%0 Journal Article %1 KI_LIMES_2021 %A Ngonga Ngomo, Axel-Cyrille %A Sherif, Mohamed Ahmed %A Georgala, Kleanthi %A Hassan, Mofeed %A Dreßler, Kevin %A Lyko, Klaus %A Obraczka, Daniel %A Soru, Tommaso %D 2021 %I Springer %J KI - Künstliche Intelligenz, German Journal of Artificial Intelligence - Organ des Fachbereichs "Künstliche Intelligenz" der Gesellschaft für Informatik e.V. %K 2021 dice georgala kevin knowgraphs limbo limboproject limes ngonga opal sage sherif simba slipo %R 10.1007/s13218-021-00713-x %T LIMES - A Framework for Link Discovery on the Semantic Web %U https://papers.dice-research.org/2021/KI_LIMES/public.pdf %X The Linked Data paradigm builds upon the backbone of distributed knowledge bases connected by typed links. The mere volume of current knowledge bases as well as their sheer number pose two major challenges when aiming to support the computation of links across and within them. The first is that tools for link discovery have to be time-efficient when they compute links. Secondly, these tools have to produce links of high quality to serve the applications built upon Linked Data well. Solutions to the second problem build upon efficient computational approaches developed to solve the first and combine these with dedicated machine learning techniques. The current version of the LIMES framework is the product of seven years of research on these two challenges. A series of machine learning techniques and efficient computation approaches were developed and integrated into this framework to address the link discovery problem. The framework combines these diverse algorithms within a generic and extensible architecture. In this article, we give an overview of version 1.7.4 of the open-source release of the framework. In particular, we focus on an overview of the architecture of the framework, an intuition of its inner workings and a brief overview of the approaches it contains. Some descriptions of the applications within which the framework was used complete the paper. Our framework is open-source and available under a GNU license at https: //github.com/dice-group/LIMES together with a user manual and a developer manual.

@article{KI_LIMES_2021, abstract = {The Linked Data paradigm builds upon the backbone of distributed knowledge bases connected by typed links. The mere volume of current knowledge bases as well as their sheer number pose two major challenges when aiming to support the computation of links across and within them. The first is that tools for link discovery have to be time-efficient when they compute links. Secondly, these tools have to produce links of high quality to serve the applications built upon Linked Data well. Solutions to the second problem build upon efficient computational approaches developed to solve the first and combine these with dedicated machine learning techniques. The current version of the LIMES framework is the product of seven years of research on these two challenges. A series of machine learning techniques and efficient computation approaches were developed and integrated into this framework to address the link discovery problem. The framework combines these diverse algorithms within a generic and extensible architecture. In this article, we give an overview of version 1.7.4 of the open-source release of the framework. In particular, we focus on an overview of the architecture of the framework, an intuition of its inner workings and a brief overview of the approaches it contains. Some descriptions of the applications within which the framework was used complete the paper. Our framework is open-source and available under a GNU license at https: //github.com/dice-group/LIMES together with a user manual and a developer manual.}, added-at = {2023-04-25T16:34:32.000+0200}, author = {{Ngonga Ngomo}, Axel-Cyrille and Sherif, Mohamed Ahmed and Georgala, Kleanthi and Hassan, Mofeed and Dre{\ss}ler, Kevin and Lyko, Klaus and Obraczka, Daniel and Soru, Tommaso}, bdsk-url-1 = {https://papers.dice-research.org/2021/KI_LIMES/public.pdf}, bdsk-url-2 = {https://doi.org/10.1007/s13218-021-00713-x}, biburl = {https://www.bibsonomy.org/bibtex/2b578236d6613b17a41f78899d1b88d72/dice-research}, doi = {10.1007/s13218-021-00713-x}, interhash = {e8bfd751ec3b676534f6fab0a1211e22}, intrahash = {b578236d6613b17a41f78899d1b88d72}, journal = {KI - K{\"u}nstliche Intelligenz, German Journal of Artificial Intelligence - Organ des Fachbereichs "K{\"u}nstliche Intelligenz" der Gesellschaft f{\"u}r Informatik e.V.}, keywords = {2021 dice georgala kevin knowgraphs limbo limboproject limes ngonga opal sage sherif simba slipo}, owner = {sherif}, publisher = {Springer}, timestamp = {2023-04-25T16:34:32.000+0200}, title = {{LIMES - A Framework for Link Discovery on the Semantic Web}}, url = {https://papers.dice-research.org/2021/KI_LIMES/public.pdf}, year = 2021 }