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This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph embedding models. Different from the previous translation-based models, TransERR encodes knowledge graphs in the hypercomplex-valued space, thus enabling it to possess a higher degree of translation freedom in mining latent information between the head and tail entities. To further minimize the translation distance, TransERR adaptively rotates the head entity and the tail entity with their corresponding unit quaternions, which are learnable in model training. We also provide mathematical proofs to demonstrate the ability of TransERR in modeling various relation patterns, including symmetry, antisymmetry, inversion, composition, and subrelation patterns. The experiments on 10 benchmark datasets validate the effectiveness and the generalization of TransERR. The results also indicate that TransERR can better encode large-scale datasets with fewer parameters than the previous translation-based models. Our code and datasets are available at~\url{https://github.com/dellixx/TransERR}.

Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are also an essential kind of knowledge in the world, which trigger the spring up of event-centric knowledge representation form like Event KG (EKG). It plays an increasingly important role in many downstream applications, such as search, question-answering, recommendation, financial quantitative investments, and text generation. This paper provides a comprehensive survey of EKG from history, ontology, instance, and application views. Specifically, to characterize EKG thoroughly, we focus on its history, definition, schema induction, acquisition, related representative graphs/systems, and applications. The development processes and trends are studied therein. We further summarize prospective directions to facilitate future research on EKG., Comment: Accepted to TKDE 2022

Organic semiconductors possess an intrinsic energetic disorder characteristic, which holds an exceptionally important role for understanding organic photovoltaic (OPV) operation and future optimization. We performed illumination intensity dependence of capacitance-voltage (C-V) measurements in PIDTDTQx:PC70BM based organic bulk heterojunction (BHJ) photovoltaics in working conditions. Energetic disorder profiles for the photo-active layer, PIDTDTQx:PC70BM, changed significantly when different interfaces were involved. The effects of energetic disorder that could be reflected from C-V profiles are incorporated through an exponential or Gaussian model of density of states (DOS), or a combination of these two. Results underlie that an identical organic blend in BHJ solar cells exhibits different energetic disorder when it interacts with various interfaces. It may, thus, has a certain impact on OPV performances, such as open-circuit voltage (V_oc ). Our study provides device physicists a different perspective view for tailoring the organic energetic disorder parameter via interfaces in order to enhance photo-electron conversion efficiencies (PCE).