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

A randomised phase I study of etrolizumab (rhuMAb β7) in moderate to severe ulcerative colitis.

ObjectiveEtrolizumab (rhuMAb β7, anti-β7, PRO145223) is a humanised monoclonal antibody targeting the β7 subunit of the heterodimeric integrins α4β7 and αEβ7, which are implicated in leucocyte migration and retention in ulcerative colitis (UC). This randomised phase I study evaluated the safety and pharmacology of etrolizumab in patients with moderate to severe UC.DesignIn the single ascending dose (SAD) stage, etrolizumab (0.3, 1.0, 3.0, 10 mg/kg intravenous, 3.0 mg/kg subcutaneous (SC) or placebo) was administered 4:1 (n=25) in each cohort. In the multiple dose (MD) stage, new patients received monthly etrolizumab (0.5 mg/kg SC (n=4), 1.5 mg/kg SC (n=5), 3.0 mg/kg SC (n=4), 4.0 mg/kg intravenous (n=5)) or placebo (n=5). The pharmacokinetics was studied and Mayo Clinic Score evaluated at baseline, day 29 (SAD), and days 43 and 71 (MD).ResultsIn the SAD stage, there were no dose limiting toxicities, infusion or injection site reactions. Two impaired wound healing serious adverse events occurred in two patients receiving etrolizumab. In the MD stage, there were no dose limiting toxicities, and no infusion or injection site reactions. Headache was the most common adverse event, occurring more often in etrolizumab patients. Antietrolizumab antibodies were detected in two subjects. The duration of β7 receptor full occupancy was dose related. A clinical response was observed in 12/18 patients, and clinical remission in 3/18 patients treated with etrolizumab in the MD stage, compared with 4/5 and 1/5 placebo patients, respectively.ConclusionEtrolizumab is well tolerated in moderate to severe UC. Further investigation is warranted

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

Changes of Adult Population Health Status in China from 2003 to 2008

Objectives: The purpose of this study was to examine the change in health status of China’s adult population between the years of 2003 and 2008 due to rapid economic growth and medical system improvement. Methods: Data from the third and fourth Chinese national health services surveys covering 141,927 residents in 2003 and 136,371 residents in 2008 who were aged.18 years were analyzed. Results: Chinese respondents in 2008 were more likely to report disease than in 2003. Smoking slightly decreased among men and women, and regular exercise showed much improvement. Stratified analyses revealed significant subpopulation disparities in rate ratios for 2008/2003 in the presence of chronic disease, with greater increases among women, elderly, the Han nationality, unmarried and widow, illiterate, rural, and regions east of China than other groups. Conclusions: Chinese adults in 2008 had worse health status than in 2003 in terms of presence of chronic disease. China’s reform of health care will face more complex challenges in coming years from the deteriorating health status in Chinese adults

MOOC instructor motivation and career development

As MOOCs proliferate, a better understanding of MOOC instructors is essential. This study examined the motivation as well as the career and professional development of 142 MOOC instructors using a mixed-methods approach entailing an online survey combined with six MOOC instructor interviews. The research findings indicated that instructors’ motivation to teach MOOCs primarily related to intrinsic motivation. Importantly, this study classified different motivational factors of MOOC instructors into seven categories. While the frustrations of MOOC instructors included time for creating MOOCs and a lack of interaction in MOOCs caused, in part, by heavy reliance on asynchronous communication, many MOOC instructors perceived that MOOC teaching positively influenced their professional development. Most MOOC instructors learned how to teach MOOCs informally and individually. Nevertheless, they made efforts to help other MOOC instructors. Finally, this study pointed to the strong need for professional development of MOOC instructors in developing MOOCs and their effective implementation

The effects of openness, altruism and instructional self-efficacy on work engagement of MOOC instructors

Many of the characteristics and nature of teaching massive open online courses (MOOCs) are different from face-to-face teaching, which can directly affect instructor work engagement and ultimately the success of MOOCs. As such, this study investigated the effects of openness, altruism, and instructional self-efficacy on MOOC instructors’ work engagement. A total of 209 MOOC instructors participated in an online survey, and their responses were analysed. The research findings indicated that openness to experience influenced MOOC instructors’ instructional self-efficacy and work engagement. Altruism did not directly influence work engagement of MOOC instructors; however, it indirectly affected work engagement through instructional self-efficacy. Instructional self-efficacy also affected MOOC instructors’ work engagement and fully mediated the relationship between altruism and work engagement. The variables examined in this study, openness, altruism, and instructional self-efficacy, were found to significantly influence work engagement of MOOC instructors. As the number of MOOCs and MOOC students increase, the importance of MOOC instructors and their work engagement will be crucial for the success of these courses. Thus, additional research is needed on the ways to enhance MOOC instructors’ work engagement

Alarm Calling in Plateau Pika (<i>Ochotona curzoniae</i>): Evidence from Field Observations and Simulated Predator and Playback Experiments

Acoustic communication plays a vital role in passing or sharing information between individuals. Identifying the biological meaning of vocal signals is crucial in understanding the survival strategies of animals. However, there are many challenges in identifying the true meaning of such signals. The plateau pika (Ochotona curzoniae) is a call-producing mammal endemic to the Qinghai–Tibet plateau (QTP) and considered a keystone species owing to its multiple benefits in alpine rangeland ecosystems. Previous studies have shown that plateau pikas emit alarm calls as part of their daily activities. However, only field observations have been used to identify these alarm calls of the plateau pika, with no attempts at using playback experiments. Here, we report the alarm calling of plateau pikas through field observations as well as simulated predator and playback experiments in the Eastern QTP from 2021 to 2022. We found that both female and male adults emitted alarm calls, the signals of which comprised only one syllable, with a duration of 0.1–0.3 s. There were no differences in the characteristics between the observed alarm calls and those made in response to the simulated predator. The duration of the alarm call response varied with altitude, with plateau pikas living at higher altitudes responding at shorter durations than those at lower altitudes

HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level

Link Prediction on Hyper-relational Knowledge Graphs (HKG) is a worthwhile endeavor. HKG consists of hyper-relational facts (H-Facts), composed of a main triple and several auxiliary attribute-value qualifiers, which can effectively represent factually comprehensive information. The internal structure of HKG can be represented as a hypergraph-based representation globally and a semantic sequence-based representation locally. However, existing research seldom simultaneously models the graphical and sequential structure of HKGs, limiting HKGs' representation. To overcome this limitation, we propose a novel Hierarchical Attention model for HKG Embedding (HAHE), including global-level and local-level attention. The global-level attention can model the graphical structure of HKG using hypergraph dual-attention layers, while the local-level attention can learn the sequential structure inside H-Facts via heterogeneous self-attention layers. Experiment results indicate that HAHE achieves state-of-the-art performance in link prediction tasks on HKG standard datasets. In addition, HAHE addresses the issue of HKG multi-position prediction for the first time, increasing the applicability of the HKG link prediction task. Our code is publicly available.Comment: Accepted by ACL 2023 main conferenc