DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing

In the field of representation learning on knowledge graphs (KGs), a hyper-relational fact consists of a main triple and several auxiliary attribute-value descriptions, which is considered more comprehensive and specific than a triple-based fact. However, currently available hyper-relational KG embedding methods in a single view are limited in application because they weaken the hierarchical structure that represents the affiliation between entities. To overcome this limitation, we propose a dual-view hyper-relational KG structure (DH-KG) that contains a hyper-relational instance view for entities and a hyper-relational ontology view for concepts that are abstracted hierarchically from the entities. This paper defines link prediction and entity typing tasks on DH-KG for the first time and constructs two DH-KG datasets, JW44K-6K, extracted from Wikidata, and HTDM based on medical data. Furthermore, we propose DHGE, a DH-KG embedding model based on GRAN encoders, HGNNs, and joint learning. DHGE outperforms baseline models on DH-KG, according to experimental results. Finally, we provide an example of how this technology can be used to treat hypertension. Our model and new datasets are publicly available.Comment: Accepted by AAAI 202

Functions of the Clostridium acetobutylicium FabF and FabZ proteins in unsaturated fatty acid biosynthesis

<p>Abstract</p> <p>Background</p> <p>The original anaerobic unsaturated fatty acid biosynthesis pathway proposed by Goldfine and Bloch was based on in <it>vivo </it>labeling studies in <it>Clostridium butyricum </it>ATCC 6015 (now <it>C. beijerinckii</it>) but to date no dedicated unsaturated fatty acid biosynthetic enzyme has been identified in Clostridia. <it>C. acetobutylicium </it>synthesizes the same species of unsaturated fatty acids as <it>E. coli</it>, but lacks all of the known unsaturated fatty acid synthetic genes identified in <it>E. coli </it>and other bacteria. A possible explanation was that two enzymes of saturated fatty acid synthesis of <it>C. acetobutylicium</it>, FabZ and FabF might also function in the unsaturated arm of the pathway (a FabZ homologue is known to be an unsaturated fatty acid synthetic enzyme in enterococci).</p> <p>Results</p> <p>We report that the FabF homologue located within the fatty acid biosynthetic gene cluster of <it>C. acetobutylicium </it>functions in synthesis of both unsaturated fatty acids and saturated fatty acids. Expression of this protein in <it>E. coli </it>functionally replaced both the FabB and FabF proteins of the host in <it>vivo </it>and replaced <it>E. coli </it>FabB in a defined in <it>vitro </it>fatty acid synthesis system. In contrast the single <it>C. acetobutylicium </it>FabZ homologue, although able to functionally replace <it>E. coli </it>FabZ in <it>vivo </it>and in <it>vitro</it>, was unable to replace FabA, the key dehydratase-isomerase of <it>E. coli </it>unsaturated fatty acid biosynthesis in <it>vivo </it>and lacked isomerase activity in <it>vitro</it>.</p> <p>Conclusion</p> <p>Thus, <it>C. acetobutylicium </it>introduces the double of unsaturated fatty acids by use of a novel and unknown enzyme.</p

A Neurotrophin Signaling Cascade Coordinates Sympathetic Neuron Development through Differential Control of TrkA Trafficking and Retrograde Signaling

AbstractA fundamental question in developmental biology is how a limited number of growth factors and their cognate receptors coordinate the formation of tissues and organs endowed with enormous morphological complexity. We report that the related neurotrophins NGF and NT-3, acting through a common receptor, TrkA, are required for sequential stages of sympathetic axon growth and, thus, innervation of target fields. Yet, while NGF supports TrkA internalization and retrograde signaling from distal axons to cell bodies to promote neuronal survival, NT-3 cannot. Interestingly, final target-derived NGF promotes expression of the p75 neurotrophin receptor, in turn causing a reduction in the sensitivity of axons to intermediate target-derived NT-3. We propose that a hierarchical neurotrophin signaling cascade coordinates sequential stages of sympathetic axon growth, innervation of targets, and survival in a manner dependent on the differential control of TrkA internalization, trafficking, and retrograde axonal signaling

