Editing Language Model-based Knowledge Graph Embeddings

Recently decades have witnessed the empirical success of framing Knowledge Graph (KG) embeddings via language models. However, language model-based KG embeddings are usually deployed as static artifacts, which are challenging to modify without re-training after deployment. To address this issue, we propose a new task of editing language model-based KG embeddings in this paper. The proposed task aims to enable data-efficient and fast updates to KG embeddings without damaging the performance of the rest. We build four new datasets: E-FB15k237, A-FB15k237, E-WN18RR, and A-WN18RR, and evaluate several knowledge editing baselines demonstrating the limited ability of previous models to handle the proposed challenging task. We further propose a simple yet strong baseline dubbed KGEditor, which utilizes additional parametric layers of the hyper network to edit/add facts. Comprehensive experimental results demonstrate that KGEditor can perform better when updating specific facts while not affecting the rest with low training resources. Code and datasets will be available in https://github.com/zjunlp/PromptKG/tree/main/deltaKG.Comment: Work in progress and the project website is https://zjunlp.github.io/project/KGE_Editing

Construction and Applications of Billion-Scale Pre-trained Multimodal Business Knowledge Graph

Business Knowledge Graphs (KGs) are important to many enterprises today, providing factual knowledge and structured data that steer many products and make them more intelligent. Despite their promising benefits, building business KG necessitates solving prohibitive issues of deficient structure and multiple modalities. In this paper, we advance the understanding of the practical challenges related to building KG in non-trivial real-world systems. We introduce the process of building an open business knowledge graph (OpenBG) derived from a well-known enterprise, Alibaba Group. Specifically, we define a core ontology to cover various abstract products and consumption demands, with fine-grained taxonomy and multimodal facts in deployed applications. OpenBG is an open business KG of unprecedented scale: 2.6 billion triples with more than 88 million entities covering over 1 million core classes/concepts and 2,681 types of relations. We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks. We also run up an online competition based on OpenBG benchmarks, and has attracted thousands of teams. We further pre-train OpenBG and apply it to many KG- enhanced downstream tasks in business scenarios, demonstrating the effectiveness of billion-scale multimodal knowledge for e-commerce. All the resources with codes have been released at \url{https://github.com/OpenBGBenchmark/OpenBG}.Comment: OpenBG. Work in Progres

Characterization on Crack Initiation and Early Propagation Region of Nickel-Based Alloys in Very High Cycle Fatigue

As nickel-based alloys are more and more widely used in engineering fields for bearing cyclic loadings, it is necessary to study their very-high-cycle fatigue (VHCF) properties. In this paper, the fatigue properties of nickel-based alloy 625 were investigated using an ultrasonic fatigue test apparatus. The fracture microscopy shows that around the crack initiation site there are two characteristic zones, a rough area (RA) and a fine granular area (FGA). Inclusions caused the interior fatigue crack initiation, and the coalescence of neighboring micro cracks was strongly influenced by the local microstructure, resulting in the RA morphology. Subsequently, the contact and compressing of the crack surfaces contributed to the formation of the FGA. Finally, the stress intensity factors of the RA and FGA were quantitatively evaluated for further discussion of the crack initiation and propagation processes

Experimental Investigation of the Dynamic Tensile Properties of Naturally Saturated Rocks Using the Coupled Static–Dynamic Flattened Brazilian Disc Method

In a naturally saturated state, rocks are likely to be in a stress field simultaneously containing static and dynamic loads. Since rocks are more vulnerable to tensile loads, it is significant to characterize the tensile properties of naturally saturated rocks under coupled static–dynamic loads. In this study, dynamic flattened Brazilian disc (FBD) tensile tests were conducted on naturally saturated sandstone under static pre-tension using a modified split-Hopkinson pressure bar (SHPB) device. Combining high-speed photographs with digital image correlation (DIC) technology, we can observe the variation of strain applied to specimens’ surfaces, including the central crack initiation. The experimental results indicate that the dynamic tensile strength of naturally saturated specimens increases with an increase in loading rate, but with the pre-tension increases, the dynamic strength at a certain loading rate decreases accordingly. Moreover, the dynamic strength of naturally saturated sandstone is found to be lower than that of natural sandstone. The fracture behavior of naturally saturated and natural specimens is similar, and both exhibit obvious tensile cracks. The comprehensive micromechanism of water effects concerning the dynamic tensile behavior of rocks with static preload can be explained by the weakening effects of water on mechanical properties, the water wedging effect, and the Stefan effect

Research on Short-Time Wind Speed Prediction in Mountainous Areas Based on Improved ARIMA Model

