Holographic Embeddings of Knowledge Graphs

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator HolE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. In extensive experiments we show that holographic embeddings are able to outperform state-of-the-art methods for link prediction in knowledge graphs and relational learning benchmark datasets.Comment: To appear in AAAI-1

Neurophysiological mechanisms of longer-lasting experimental pain in humans

Pain serves the protection of the body. Consequently, noxious stimuli or, more precisely, the thereby induced neurophysiological processes commonly lead to pain perception. Identical noxious stimuli, however, do not always translate into the same pain experience depending on multiple factors. To acknowledge this variability, the distinction between nociception as the neural process elicited by noxious stimuli and pain as subjective multifactorial experience is essential. During longer-lasting experimental pain and chronic pain, nociception and pain can substantially dissociate. Moreover, longer-lasting experimental pain resembles chronic pain regarding certain perceptual features such as prolonged pain duration and intensity fluctuations. Thus, longer-lasting experimental pain offers the opportunity to gain new insights into both the differential neural representation of noxious stimuli and pain and the neuronal mechanisms associated with the state of longer- lasting pain. We applied 10 minutes of painful heat stimulation to the left and right hand of 39 healthy participants while we recorded continuous pain ratings, electroencephalography (EEG), and autonomic responses. Data were analyzed in three distinct projects aiming at different aspects of neuronal mechanisms underlying longer-lasting pain. Project 1 assessed whether stimulus intensity as proxy of nociception and pain intensity relate to distinct patterns of oscillatory brain activity. EEG recordings revealed that increases in stimulus intensity were reflected by suppressions of alpha and beta oscillations in sensorimotor areas contralateral to the stimulated hand. In contrast, increases in pain intensity were associated with enhanced gamma oscillations in the medial prefrontal cortex. More importantly, the encoding of stimulus intensity by alpha and beta oscillations in the sensorimotor areas was spatially specific, i.e. depended on the stimulus location, whereas the encoding of pain intensity by gamma oscillations in the medial prefrontal cortex was independent of stimulus location. Thus, prefrontal gamma oscillations might reflect higher- order aspects of noxious stimuli, such as salience, valence, and motivational aspects rather than precise sensory features. Project 2 investigated the relationship between stimulus intensity, pain intensity, autonomic responses, and brain activity. Skin conductance measures, as markers of sympathetic activity, co-varied more closely with stimulus intensity than with pain intensity. Correspondingly, skin conductance measures were related to suppressions of alpha and beta oscillations in the sensorimotor area contralateral to the stimulated hand. These finding suggest that skin conductance measures are in part directly elicited by nociceptive processes and, thus, at least partially independent of perceptual processes during longer-lasting pain. Hence, these observations corroborate concepts of pain in which sensory, motivational, and autonomic processes partially independently contribute to the experience of pain. Finally, project 3 incorporated the systematic and comprehensive assessment of oscillatory brain activity, functional connectivity, and graph- theory based network measures during the state of longer-lasting pain. Longer-lasting pain was associated with suppressions of oscillatory brain activity at alpha frequencies in addition to stronger connectivity at alpha and beta frequencies in sensorimotor areas. Furthermore, sensorimotor areas contralateral to stimulation showed increased connectivity to a common area in the medial prefrontal cortex at alpha frequencies and built a sensorimotor-prefrontal network during longer-lasting pain. This network might be involved in the integration of sensory, cognitive, and motivational-affective information and, consequently, in the translation of a noxious stimulus into a subjective pain experience. All three projects contribute to a better understanding of neuronal mechanisms underlying longer-lasting experimental pain, which serves as an experimental model for chronic pain. Since the treatment of chronic pain has remained insufficient and unsatisfactory, the current results might provide EEG-based targets for urgently needed new treatment approaches, such as non-invasive brain stimulation and neurofeedback

