webp, /media/reserach/research_fields/werkzeug_res_800x450.webp 2x" width="800" height="450" alt="Icon "> </picture> </div> </figure> </div> <div class="M-TextImageTwo__textWrapper"> <div class="M-TextImageTwo__body--description"> <div class="A-Text-RichText"> <p>AI methods on top of knowledge graphs allow one to explore and automatically analyze structured knowledge. For example, we develop techniques that simultaneously exploit ontologies and machine learning for answering complex queries over incomplete knowledge graphs, as well as approaches for explainable clustering over symbolic knowledge.</p> </div> </div> </div> </div> </div> </section> <ul class="M-ImageGroup__list"> <li class="M-ImageGroup__item"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/machine_learning_on_knowledge_graphs-2_res_400x225.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/machine_learning_on_knowledge_graphs-2_res_400x225.webp, /media/reserach/research_fields/machine_learning_on_knowledge_graphs-2_res_800x450.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/machine_learning_on_knowledge_graphs-2_res_400x225.webp, /media/reserach/research_fields/machine_learning_on_knowledge_graphs-2_res_800x450.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/machine_learning_on_knowledge_graphs-2_res_400x225.webp, /media/reserach/research_fields/machine_learning_on_knowledge_graphs-2_res_800x450.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/machine_learning_on_knowledge_graphs-2_res_400x225.webp, /media/reserach/research_fields/machine_learning_on_knowledge_graphs-2_res_800x450.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/machine_learning_on_knowledge_graphs-2_res_400x225.webp, /media/reserach/research_fields/machine_learning_on_knowledge_graphs-2_res_800x450.webp 2x" width="800" height="450" alt="Machine Learning on Knowledge Graphs"> </picture> </div> <figcaption class="A-Image__caption">Figure 2</figcaption> </figure> </li> </ul> <section class="M-TextTwo" aria-label="References "> <div class="e-container"> <div class="a-component-header -small"> <div class="a-text"> <h2>References </h2> </div> </div> <div class="M-TextTwo__body"> <div class="M-TextTwo__description"> <div class="A-Text-RichText"> <p>Feng, W., Zhang, J., Dong, Y., Han, Y., Luan, H., Xu, Q., Yang, Q., Kharlamov, E., & Tang, J. (2020). Graph Random Neural Networks for Semi-Supervised Learning on Graphs. NeurIPS. <a target="_blank" aria-label="[PDF] opens external site" rel="noopener, noreferrer" href="https://arxiv.org/pdf/2005.11079.pdf"><span>[PDF]<i class="a-icon ui-ic-nosafe-lr-externallink" title="externallink"></i></span></a></p> <p>Gad-Elrab, M.H., Stepanova, D., Tran, T-K., Adel, H., & Weikum, G. (2020). ExCut: Explainable Embedding-based Clustering over Knowledge Graphs. ISWC. <a target="_blank" aria-label="[PDF] opens external site" rel="noopener, noreferrer" href="https://www.researchgate.net/publication/344886998_ExCut_Explainable_Embedding-based_Clustering_over_Knowledge_Graphs"><span>[PDF]<i class="a-icon ui-ic-nosafe-lr-externallink" title="externallink"></i></span></a></p> <p>Jain, N., Tran, T-K., Gad-Elrab, M.H., & Stepanova, D. (2021). Improving Knowledge Graph Embeddings with Ontological Reasoning. ISWC. <a target="_blank" aria-label="[PDF] opens external site" rel="noopener, noreferrer" href="https://link.springer.com/chapter/10.1007/978-3-030-88361-4_24"><span>[PDF]<i class="a-icon ui-ic-nosafe-lr-externallink" title="externallink"></i></span></a></p> <p>Shi, Y., Cheng, G., Tran, T-K., Kharlamov, E., & Shen, Y. (2021). Efficient Computation of Semantically Cohesive Subgraphs for Keyword-Based Knowledge Graph Exploration. WWW. <a target="_blank" aria-label="[PDF] opens external site" rel="noopener, noreferrer" href="https://dl.acm.org/doi/10.1145/3442381.3449900"><span>[PDF]<i class="a-icon ui-ic-nosafe-lr-externallink" title="externallink"></i></span></a></p> <p>Tran, T-K., Stepanova, D., Kharlamov, E., & Stroetgen, J. (2020) Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs. WWW. <a target="_blank" aria-label="[PDF] opens external site" rel="noopener, noreferrer" href="https://dl.acm.org/doi/abs/10.1145/3366423.3380014"><span>[PDF]<i class="a-icon ui-ic-nosafe-lr-externallink" title="externallink"></i></span></a> </p> <p>Wang, P-W., Stepanova, D., Domokos, C., & Kolter, Z. (2020). Differentiable Learning of Numerical Rules from Knowledge Graphs. ICLR. <a target="_blank" aria-label="[PDF] opens external site" rel="noopener, noreferrer" href="https://openreview.net/pdf?id=rJleKgrKwS"><span>[PDF]<i class="a-icon ui-ic-nosafe-lr-externallink" title="externallink"></i></span></a></p> <p>Shi, Y., Cheng, G., Tran, T.