dataesr/scientific-paragraphs-categorization huggingface dataset:
Besides, the number of entities peaks at degree of 4, and drops consistently when the entity degree increases. The (c) series of sub-figures corresponds to SRPRS. Evidently, the degree distribution of entities in this dataset is more realistic, where the entities with lower degrees account for higher percentages. This can be attributed to its carefully designed sampling strategy. Note that the (d) series of sub-figures corresponds to our constructed dataset, which will be introduced in Section 5. Evaluation metrics. Following existing EA solutions, we utilize Hits@k (k=1, 10) and mean reciprocal rank (MRR) as the evaluation metrics. At the prediction stage, for each source entity, the target entities are ranked according to their distance scores with the source entity in an ascending order.
Implementation details. The experiments are conducted on a personal computer with an Intel Core i7-4790 CPU, an NVIDIA GeForce GTX TITAN X GPU and 128 GB memory. The programs are all implemented in Python. We directly use the source codes provided by the authors for reproduction, and the results are obtained by executing the models using the set of parameters reported in their original papers 7. We use the same set of parameters on the datasets that are not included in the original papers. On the DBP15K dataset, all of the evaluated methods provide results in their original papers, except for MTransE and ITransE. We compare our implemented results with their reported results. If the difference falls out of a reasonable range, i. e. , ±5% of the original results, we mark the methods with *.
Note that theoretically there should not be a huge difference, since we use the same source codes and the same parameters for implementation. On SRPRS, only RSNs reports the results in its original paper  [24]. We run all methods on SRPRS and provide the results in Table  4. On DWY100K, we run all approaches, and compare the performance of BootEA, MuGNN, NAEA, KECG and TransEdge with the results provided in their original papers. Methods with notable differences are marked with *. 7. In the interest of space, we put the detailed parameter settings in the appendix. On each dataset, the best results within each group are denoted in bold. We mark the best Hits@1 performance among all approaches with , as this metric can best reflect the effectiveness of EA methods.
Name-based heuristics. On KG pairs with closely-related languages, Lev attains promising results, whereas it fails to work on distantly-related language pairs, i. e. , DBP15K ZH-EN and DBP15K JA-EN. As for Embed, it achieves consistent performance on all KG pairs. Intra-category comparison. CEA achieves the best Hits@1 performance on all datasets. As for other metrics, TransEdge, RDGCN and HGCN attain the leading results. This verifies the effectiveness of using extra information, i. e. , the bootstrapping strategy and textual information. The performance of the name-based heuristics (i. e. , Embed) is very competitive, exceeding most of the methods that do not use entity name information in terms of Hits@1. This demonstrates that typical ER solutions can still work on the task of EA. Nevertheless, Embed is still inferior to most EA methods that incorporate the entity name information, i.
e. , RDGCN, HGCN and CEA. It can also be observed that, methods from the first two groups, e. g. , TransEdge, attain consistent results across all three KG pairs, while the solutions utilizing the entity name information, e. g. , HGCN, achieve much better results on the KG pairs with closely-related languages (FR-EN) than those with distantly-related languages (ZH-EN). This reveals that the language barriers can hamper the use of textual information and in turn hurt the overall effectiveness.
Group III. Compared with GCN and JAPE-Stru, incorporating the attributes leads to better results for GCN-Align, while it does not contribute to the performance of JAPE. This is because the number of attributes is relatively smaller in this dataset. In comparison, using entity names enhances the results to a much higher level. Note that CEA attains ground-truth performance on SRPRS DBP-WD and SRPRS DBP-YG. Name-based heuristics. Lev and Embed achieve ground-truth performance on all mono-lingual EA datasets, since the names in the entity identifiers of DBpedia, Wikidata and YAGO are identical. Lev also achieves promising results on cross-lingual KG pairs with closely-related language pairs. Intra-category comparison. Different from DBP15K, methods that incorporate entity names (Group III) dominate on SRPRS.
Results and Analyses on DWY100K The results on the large-scale mono-lingual dataset, DWY100K, are reported in Table  5. We fail to obtain the results of RDGCN and NAEA under our experimental environment, as they require extremely large amount of memory space. Methods in the first group achieve more promising results on this dataset, which can be ascribed to the relatively richer KG structure (shown in Figure  3 ). Among them, MuGNN and KECG attain over 60% on DWY100K DBP-WD and 70% on DWY100K DBP-YG in terms of Hits@1, as the rich structure facilitates the process of KG completion, which in turn enhances the overall EA performance. With the aid of the iterative training strategy, approaches in the second group further improve the results, whereas the results of BootEA and TransEdge are slightly lower than their reported values.
