content adjustments

_includes/postslist.njk

@@ -5,7 +5,7 @@
 		<span class="postlist-link">{{post.data.title}}</span>
 		<time class="postlist-date" datetime="{{ post.date | htmlDateString }}">{{ post.date | readableDate("LLLL yyyy") }}</time>
 		<div>{{post.data.page.excerpt | markdownify | safe}}</div>
-		(<a class="color-gray-50" href="{{ post.url }}">Read More</a>)&nbsp;&nbsp;(<a class="color-gray-50" href="{{ post.data.linkedInUrl }}">LinkedIn Post</a>)
+		(<a class="color-gray-50" href="{{ post.url }}">Read More</a>)&nbsp;&nbsp;(<a class="color-gray-50" href="{{ post.data.linkedInUrl }}">ResearchGate/LinkedIn</a>)
 	</li>
 {% endfor %}
 </ol>

content/research/160901_bachelor_thesis_the_development_of_complexity_in_chip_design_and_its_visualization_within_virtual_reality/index.md

@@ -1,12 +1,11 @@
 ---
 title: The development of complexity in chip design and its visualization within Virtual Reality
-description: Bachelor Thesis - Carsten Felix Draschner in colab with Jonas Weinz ❤️
 date: 2016-09-30
 linkedInUrl: https://youtu.be/bwYKVCDIPbE?si=EflQEBoH0PjjDvKD&t=373
 tags: research
 ---
 
-This research project presents a novel approach to visualizing complex chip designs using Virtual Reality (VR) technology.
+Chip-Design Exhibition in Arithmeum and Bachelor Thesis - Carsten Felix Draschner in colab with Jonas Weinz ❤️
 
 ![Model Size](/img/research_images/bt.png)
 

content/research/190401_smart_chef_evolving_recipes/index.md

@@ -1,12 +1,11 @@
 ---
 title: Smart Chef - Evolving Recipes
-description: Master Thesis - Carsten Felix Draschner
 date: 2019-04-01
 linkedInUrl: https://www.researchgate.net/publication/357910428_SmartChef_Evolving_Recipes_Poster
 tags: research
 ---
 
-Smart Chef demonstrates the creativity of evolution in culinary arts by autonomously evolving novel and human readable recipes.
+Proceedings of Evostar 2019 and Master Thesis - Carsten Felix Draschner supervised by Hajira Jabeen, Jens Lehmann
 
 ![Smart Chef](/img/research_images/mt.png)
 

content/research/200901_distsim_scalable_distributed_in_memory/index.md

@@ -1,12 +1,11 @@
 ---
 title: DistSim - Scalable Distributed in-Memory Semantic Similarity Estimation for RDF Knowledge Graphs
-description: 2021 IEEE 15th International Conference on Semantic Computing (ICSC), Carsten Felix Draschner, Jens Lehmann and Hajira Jabeen
 date: 2021-01-01
 linkedInUrl: https://ieeexplore.ieee.org/document/9364473
 tags: research
 ---
 
-A semantic similarity estimation extension to SANSA.
+2021 IEEE 15th International Conference on Semantic Computing (ICSC), Carsten Felix Draschner, Jens Lehmann and Hajira Jabeen
 
 ![DistSim](/img/research_images/distsim.png)
 

content/research/210401_literal2feature/index.md

@@ -1,12 +1,11 @@
 ---
 title: Literal2Feature - An automatic scalable RDF graph feature extractor
-description: International Conference on Semantic Systems (SEMANTICS), Farshad B. Moghaddam, Carsten Felix Draschner, Jens Lehmann and Hajira Jabeen
 date: 2021-08-01
 linkedInUrl: https://ebooks.iospress.nl/volumearticle/57407
 tags: research
 ---
 
-A generic, distributed, and scalable software framework for translating massive RDF data into an explainable feature matrix.
+International Conference on Semantic Systems (SEMANTICS), Farshad B. Moghaddam, Carsten Felix Draschner, Jens Lehmann and Hajira Jabeen
 
 ![Literal2Feature](/img/research_images/literal2feature.png)
 

content/research/210501_distrdf2ml/index.md

@@ -1,12 +1,11 @@
 ---
 title: DistRDF2ML - Scalable Distributed In-Memory Machine Learning Pipelines for RDF Knowledge Graphs
-description: Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM), Carsten Felix Draschner, Claus Stadler, Farshad B. Moghaddam, Jens Lehmann, and Hajira Jabeen
 date: 2021-12-01
 linkedInUrl: https://dl.acm.org/doi/10.1145/3459637.3481999
 tags: research
 ---
 
-A scalable and distributed framework for building in-memory data preprocessing pipelines for Spark-based ML on RDF Knowledge Graphs.
+Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM), Carsten Felix Draschner, Claus Stadler, Farshad B. Moghaddam, Jens Lehmann, and Hajira Jabeen
 