-K., Tang, J., & Kharlamov, E. (2021). Keyword-Based Knowledge Graph Exploration Based on Quadratic Group Steiner Trees. IJCAI. <a target="_blank" aria-label="[PDF] opens external site" rel="noopener, noreferrer" href="https://www.ijcai.org/proceedings/2021/0215.pdf"><span>[PDF]<i class="a-icon ui-ic-nosafe-lr-externallink" title="externallink"></i></span></a></p> </div> </div> </div> </div> </section> </div> <div id="TAB-PANEL-5033871-tab-5033871-2" aria-labelledby="tab-5033871-2" role="tabpanel" class="a-tab-navigation-pane" data-tab-name="tab-5033871-2" tabindex="0"> <section class="M-TextImageTwo" aria-label="Knowledge-Driven Problem Solving"> <div class="e-container"> <div class="a-component-header"> <div class="a-text"> <h2>Knowledge-Driven Problem Solving</h2> </div> </div> <div class="M-TextImageTwo__body -ratio-4-to-8" data="rightAlignedSmall"> <div class="M-TextImageTwo__imageWrapper"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/bild8_res_400x225.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/bild8_res_400x225.webp, /media/reserach/research_fields/bild8_res_800x450.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/bild8_res_400x225.webp, /media/reserach/research_fields/bild8_res_800x450.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/bild8_res_400x225.webp, /media/reserach/research_fields/bild8_res_800x450.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/bild8_res_400x225.webp, /media/reserach/research_fields/bild8_res_800x450.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/bild8_res_400x225.webp, /media/reserach/research_fields/bild8_res_800x450.webp 2x" width="800" height="450" alt="Knowledge-driven problem solving"> </picture> </div> </figure> </div> <div class="M-TextImageTwo__textWrapper"> <div class="M-TextImageTwo__body--description"> <div class="A-Text-RichText"> <p>Answer-Set Programming (ASP) is a prominent approach to declarative problem solving oriented towards difficult search and optimization problems such as resource allocation, configuration or design of assembly lines. </p> <p>Rich modeling language and the availability of highly optimized solvers make ASP especially attractive for industry allowing to reduce implementation and maintenance costs as well as improve the man-machine interaction of deployed solutions. </p> </div> </div> </div> </div> </div> </section> <section class="M-TextTwo" aria-label="Use Case"> <div class="e-container"> <div class="a-component-header -small"> <div class="a-text"> <h2>Use Case</h2> </div> </div> <div class="M-TextTwo__body"> <p class="M-TextTwo__intro-text"> Answer Set Programming for Knowledge-Driven Problem Solving </p> </div> </div> </section> <section class="M-Image" aria-label="M_Navigation_Tabs"> <div class="e-container"> <div class="M-Image__image-wrapper -small"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/bild10_res_400x225.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/bild10_res_800x450.webp, /media/reserach/research_fields/bild10_res_1600x900.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/bild10_res_800x450.webp, /media/reserach/research_fields/bild10_res_1600x900.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/bild10_res_800x450.webp, /media/reserach/research_fields/bild10_res_1600x900.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/bild10_res_400x225.webp, /media/reserach/research_fields/bild10_res_800x450.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/bild10_res_400x225.webp, /media/reserach/research_fields/bild10_res_800x450.webp 2x" width="800" height="450" alt="Neuro symbolic AI "> </picture> </div> </figure> </div> </div> </section> <section class="M-TextTwo" aria-label="Our Research "> <div class="e-container"> <div class="a-component-header -small"> <div class="a-text"> <h2>Our Research </h2> </div> </div> </div> </section> <section class="M-TextImageTwo" aria-label="M_Navigation_Tabs"> <div class="e-container"> <div class="a-component-header"> <div class="a-text"> <p>Scalable Rule-Based AI Methods</p> </div> </div> <div class="M-TextImageTwo__body -ratio-8-to-4" data="leftAlignedSmall"> <div class="M-TextImageTwo__textWrapper"> <div class="M-TextImageTwo__body--description"> <div class="A-Text-RichText"> <p>We develop AI methods that combine answer set programming with classical optimization techniques (e.g., large neighborhood search) to improve scalability of the resulting systems.</p> </div> </div> </div> <div class="M-TextImageTwo__imageWrapper"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/uhr_res_400x225.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/uhr_res_400x225.webp, /media/reserach/research_fields/uhr_res_800x450.