Efficiency Analysis For the comprehensiveness of the evaluation, we report the averaged running time on each dataset in Table  6  to compare the efficiency of state-of-the-art solutions, which can also reflect their scalability. We are aware that different parameter settings, e. g. , the learning rate and the number of epochs, might influence the eventual time cost. However, here we merely aim to provide a general picture of the efficiency of these methods by adopting the parameters reported in their original papers. Again, we fail to obtain the results of RDGCN and NAEA on DWY100K under our experimental environment, as they require extremely large amount of memory space. On DBP15K and SRPRS, GCN is the most efficient method with consistent alignment performance, which is closely followed by JAPE-Stru and ITransE.
For the other methods, most of them have the same magnitude of time costs (1,000-10,000s), except for NAEA and GM-Align, which require extremely higher running time. On the much larger dataset DWY100K, the time costs of all solutions climb dramatically, due to the larger number of parameters and higher computational costs. Among others, MuGNN, KECG, HMAN cannot work by using the GPU because of the memory limitation, and we report the time cost by using the CPU as suggested by the authors of these approaches. It is noted that merely three methods can finish the alignment process within 10,000s, and the time costs for most approaches fall between 10,000s and 100,000s. GM-Align even requires 5 days to generate the results. This unveils that state-of-the-art EA methods still have low efficiency when dealing with data at very large scale.
e. , the number of correctly aligned entity pairs divided by the number of source entities for which an approach returns a target entity) and recall (i. e. , the number of source entities for which an approach returns a target entity divided by the total number of source entities). As depicted in Figure  4 , the overall performance of PARIS and AML are slightly inferior to CEA. However, although CEA has more robust performance, it relies on the training data (seed entity pairs), which might not exist in the real-world KGs. In contrast, unsupervised systems work without requiring any training data, and can still output very promising results. Besides, by comparing the results of PARIS and AML, it shows that the ontology information indeed can improve the alignment results. 9. AML requires ontology information, which does not exist in current EA datasets.
With regard to the extra information module, we denote the use of the bootstrapping strategy as B by implementing the iterative method in ITransE. The use of multi-type information is represented as Mul, and we adopt the semantic and string-level features of entity names as in CEA. The Hits@1 results of 24 combinations are shown in Table  7   10. It can be observed that, adding the bootstrapping strategy and/or textual information indeed enhances the overall performance. Regarding the embedding model, the GCN+Mgn model appears to have more robust and superior performance than TransE+Cps. Besides, the distance measures also have influence on the results. Compared with Manh and Euc, Cos leads to better performance on TransE-based models, while it brings worse results on GCN-based models. Nevertheless, after incorporating entity name embeddings, using Cos leads to consistently better performance.
Influence of datasets. As shown in Figure  5 , the performance of EA solutions varies greatly on different datasets. Generally, EA methods achieve relatively better results on dense datasets, i. e. , on DBP15K and DWY100K. Besides, the results on mono-lingual KGs are better than those on cross-lingual ones (DWY100K vs. DBP15K). Particularly, on mono-lingual datasets, the most performant method CEA, as well as the name-based heuristics Lev and Embed, attain 100% accuracy. This is because these datasets are extracted from DBpedia, Wikidata and YAGO, and the equivalent entities in these KGs possess identical names in the entity identifiers. However, these datasets fail to mirror the real-life challenge of ambiguous entity names. To fill in this gap, we construct a new mono-lingual benchmark, which is to be detailed in Section 5.
Guidelines for practitioners. There are many factors that might influence the choice of EA models. We select four most common factors and give the following suggestions: Nevertheless, it suffers from the error propagation problem, which might introduce wrongly-matched entity pairs and amplify their negative effect on the alignment in the following rounds. Also, it can be time-consuming. Consequently, when deciding whether to use the bootstrapping strategy, one could estimate the difficulty of the datasets. If the datasets are relatively easy, e. g. , with rich literal information and dense KG structures, exploiting the bootstrapping strategy might be a better choice. Otherwise, one should be careful when using such a strategy. Suggestions for future research. We also discuss some open problems that are worthy of exploration in the future: • EA for long-tail entities.
NEW DATASET AND FURTHER EXPERIMENTS As highlighted in Section 4, in existing mono-lingual datasets, the names in the identifiers of equivalent entities from different KGs are identical. This means that a simple comparison of these names can achieve reasonably accurate results (100% precision on SRPRS DBP-YG ). In real-life KGs, however, entity identifiers are often not human-readable. For example, Freebase identifies Paris (the capital of France) by /m/05qtj. Wikidata has a similar policy. These identifiers are then linked to one or several human-readable names. For example, /m/05qtj is linked to "Paris", "The City of Light", etc. As it so happens, just retrieving these names from the KGs, and matching entities that share a name, still achieves a precision of 100% on datasets such as DWY100K DBP-WD and SRPRS DBP-WD.
