AIKG-SD 2025 Summe School co-loca ed wi h he NFDI4DS Con e ence 2025
No embe 25-26, 2025, Be lin, Ge many
In oducing Knowledge G aphs o Hie a chical Time Se ies Reconcilia ion
Di k Beine ,1,2 Timm Bönke,1 Ch is ian Menden,3 and Michael Ma in2
1DATEV eG, 2TU Chemni z, 3TH Wü zbu g-Schwein u
Abs ac
A ecen ly published eal- ime sys em o business indica o s based on Ge man SME company
da a o e s new possibili ies o now- and o ecas he economy in de ail and di e en le els o
agg ega ion (sec o s, egions, company size). The da a con ains axes, wages and business
e alua ion da a p ocessed and iled mon hly by ax consul an s displaying a majo sample size
wi hin Ge many (Beine e al 2025). Recen indings on he op imiza ion o o ecas ing
me hods, especially when based on hie a chical da a de eloped mo e insigh s and accu acy
h ough he di e en le els o ime se ies agg ega ions (A hanasopoulos e al, 2024).
Knowledge G aphs (KGs) a e c ucial using a i icial in elligence and especially ques ion-
answe ing (Yucheng e al, 2020). Howe e , hei alue wi hin o he disciplines han compu e
science e.g. economics and o ecas ing only slowly eme ges (Tilly and Li an, 2021). This
p esen a ion ies o combine all h ee aspec s in o a common cogni i e map o he pu poses
o high equency economic da a, i s ad anced o ecas ing possibili ies and a deepe
unde s anding o how de ailed la ge scale business da a helps o analyze he heal h s a us o
companies and he ele an business sec o s. I may open he doo o simula ions and
modelling which migh be supe io o he well-known and ecognized su ey-based business
clima e es ima ions. The pos e will ocus on KG cons uc ion (Mo e al, 2025) and VAR model
aining and p esen s i s esul s.
Re e ences
Beine , D., Bönke, T., and Menden, C., (2025), T acking he Economic Pe o mance o
Ge many‘s SMEs in Real-Time. WIBF 2025.
S ock, J.H., and Wa son, M.W., (2010), Dynamic Fac o Models. Ox o d Handbook o
Economic Fo ecas ing.
Kolassa, Ros ami-Taba and Siemsen (2024), Demand Fo ecas ing o Execu i es and
P o essionals. CRC P ess.
Tilly, S., and Li an, G., (2021), Mac oeconomic Fo ecas ing wi h s a is ically alida ed
Knowledge G aphs. Expe Sys ems wi h Applica ions.
Yucheng. Y, Yue, P, Guanhua, H., and Weinan, E, (2020), The Knowledge G aph o
Mac oeconomic analysis wi h Al e na i e Big da a. a Xi .2010.05172.
A hanasopoulos, Hyndman, Kou en zes and Panagio elis (2024), Fo ecas Reconcilia ion: A
Re iew. In e na ional Jou nal o Fo ecas ing 2025.
Mo, B., Yu, K., Kazdan, J., Mpala, P., Yu, L., Kana soulis, C. I., and Koyejo, S., (2025),
Ex ac ing Knowledge G aphs om Plain Tex wi h Language Models. Neu oIPS
Con e ence 2025.
