From a1f38268ddfb8910ff3b09bfb0db82e6e9ff3012 Mon Sep 17 00:00:00 2001 From: Lars-Peter Meyer Date: Mon, 17 Jun 2024 13:02:28 +0200 Subject: [PATCH] added eswc2024 papers --- aksw.bib | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/aksw.bib b/aksw.bib index 518f21c..1706315 100644 --- a/aksw.bib +++ b/aksw.bib @@ -12506,4 +12506,41 @@ @InProceedings{icsc2023natuke keywords = {carmo marx marcacini valli silva-silva pilon ls}, } +@InProceedings{Brei2024Leveragingsmalllanguage, + author = {Brei, Felix and Frey, Johannes and Meyer, Lars-Peter}, + booktitle = {Proceedings of the Third International Workshop on Linked Data-driven Resilience Research 2024 (D2R2'24), colocated with ESWC 2024}, + title = {Leveraging small language models for Text2SPARQLtasks to improve the resilience of AI assistance}, + year = {2024}, + editor = {Julia Holze and Sebastian Tramp and Michael Martin and Sören Auer and Ricardo Usbeck and Nenad Krdzavac}, + series = {CEUR-WS}, + volume = {3707}, + abstract = {In this work we will show that language models with less than one billion parameters can be used to translate natural language to SPARQL queries after fine-tuning. Using three different datasets ranging from academic to real world, we identify prerequisites that the training data must fulfill in order for the training to be successful. The goal is to empower users of semantic web technology to use AI assistance with affordable commodity hardware, making them more resilient against external factors}, + doi = {10.48550/arXiv.2405.17076}, + keywords = {group_aksw sys:relevantFor:infai frey lpmeyer}, + url = {https://ceur-ws.org/Vol-3707/D2R224_paper_5.pdf}, +} + +@InProceedings{Frey2024AssessingEvolutionLLM, + author = {Johannes Frey and Lars-Peter Meyer and Felix Brei and Sabine Gruender and Michael Martin}, + booktitle = {Proceedings of Special Track Large Language Models for Knowledge Engineering at Extended Semantic Web Conference 2024 (ESWC24)}, + title = {Assessing the Evolution of LLM capabilities for Knowledge Graph Engineering in 2023}, + year = {2024}, + abstract = {In this study, we evaluate the evolution of LLM capabilities w.r.t. the RDF Turtle and SPARQL language as foundational skills to assist with various KGE tasks. We measure the LLM response quality using 6 LLM-KG-Bench tasks for a total of 15 LLM versions available over the course of 2023, covering 5 different “major version” LLM classes (GPT3.5 Turbo, GPT4, Claude-1.x, Claude-2.x, and Claude-instant-1.x).}, + keywords = {group_aksw sys:relevantFor:infai frey lpmeyer martin}, + url = {https://2024.eswc-conferences.org/wp-content/uploads/2024/05/77770050.pdf}, +} + +@InProceedings{Kilic2024TowardsRegionalPublic, + author = {Fatih Kılıç and Till Grabo and Julia Lücke and Norman Radtke and Christian Danne and Sabine Gründer-Fahrer and Michael Martin}, + booktitle = {Proceedings of the Third International Workshop on Linked Data-driven Resilience Research 2024 (D2R2'24), colocated with ESWC 2024}, + title = {Towards a Regional Public Dashboard for Crisis andResilience Management}, + year = {2024}, + editor = {Julia Holze and Sebastian Tramp and Michael Martin and Sören Auer and Ricardo Usbeck and Nenad Krdzavac}, + series = {CEUR-WS}, + volume = {3707}, + abstract = {The paper presents ongoing work on a public dashboard that displays the trade relationships of a regional economy in Germany (Saxony) and uses semantic data integration techniques to connect it with localized information on global crisis events in supplying countries. Furthermore, it quantifies the impact of external supply shocks on (subregions of) the Saxon economy in quasi-real time and provides estimates of changes in macroeconomic determinants based on a regional input-output model. The dashboard will be a public resource to support decision makers from politics, business and administration in mitigating the effects of crises and improving regional resilience.}, + keywords = {group_aksw sys:relevantFor:infai kilic grabo radtke martin}, + url = {https://ceur-ws.org/Vol-3707/D2R224_paper_7.pdf}, +} + @Comment{jabref-meta: databaseType:bibtex;}