NQE: N-ary Query Embedding for Complex Query Answering over Hyper-relational Knowledge Graphs

Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs). Currently, most approaches are limited to queries among binary relational facts and pay less attention to n-ary facts (n>=2) containing more than two entities, which are more prevalent in the real world. Moreover, previous CQA methods can only make predictions for a few given types of queries and cannot be flexibly extended to more complex logical queries, which significantly limits their applications. To overcome these challenges, in this work, we propose a novel N-ary Query Embedding (NQE) model for CQA over hyper-relational knowledge graphs (HKGs), which include massive n-ary facts. The NQE utilizes a dual-heterogeneous Transformer encoder and fuzzy logic theory to satisfy all n-ary FOL queries, including existential quantifiers, conjunction, disjunction, and negation. We also propose a parallel processing algorithm that can train or predict arbitrary n-ary FOL queries in a single batch, regardless of the kind of each query, with good flexibility and extensibility. In addition, we generate a new CQA dataset WD50K-NFOL, including diverse n-ary FOL queries over WD50K. Experimental results on WD50K-NFOL and other standard CQA datasets show that NQE is the state-of-the-art CQA method over HKGs with good generalization capability. Our code and dataset are publicly available.Comment: Accepted by the 37th AAAI Conference on Artificial Intelligence (AAAI-2023

ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models

Knowledge Base Question Answering (KBQA) aims to derive answers to natural language questions over large-scale knowledge bases (KBs), which are generally divided into two research components: knowledge retrieval and semantic parsing. However, three core challenges remain, including inefficient knowledge retrieval, retrieval errors adversely affecting semantic parsing, and the complexity of previous KBQA methods. In the era of large language models (LLMs), we introduce ChatKBQA, a novel generate-then-retrieve KBQA framework built on fine-tuning open-source LLMs such as Llama-2, ChatGLM2 and Baichuan2. ChatKBQA proposes generating the logical form with fine-tuned LLMs first, then retrieving and replacing entities and relations through an unsupervised retrieval method, which improves both generation and retrieval more straightforwardly. Experimental results reveal that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets, WebQSP, and ComplexWebQuestions (CWQ). This work also provides a new paradigm for combining LLMs with knowledge graphs (KGs) for interpretable and knowledge-required question answering. Our code is publicly available.Comment: Preprin

Reduced cortical thickness in right Heschl’s gyrus associated with auditory verbal hallucinations severity in first-episode schizophrenia

BACKGROUND: Auditory verbal hallucinations (AVHs) represent one of the most intriguing phenomena in schizophrenia, however, brain abnormalities underlying AVHs remain unclear. The present study examined the association between cortical thickness and AVHs in first-episode schizophrenia. METHOD: High-resolution MR images were obtained in 49 first-episode schizophrenia (FES) patients and 50 well-matched healthy controls (HCs). Among the FES patients, 18 suffered persistent AVHs (“auditory hallucination” AH group), and 31 never experienced AVHs (“no hallucination” NH group). The severity of AVHs was rated by the Auditory Hallucinations Rating Scale (AHRS). Cortical thickness differences among the three groups and their association with AVHs severity were examined. RESULTS: Compared to both HCs and NH patients, AH patients showed lower cortical thickness in the right Heschl’s gyrus. The degree of reduction in the cortical thickness was correlated with AVH severity in the AH patients. CONCLUSIONS: Abnormalities of cortical thickness in the Heschl’s gyrus may be a physiological factor underlying auditory verbal hallucinations in schizophrenia. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12888-015-0546-2) contains supplementary material, which is available to authorized users

Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs still significantly relies on manual labor, and n-ary relation extraction still remains at a course-grained level, which is always in a single schema and fixed arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. We introduce a span-tuple classification approach with hetero-ordered merging to accomplish fine-grained n-ary relation extraction in different arity. Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational schema, event-based schema, role-based schema, and hypergraph-based schema, with high flexibility and practicality. Experimental results demonstrate that Text2NKG outperforms the previous state-of-the-art model by nearly 20\% points in the $F_1$ scores on the fine-grained n-ary relation extraction benchmark in the hyper-relational schema. Our code and datasets are publicly available.Comment: Preprin