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/uhr_res_400x225.webp, /media/reserach/research_fields/uhr_res_800x450.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/uhr_res_400x225.webp, /media/reserach/research_fields/uhr_res_800x450.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/uhr_res_400x225.webp, /media/reserach/research_fields/uhr_res_800x450.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/uhr_res_400x225.webp, /media/reserach/research_fields/uhr_res_800x450.webp 2x" width="800" height="450" alt="Icon "> </picture> </div> </figure> </div> </div> </div> </section> <section class="M-TextImageTwo" aria-label="M_Navigation_Tabs"> <div class="e-container"> <div class="a-component-header"> <div class="a-text"> <p>Explainable Rule-Based AI Methods</p> </div> </div> <div class="M-TextImageTwo__body -ratio-4-to-8" data="rightAlignedSmall"> <div class="M-TextImageTwo__imageWrapper"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/buch_res_400x225.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/buch_res_400x225.webp, /media/reserach/research_fields/buch_res_800x450.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/buch_res_400x225.webp, /media/reserach/research_fields/buch_res_800x450.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/buch_res_400x225.webp, /media/reserach/research_fields/buch_res_800x450.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/buch_res_400x225.webp, /media/reserach/research_fields/buch_res_800x450.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/buch_res_400x225.webp, /media/reserach/research_fields/buch_res_800x450.webp 2x" width="800" height="450" alt="Icon "> </picture> </div> </figure> </div> <div class="M-TextImageTwo__textWrapper"> <div class="M-TextImageTwo__body--description"> <div class="A-Text-RichText"> <p>Explanations are very important in the context of manufacturing optimization, but typically existing solutions are black boxes, which are inaccessible to users. We develop explainable methods that allow for understanding the reasons behind computed solutions.</p> </div> </div> </div> </div> </div> </section> <section class="M-TextImageTwo" aria-label="M_Navigation_Tabs"> <div class="e-container"> <div class="a-component-header"> <div class="a-text"> <p>Combining Rule-Based Methods with Neural Networks</p> </div> </div> <div class="M-TextImageTwo__body -ratio-8-to-4" data="leftAlignedSmall"> <div class="M-TextImageTwo__textWrapper"> <div class="M-TextImageTwo__body--description"> <div class="A-Text-RichText"> <p>Machine Learning is data-driven and agnostic to vital experts’ knowledge. To address this, we work on novel AI methods that combine rule-based answer set programming systems with (deep) learning thus allowing for advanced reasoning on top of ML predictions. </p> </div> </div> </div> <div class="M-TextImageTwo__imageWrapper"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/zahnrad_res_400x225.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/zahnrad_res_400x225.webp, /media/reserach/research_fields/zahnrad_res_800x450.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/zahnrad_res_400x225.webp, /media/reserach/research_fields/zahnrad_res_800x450.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/zahnrad_res_400x225.webp, /media/reserach/research_fields/zahnrad_res_800x450.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/zahnrad_res_400x225.webp, /media/reserach/research_fields/zahnrad_res_800x450.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/zahnrad_res_400x225.webp, /media/reserach/research_fields/zahnrad_res_800x450.webp 2x" width="800" height="450" alt="Icon "> </picture> </div> </figure> </div> </div> </div> </section> <section class="M-TextImageTwo" aria-label="M_Navigation_Tabs"> <div class="e-container"> <div class="a-component-header"> <div class="a-text"> <p>Cutting-Edge Research</p> </div> </div> <div class="M-TextImageTwo__body -ratio-4-to-8" data="rightAlignedSmall"> <div class="M-TextImageTwo__imageWrapper"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/medaille-2_res_400x225.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/medaille-2_res_400x225.webp, /media/reserach/research_fields/medaille-2_res_800x450.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/medaille-2_res_400x225.webp, /media/reserach/research_fields/medaille-2_res_800x450.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/medaille-2_res_400x225.webp, /media/reserach/research_fields/medaille-2_res_800x450.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/medaille-2_res_400x225.webp, /media/reserach/research_fields/medaille-2_res_800x450.