In rugged mountain areas, the lateral aerodynamic force and aerodynamic lift caused by strong winds are the main reasons for the lateral overturning of trains and the destruction of buildings and structures along the railroad line. Therefore, it is important to build a strong wind alarm system along the railroad line, and a reasonable and accurate short-time forecast of a strong wind is the basis of it. In this research, two methods of constructive function and time-series decomposition are proposed to pre-process the input wind speed for periodic strong winds in mountainous areas. Then, the improved Auto-Regressive Integrated Moving Average model time-series model was established through the steps of a white noise test, data stationarity test, model recognition, and order determination. Finally, the effectiveness of the improved wind speed prediction was examined. The results of the research showed that rational choice of processing functions has a large impact on wind speed prediction results. The prediction accuracy of the improved ARIMA model proposed in this paper is better than the results of the traditional Seasonal Auto-Regressive Integrated Moving Average model, and it can quickly and accurately realize the short-time wind speed prediction along the railroad line in rugged mountains. In addition, the improved ARIMA model has verified its universality in different mountainous places

Research on Short-Time Wind Speed Prediction in Mountainous Areas Based on Improved ARIMA Model

In rugged mountain areas, the lateral aerodynamic force and aerodynamic lift caused by strong winds are the main reasons for the lateral overturning of trains and the destruction of buildings and structures along the railroad line. Therefore, it is important to build a strong wind alarm system along the railroad line, and a reasonable and accurate short-time forecast of a strong wind is the basis of it. In this research, two methods of constructive function and time-series decomposition are proposed to pre-process the input wind speed for periodic strong winds in mountainous areas. Then, the improved Auto-Regressive Integrated Moving Average model time-series model was established through the steps of a white noise test, data stationarity test, model recognition, and order determination. Finally, the effectiveness of the improved wind speed prediction was examined. The results of the research showed that rational choice of processing functions has a large impact on wind speed prediction results. The prediction accuracy of the improved ARIMA model proposed in this paper is better than the results of the traditional Seasonal Auto-Regressive Integrated Moving Average model, and it can quickly and accurately realize the short-time wind speed prediction along the railroad line in rugged mountains. In addition, the improved ARIMA model has verified its universality in different mountainous places

Analysis of Landscape Change and Its Driving Mechanism in Chagan Lake National Nature Reserve

Lake ecosystems play an important role in regional ecological security and the sustainable development of the economy and society. In order to study the evolution of landscape patterns and the main driving forces in the Chagan Lake Nature Reserve in recent years, we used landscape type data from 2005, 2010, 2015, and 2019 to study the characteristics of the regional landscape’s structural changes. At the same time, the spatial heterogeneity of the driving factors of landscape change was analyzed using the spatial analysis method, and the driving mechanism of landscape change was quantitatively analyzed. The results showed that: (1) from 2005 to 2019, the area of cultivated land, marshland, and water bodies increased, while the area of grassland and the area of bare land decreased. (2) The dominant patch types in the study area formed good connectivity, and the degree of landscape fragmentation increased. (3) In the past 15 years, there has been spatial heterogeneity in the regression coefficients of different driving factors of landscape change: the area with a greater influence of the elevation factor was in the south; the regression coefficient of precipitation showed the spatial distribution characteristics of highs in the west and lows in the east; the gross domestic product had a greater impact on the east and the south; the spatial variation of grain yield was mainly reflected in the southeast and northwest regions; the fishery yield gradually changed from high in the southeast and low in the northwest to the distribution characteristic of decreasing from the east to the southwest; the lake fluorine content showed a distribution pattern that gradually changed from high in the southeast and low in the northwest to high in the middle and low in the north and south; the distribution pattern of the distance to oil production changed from north to southeast to south to north; the distance to the road changed from high in the east and low in the west to the opposite spatial distribution pattern. (4) The interaction of precipitation and lake fluoride content with other factors showed a strong driving effect, which had a significant impact on the landscape change of Chagan Lake Nature Reserve. Since the study area is located in a typical fluorine-rich geochemical environment, human activities, such as the expansion of irrigation areas around Chagan Lake and groundwater exploitation, have accelerated the dissolution of fluorine-containing minerals, promoted the enrichment process of fluorine in Chagan Lake, and enhanced the explanatory power of lake fluorine content in terms of landscape changes. At the same time, the increase in precipitation during the study period is beneficial to the growth of vegetation and the storage of water in lakes, which promotes changes in landscape types such as grasslands and areas of water

Is multi-hop reasoning really explainable? Towards benchmarking reasoning interpretability

Multi-hop reasoning has been widely studied in recent years to obtain more interpretable link prediction. However, we find in experiments that many paths given by these models are actually unreasonable, while little works have been done on interpretability evaluation for them. In this paper, we propose a unified framework to quantitatively evaluate the interpretability of multi-hop reasoning models so as to advance their development. In specific, we define three metrics including path recall, local interpretability, and global interpretability for evaluation, and design an approximate strategy to calculate them using the interpretability scores of rules. Furthermore, we manually annotate all possible rules and establish a Benchmark to detect the Interpretability of Multi-hop Reasoning (BIMR). In experiments, we run nine baselines on our benchmark. The experimental results show that the interpretability of current multi-hop reasoning models is less satisfactory and is still far from the upper bound given by our benchmark. Moreover, the rule-based models outperform the multi-hop reasoning models in terms of performance and interpretability, which points to a direction for future research, i.e., we should investigate how to better incorporate rule information into the multi-hop reasoning model. Our codes and datasets can be obtained from https://github.com/THU-KEG/BIMR