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Hakken: Predicting future discoveries to fill the gaps in today's knowledge.
Tarek R. Besold, Uchenna Akujuobi,
Pablo Sanchez, Alessandra Toniato, Kana Maruyama, Jihun Choi, Samy Badreddine, Frederick Gifford, Daniel Evans-Yamamoto, Sucheendra
K. Palaniappan, Miquel Ferrer, Kae Nagano, Iris Rossell, Tom Joy, Hatem ElShazly, Chrysa Iliopoulou, Christoph Wehner, Thiviyan
Thanapalasingam, Susana Nunes, Pedro G. Cotovio, Peter Wurman, Peter
Stone, Hiroaki Kitano, and Michael Spranger.
Technical Report
arXiv e-Prints 2609.04494, arXiv, 2026.
arXiv version
(unavailable)
We present Hakken, a domain-agnostic prediction and explanation system performing knowledge prediction, i.e., growing scientific knowledge by establishing novel relationships, ones that are not limited to the deductive hull of previous knowledge. Hakken uses a transformer-based prediction model built on temporal sequences of knowledge graphs extracted from vast bodies of research publications, fused with an LLM's semantic knowledge, to predict the presence and define the type of as-yet undocumented relationships between scientific concepts. It then calls a model-agnostic explanation framework to provide accompanying information for each prediction that allows scientists to evaluate the suggested new relationship. While general purpose, we demonstrate Hakken's practical capabilities by applying it to the biomedical domain. There, Hakken's prediction model establishes a new benchmark for time-aware multi-label relation prediction, and we show that the model's output stays coherent and informative over extended time spans in historic data. In addition, we scored 1.5 million above-confidence-threshold hypotheses related to aging, qualitatively validated batches of these predictions with biologists and progressed three of them for empirical validation in wet-lab. Two predictions with potentially significant impact in the context of drug discovery and repurposing were confirmed, introducing previously undocumented interactions between TP53 and BAMBI, and between RAF1 and TNF, to biomedical science.
@Techreport{hakken_2026,
author = {Tarek R. Besold and Uchenna Akujuobi and Pablo Sanchez and Alessandra Toniato and Kana Maruyama and Jihun Choi and Samy Badreddine and Frederick Gifford and Daniel Evans-Yamamoto and Sucheendra K. Palaniappan and Miquel Ferrer and Kae Nagano and Iris Rossell and Tom Joy and Hatem ElShazly and Chrysa Iliopoulou and Christoph Wehner and Thiviyan Thanapalasingam and Susana Nunes and Pedro G. Cotovio and Peter Wurman and Peter Stone and Hiroaki Kitano and Michael Spranger},
title = {Hakken: {P}redicting future discoveries to fill the gaps in today's knowledge},
institution = "arXiv",
number = "arXiv e-Prints 2609.04494",
year = {2026},
abstract = {
We present Hakken, a domain-agnostic prediction and
explanation system performing knowledge prediction,
i.e., growing scientific knowledge by establishing novel
relationships, ones that are not limited to the
deductive hull of previous knowledge. Hakken uses a
transformer-based prediction model built on temporal
sequences of knowledge graphs extracted from vast bodies
of research publications, fused with an LLM's semantic
knowledge, to predict the presence and define the type
of as-yet undocumented relationships between scientific
concepts. It then calls a model-agnostic explanation
framework to provide accompanying information for each
prediction that allows scientists to evaluate the
suggested new relationship. While general purpose, we
demonstrate Hakken's practical capabilities by applying
it to the biomedical domain. There, Hakken's prediction
model establishes a new benchmark for time-aware
multi-label relation prediction, and we show that the
model's output stays coherent and informative over
extended time spans in historic data. In addition, we
scored 1.5 million above-confidence-threshold hypotheses
related to aging, qualitatively validated batches of
these predictions with biologists and progressed three
of them for empirical validation in wet-lab. Two
predictions with potentially significant impact in the
context of drug discovery and repurposing were
confirmed, introducing previously undocumented
interactions between TP53 and BAMBI, and between RAF1
and TNF, to biomedical science.
},
wwwnote={<a href="https://arxiv.org/abs/2609.04494">arXiv version</a>},
}
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