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/medaille-2_res_400x225.webp, /media/reserach/research_fields/medaille-2_res_800x450.webp 2x" width="800" height="450" alt="Icon "> </picture> </div> </figure> </div> <div class="M-TextImageTwo__textWrapper"> <div class="M-TextImageTwo__body--description"> <div class="A-Text-RichText"> <p>Our tools and methods in the area of rule-based, knowledge-driven problem solving result from world-class research of our academic collaborators at the Vienna University of Technology and colleagues at BCAI. We constantly challenge and advance the state-of-the-art in the field, publish at top-tier venues, and exploit the developed methods in real-world Bosch applications.</p> </div> </div> </div> </div> </div> </section> <ul class="M-ImageGroup__list"> <li class="M-ImageGroup__item"> <figure class="A-Image__figure" > <div class="A-Image__preloadWrapper A-Image__preloadWrapper--ratio16to9"> <div itemtype="https://schema.org/ImageObject" itemscope="" itemprop="image"> <meta content="https://www.bosch-ai.com/media/reserach/research_fields/knowledge-driven_problem_solving_res_400x225.webp" itemprop="url" /> <meta content="100%" itemprop="width" /> <meta content="100%" itemprop="height" /> </div> <picture class="A-Image__picture" data-js-component="source-fallback"> <source data-srcset="/media/reserach/research_fields/knowledge-driven_problem_solving_res_400x225.webp, /media/reserach/research_fields/knowledge-driven_problem_solving_res_800x450.webp 2x" media="(min-width: 1200px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/knowledge-driven_problem_solving_res_400x225.webp, /media/reserach/research_fields/knowledge-driven_problem_solving_res_800x450.webp 2x" media="(min-width: 992px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/knowledge-driven_problem_solving_res_400x225.webp, /media/reserach/research_fields/knowledge-driven_problem_solving_res_800x450.webp 2x" media="(min-width: 768px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <source data-srcset="/media/reserach/research_fields/knowledge-driven_problem_solving_res_400x225.webp, /media/reserach/research_fields/knowledge-driven_problem_solving_res_800x450.webp 2x" media="(max-width: 767px)" srcset="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="> <img class="A-Image A-Image--preload lazyload" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" data-srcset="/media/reserach/research_fields/knowledge-driven_problem_solving_res_400x225.webp, /media/reserach/research_fields/knowledge-driven_problem_solving_res_800x450.webp 2x" width="800" height="450" alt="Knowledge-Driven Problem Solving"> </picture> </div> <figcaption class="A-Image__caption">Figure 1</figcaption> </figure> </li> </ul> <section class="M-TextTwo" aria-label="References "> <div class="e-container"> <div class="a-component-header -small"> <div class="a-text"> <h2>References </h2> </div> </div> <div class="M-TextTwo__body"> <div class="M-TextTwo__description"> <div class="A-Text-RichText"> <p>Eiter, T., Higuera, N., Oetsch, J., & Pritz, M. (2022). A Neuro-Symbolic ASP Pipeline for Visual Question Answering. ICLP. <a target="_blank" aria-label="[PDF] opens external site" rel="noopener, noreferrer" href="https://arxiv.org/pdf/2205.07548.pdf"><span>[PDF]<i class="a-icon ui-ic-nosafe-lr-externallink" title="externallink"></i></span></a></p> <p>Eiter, T., Higuera, N., Oetsch, J., & Pritz, M. (2022). A Confidence-Based Interface for Neuro-Symbolic Visual Question Answering. AAAI. <a target="_blank" aria-label="[PDF] opens external site" rel="noopener, noreferrer" href="https://openreview.net/forum?id=_vpnJ0IJJI0"><span>[PDF]<i class="a-icon ui-ic-nosafe-lr-externallink" title="externallink"></i></span></a></p> <p>Eiter, T., Geibinger, T., Musliu, N., Oetsch, J., Skocovský, P., & Stepanova, D. (2021). Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling. KR 2021: 280-290. <a target="_blank" aria-label="[PDF] opens external site" rel="noopener, noreferrer" href="https://proceedings.kr.org/2021/27/"><span>[PDF]<i class="a-icon ui-ic-nosafe-lr-externallink" title="externallink"></i></span></a></p> <p>Eiter, T., Geibinger, T., Ruiz, N.H., Musliu, N., Oetsch, J., & Stepanova, D. (2022). Large-Neighbourhood Search for Optimisation in Answer-Set Solving. AAAI. <a target="_blank" aria-label="[PDF] opens external site" rel="noopener, noreferrer" href="https://www.aaai.org/AAAI22Papers/AAAI-8557.EiterT.pdf"><span>[PDF]<i class="a-icon ui-ic-nosafe-lr-externallink" title="externallink"></i></span></a></p> <p>Eiter, T., Geibinger, T., Ruiz, N.H., Musliu, N., Oetsch, J., & Stepanova, D. (2022). ALASPO: An Adaptive Large-Neighbourhood ASP Optimiser. 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