LogicENN: A Neural Based Knowledge Graphs Embedding Model With Logical Rules.

Knowledge graph embedding models have gained significant attention in AI research. The aim of knowledge graph embedding is to embed the graphs into a vector space in which the structure of the graph is preserved. Recent works have shown that the inclusion of background knowledge, such as logical rules, can improve the performance of embeddings in downstream machine learning tasks. However, so far, most existing models do not allow the inclusion of rules. We address the challenge of including rules and present a new neural based embedding model (LogicENN). We prove that LogicENN can learn every ground truth of encoded rules in a knowledge graph. To the best of our knowledge, this has not been proved so far for the neural based family of embedding models. Moreover, we derive formulae for the inclusion of various rules, including (anti-)symmetric, inverse, irreflexive and transitive, implication, composition, equivalence and negation. Our formulation allows to avoid grounding for implication and equivalence relations. Our experiments show that LogicENN outperforms the existing models in link prediction.

UniSKGRep: A unified representation learning framework of social network and knowledge graph.

The human-oriented applications aim to exploit behaviors of people, which impose challenges on user modeling of integrating social network (SN) with knowledge graph (KG), and jointly analyzing two types of graph data. However, existing graph representation learning methods merely represent one of two graphs alone, and hence are unable to comprehensively consider features of both SN and KG with profiling the correlation between them, resulting in unsatisfied performance in downstream tasks. Considering the diverse gap of features and the difficulty of associating of the two graph data, we introduce a Unified Social Knowledge Graph Representation learning framework (UniSKGRep), with the goal to leverage the multi-view information inherent in the SN and KG for improving the downstream tasks of user modeling. To the best of our knowledge, we are the first to present a unified representation learning framework for SN and KG. Concretely, the SN and KG are organized as the Social Knowledge Graph (SKG), a unified representation of SN and KG. For the representation learning of SKG, first, two separate encoders in the Intra-graph model capture both the social-view and knowledge-view in two embedding spaces, respectively. Then the Inter-graph model is learned to associate the two separate spaces via bridging the semantics of overlapping node pairs. In addition, the overlapping node enhancement module is designed to effectively align two spaces with the consideration of a relatively small number of overlapping nodes. The two spaces are gradually unified by continuously iterating the joint training procedure. Extensive experiments on two real-world SKG datasets have proved the effectiveness of UniSKGRep in yielding general and substantial performance improvement compared with the strong baselines in various downstream tasks.

The Effect of Law Students in Entrepreneurial Psychology Under the Artificial Intelligence Technology.

With the increasingly serious employment situation in China, the government and schools encourage college students to start businesses to alleviate employment pressure. College student's successful entrepreneurship depends on national preferential policies, social support, and, most importantly, their healthy and solid psychological quality and entrepreneurial psychological quality. The purpose is to understand the entrepreneurial psychology of college students and study the entrepreneurial psychological effect. Firstly, the four aspects of entrepreneurial psychology are summarized, including entrepreneurial awareness, entrepreneurial volition, entrepreneurial ability, and entrepreneurial personality. Secondly, the research status of college students' entrepreneurial psychology is reviewed, and the existing problems are pointed out. Thirdly, the combined model of wavelet transform and Neural Network (NN) is proposed, and the feasibility of the proposed model is evaluated through the analysis of college students' entrepreneurial psychology. The wavelet NN is used in experimental design to predict college students' entrepreneurial psychology, and the predicted results are compared with the actual value. From the perspective of the prediction results of entrepreneurial psychology, the combination of wavelet algorithm and neural network is more accurate for entrepreneurial psychology prediction and evaluation results of law students. Overall, the difference between the predicted value and the actual value is within 0.3 points, which is relatively stable. According to the analysis of single-factor results, the scores of students of different majors in the four dimensions of entrepreneurial psychology are all higher than 3.5, but there is no significant difference among the four dimensions (P > 0.05), indicating that the major has no significant impact on entrepreneurial psychology; law students with different educational backgrounds have significant differences in entrepreneurial psychology (P < 0.05), among which students with a master's degree have the strongest entrepreneurial will, while doctoral students have the lowest entrepreneurial will; in terms of entrepreneurial psychological capital, men's self-efficacy is higher than women's, and the difference is significant (P < 0.05). The difference between males and females in the scores of entrepreneurial psychological factors' four aspects is not very obvious. In terms of entrepreneurial psychological capital, males' self-efficacy is significantly higher than females' (P < 0.05). Artificial Intelligence (AI) technology has great application prospects in the prediction and evaluation of college students' entrepreneurial psychology, and college students' entrepreneurial psychology is highly correlated with gender and education.

Ultrafast and efficient removal of Pb(II) from acidic aqueous solution using a novel polyvinyl alcohol superabsorbent.

The direct removal of heavy metal ions from acidic wastewater is a hard problem. In this study, a novel superabsorbent, polyvinyl alcohol phosphate ester (PVAP), was designed and prepared to remove Pb(II) from acidic wastewater (pH = 3). The PVAP can absorb water and swell to reach equilibrium within 30 s, which provides the conditions for ultrafast kinetic adsorption. For 100 mg/L Pb(II) solution, the adsorption reaches equilibrium within 5 min, and the removal ratio is more than 99.9% over a wide pH range of 3-6. Adsorption kinetics and isotherm data are consistent with pseudo-second-order and Langmuir model, respectively. The calculated maximum adsorption capacity for Pb(II) is 558.66 mg/g. Thermodynamic results show that the adsorption is spontaneous and exothermic process. The removal ratio for Pb(II) of PVAP still maintains above 99% after ten recycles. The PVAP can also simultaneously remove more than 97% of other heavy metal ions (Cu(II), Cd(II), Zn(II), Co(II), and Ni(II)) from an acidic solution. Moreover, the PVAP can efficiently purify simulated acid mine heavy metal wastewater, and the results meet EPA drinking water standards. The studies of X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared (FT-IR) spectroscopy prove that the adsorption mechanism involves surface complexation. This new superabsorbent is a promising candidate for acidic heavy metal sewage disposal.

The Impact of Organizational Psychology Factors for the Cross-Border Legal Service Entrepreneurs.

Trade friction has always been a prominent feature in the current economic development of the world. Its impacts on multinational enterprises are self-evident, but its psychological effects on the multinational entrepreneurs are still unclear. In order to understand the impacts of trade friction on psychological effects of multinational legal service entrepreneurs, 305 multinational entrepreneurs were selected in this study for questionnaire survey, and Spearman's correlation and regression models were used to analyze the correlation among economic pressure, the thought of recession, self-efficacy, and social support. The structural equation model was used to analyze the influence path of economic pressure and social support on the thought of entrepreneurial recession, as well as the influence path of multinational entrepreneurship orientation and value-chain potential on the international performance. The results show that economic pressure is significantly and positively correlated with the thought of recession and self-efficacy extremely and significantly and negatively correlated with objective support and support utilization extremely; social support will reverse the thought of entrepreneurial recession caused by the economic pressure; the indirect impact path coefficient of social support utilization in economic pressure and entrepreneurial recession is - 0.281; the indirect impact path coefficient of value-chain potential in multinational entrepreneurial motivation and international performance is - 0.424. It shows that trade friction will indirectly trigger the thought of entrepreneurial recession of entrepreneurs by reducing their economic incomes. Besides, the social support utilization can significantly regulate the relationship between the economic pressure and the thought of entrepreneurial recession. Therefore, the value-chain potential plays an intermediary role in multinational entrepreneurial motivation and international performance.