 ![DistRDF2ML](/img/research_images/distrdf2ml.png)
 

content/research/220902_ethical_and_sustainability/index.md

@@ -1,12 +1,11 @@
 ---
 title: Ethical and Sustainability considerations for Knowledge Graphs based Machine Learning
-description: 2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Carsten Felix Draschner, Hajira Jabeen, Jens Lehmann
 date: 2022-09-01
 linkedInUrl: https://ieeexplore.ieee.org/document/9939282
 tags: research
 ---
 
-Ethical and sustainability considerations in KG-based ML.
+2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Carsten Felix Draschner, Hajira Jabeen, Jens Lehmann
 
 ![Ethical and Sustainability considerations for Knowledge Graphs based Machine Learning](/img/research_images/kg_ethics.png)
 

content/research/221214_scalable_distributed_machine_learning_for_knowledge_graphs/index.md

@@ -1,12 +1,11 @@
 ---
 title: Scalable Distributed Machine Learning for Knowledge Graphs
-description: PhD Thesis - Carsten Felix Draschner, Dr.rer.nat, supervisors - Jens Lehmann and Stefan Wrobel
 date: 2023-07-01
 linkedInUrl: https://bonndoc.ulb.uni-bonn.de/xmlui/handle/20.500.11811/10945
 tags: research
 ---
 
-Within this work, we developed novel approaches for Machine Learning on Knowledge Graphs while considering ethical and sustainability dimensions.
+PhD Thesis - Carsten Felix Draschner, Dr.rer.nat, supervisors - Jens Lehmann and Stefan Wrobel
 
 ![PhD Thesis](/img/research_images/PhD.jpg)
 
@@ -17,4 +16,4 @@ Within this work, we developed novel approaches for Machine Learning on Knowledg
 
 <!-- excerpt -->
 
-In particular, we developed technologies that create fixed-length numeric feature vectors. These include methods that, like graph kernels, extract features from the graph in the context of the map-reduce operations relevant for distributed computation. The feature extraction also includes the multi-modal data of KG literals. Accordingly, we have developed methods that enable SPARQL-based feature extraction and assist in creating complex feature-extracting queries. Based on these extracted features, we further contributed scalable, distributed, and explainable ML and data analytics methods such as semantic similarity estimation and classification or regression ML pipelines demonstrating noticeable performance. We support the transparency, reusability, and reproducibility of our novel open-source approaches by results and meta-data semantification. This semantification transfers the original graph data with the hyper-parameter setup and explainability information, in addition to the predicted results of the ML pipelines, into a semantic native KG. Due to the technological complexity, we enable the application of our algorithm technologies through complementary work such as the use in coding notebooks and the use in Rest API-based environments. Our work also describes the multidimensional and interwoven optimization dimensions of ethical and sustainable KG-based ML. We extended the existing technology stack SANSA, which is used for distributed processing and native semantic data handling, by several scientific publications and software framework releases to offer these functionalities for distributed ML on KGs.
+Within this work, we developed novel approaches for Machine Learning on Knowledge Graphs while considering ethical and sustainability dimensions. In particular, we developed technologies that create fixed-length numeric feature vectors. These include methods that, like graph kernels, extract features from the graph in the context of the map-reduce operations relevant for distributed computation. The feature extraction also includes the multi-modal data of KG literals. Accordingly, we have developed methods that enable SPARQL-based feature extraction and assist in creating complex feature-extracting queries. Based on these extracted features, we further contributed scalable, distributed, and explainable ML and data analytics methods such as semantic similarity estimation and classification or regression ML pipelines demonstrating noticeable performance. We support the transparency, reusability, and reproducibility of our novel open-source approaches by results and meta-data semantification. This semantification transfers the original graph data with the hyper-parameter setup and explainability information, in addition to the predicted results of the ML pipelines, into a semantic native KG. Due to the technological complexity, we enable the application of our algorithm technologies through complementary work such as the use in coding notebooks and the use in Rest API-based environments. Our work also describes the multidimensional and interwoven optimization dimensions of ethical and sustainable KG-based ML. We extended the existing technology stack SANSA, which is used for distributed processing and native semantic data handling, by several scientific publications and software framework releases to offer these functionalities for distributed ML on KGs.

content/research/230701_sime4kg/index.md

@@ -1,12 +1,11 @@
 ---
 title: SimE4KG - Distributed Explainable multi-modal Semantic Similarity Estimation for Knowledge Graphs
-description: 2023 International Journal of Semantic Computing, Carsten Felix Draschner, Hajira Jabeen, Jens Lehmann
 date: 2023-02-01
 linkedInUrl: https://doi.org/10.1142/S1793351X23600012
 tags: research
 ---
 
-A semantic similarity estimation extension for multoi modal Knowledge structures to SANSA.
+2023 International Journal of Semantic Computing, Carsten Felix Draschner, Hajira Jabeen, Jens Lehmann
 
 ![SimE4KG](/img/research_images/sime.png)