Neurophysiological mechanisms of longer-lasting experimental pain in humans

Pain serves the protection of the body. Consequently, noxious stimuli or, more precisely, the thereby induced neurophysiological processes commonly lead to pain perception. Identical noxious stimuli, however, do not always translate into the same pain experience depending on multiple factors. To acknowledge this variability, the distinction between nociception as the neural process elicited by noxious stimuli and pain as subjective multifactorial experience is essential. During longer-lasting experimental pain and chronic pain, nociception and pain can substantially dissociate. Moreover, longer-lasting experimental pain resembles chronic pain regarding certain perceptual features such as prolonged pain duration and intensity fluctuations. Thus, longer-lasting experimental pain offers the opportunity to gain new insights into both the differential neural representation of noxious stimuli and pain and the neuronal mechanisms associated with the state of longer- lasting pain. We applied 10 minutes of painful heat stimulation to the left and right hand of 39 healthy participants while we recorded continuous pain ratings, electroencephalography (EEG), and autonomic responses. Data were analyzed in three distinct projects aiming at different aspects of neuronal mechanisms underlying longer-lasting pain. Project 1 assessed whether stimulus intensity as proxy of nociception and pain intensity relate to distinct patterns of oscillatory brain activity. EEG recordings revealed that increases in stimulus intensity were reflected by suppressions of alpha and beta oscillations in sensorimotor areas contralateral to the stimulated hand. In contrast, increases in pain intensity were associated with enhanced gamma oscillations in the medial prefrontal cortex. More importantly, the encoding of stimulus intensity by alpha and beta oscillations in the sensorimotor areas was spatially specific, i.e. depended on the stimulus location, whereas the encoding of pain intensity by gamma oscillations in the medial prefrontal cortex was independent of stimulus location. Thus, prefrontal gamma oscillations might reflect higher- order aspects of noxious stimuli, such as salience, valence, and motivational aspects rather than precise sensory features. Project 2 investigated the relationship between stimulus intensity, pain intensity, autonomic responses, and brain activity. Skin conductance measures, as markers of sympathetic activity, co-varied more closely with stimulus intensity than with pain intensity. Correspondingly, skin conductance measures were related to suppressions of alpha and beta oscillations in the sensorimotor area contralateral to the stimulated hand. These finding suggest that skin conductance measures are in part directly elicited by nociceptive processes and, thus, at least partially independent of perceptual processes during longer-lasting pain. Hence, these observations corroborate concepts of pain in which sensory, motivational, and autonomic processes partially independently contribute to the experience of pain. Finally, project 3 incorporated the systematic and comprehensive assessment of oscillatory brain activity, functional connectivity, and graph- theory based network measures during the state of longer-lasting pain. Longer-lasting pain was associated with suppressions of oscillatory brain activity at alpha frequencies in addition to stronger connectivity at alpha and beta frequencies in sensorimotor areas. Furthermore, sensorimotor areas contralateral to stimulation showed increased connectivity to a common area in the medial prefrontal cortex at alpha frequencies and built a sensorimotor-prefrontal network during longer-lasting pain. This network might be involved in the integration of sensory, cognitive, and motivational-affective information and, consequently, in the translation of a noxious stimulus into a subjective pain experience. All three projects contribute to a better understanding of neuronal mechanisms underlying longer-lasting experimental pain, which serves as an experimental model for chronic pain. Since the treatment of chronic pain has remained insufficient and unsatisfactory, the current results might provide EEG-based targets for urgently needed new treatment approaches, such as non-invasive brain stimulation and neurofeedback

Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings

We consider the task of inferring is-a relationships from large text corpora. For this purpose, we propose a new method combining hyperbolic embeddings and Hearst patterns. This approach allows us to set appropriate constraints for inferring concept hierarchies from distributional contexts while also being able to predict missing is-a relationships and to correct wrong extractions. Moreover -- and in contrast with other methods -- the hierarchical nature of hyperbolic space allows us to learn highly efficient representations and to improve the taxonomic consistency of the inferred hierarchies. Experimentally, we show that our approach achieves state-of-the-art performance on several commonly-used benchmarks