In real-life KGs, however, this method will not work. The reason is that different entities (with different identifiers) can have the same name. For example, both the Freebase entity /m/05qtj (the capital of France) and /m/0h0_x (the king of Troy) share the name "Paris" -as do 20 cities in the U. S. that are called "Paris". This obviously poses a problem for EA, as there is no guarantee that an entity with the name "Paris" in the source KG is the same as an entity with the name "Paris" in the target KG -simply because one might be the city in France and the other one the king of Troy. This is an important complication in real-life KGs: For example, in YAGO 3, 34% of entities have a name that is shared by more than one entity. This problem is insufficiently mirrored in the mono-lingual datasets that are commonly used for EA.
There is a second problem with the EA datasets: They contain, for each entity in the source KG, exactly one corresponding entity in the target KG. Thus, an EA approach can just map every entity in the source KG to the most similar entity in the target KG. This, however, is an unrealistic scenario. In real life, KGs contain entities that other KGs do not contain. For example, when one tries to align YAGO 3 and DBpedia, one will encounter entities that appear in YAGO 3, and not in DBpedia, and vice versa. The problem is even more pronounced for KGs that feed from different sources, such as, say, YAGO 4 and IMDB. Only 1% of entities in YAGO 4 are movies or entities related to movies (such as actors). The other 99% of entities in YAGO 4 (such as universities, smartphone brands, etc. ) necessarily have no match in IMDB.
This problem is not considered at all in current EA datasets. We thus observe that the existing datasets for EA are an oversimplification of the real-life problem, disregarding the fundamental issues of ambiguity and unmatchable entities. As a remedy, we propose a new dataset that mirrors these difficulties. We expect this dataset to lead to better EA models that can deal with more challenging problem instances, and to offer a better direction for the research community. This section introduces the construction of the new dataset and our experimental results on it.
Dataset Construction To reflect the difficulty of using entity names, we adopt Freebase  [3]  as the target KG, since it represents entities with incomprehensible identifiers (i. e. , Freebase mids), and different entities might share the same name. DBpedia is used as the source KG, as it contains external links to Freebase, which can be directly utilized as gold standards. The specific construction process is elaborated as follows: Determining the source entity set. We take advantage of the disambiguation records in DBpedia and collect the entities that share the same disambiguation term to constitute the entity set of the source KG. For instance, regarding the ambiguous term Apple, the disambiguation records involve entities such as Apple Inc. and Apple(fruit), and these entities are included in the source entity set.
The entity sets are adjusted correspondingly, in which the entities that participate in triples but not in links are also included. Eventually, there are 29,861 entities in the source KG, among which 4,319 are unmatchable, and 25,542 matchable entities in the target KG. Following existing datasets, 30% of the links and unmatchable entities are used as training set. Other statistics of the dataset are shown in Table  2.
Experiment Results on DBP-FB Following the current evaluation paradigm, we first discuss EA performance without the unmatchable entities. Table  5  reveals that, the overall performance of the methods in the first two groups is worse than that on SRPRS, which can be attributed to the higher structural heterogeneity of DBP-FB. This can also be observed from sub-figures (d) in Figure  3 -unlike KG pairs in (a), (b) or (c), the entity distributions in these KGs are very different, which poses difficulty to the utilization of structural information. Methods harnessing entity names still yield the best results, whereas the performance all drops compared with the results on previous mono-lingual datasets. Additionally, on DBP-FB, Embed and Lev merely achieve the Hits@1 values at 58. 3% and 57. 8%, respectively, while these figures for SRPRS DBP-YG , SRPRS DBP-WD , DWY100K DBP-YG and DWY100K DBP-WD are all 100%.
Unmatchable Entities DBP-FB also includes the unmatchable entities, which is another a real-life challenge for EA. We take into consideration these unmatchable entities and report the performance of CombEA (from Section 4. 8) on DBP-FB. Following Section 4. 7, we adopt the precision, recall and F1 score as the evaluation metrics, except that, we define the recall as the number of matchable source entities for which an approach returns a target entity, divided by the total number of matchable source entities. Table  8  reveals that, CombEA has very high recall, but relatively low precision, as it generates a target entity for each source entity (including the unmatchable ones). This also reflects how current EA solutions perform when there are source entities that cannot be aligned. However, this issue is neglected by existing EA datasets.
CONCLUSION EA is a pivotal step for integrating KGs to increase knowledge coverage and quality. Although many solutions have been proposed, there has not been a comprehensive assessment and detailed analysis of their performance. To fill in the gap, this article reports an empirical evaluation of state-of-the-art EA approaches in terms of both effectiveness and efficiency on representative datasets, analyzes their performance in depth, and provides evidence-based discussions. Moreover, we establish a new dataset that better reflects the real-life challenges for future research.
The use of air quality modelling tools at high resolution is now common but remains a challenge, because buildings create local modifications in air flows and modify surface energy budgets and roughness, leading to very complex processes which make air quality simulation particularly difficult in the near-ground layers  [10] [11] [12] [13]. Chemical transport models usually require numerical weather prediction (NWP) models, which provide meteorological input data  [14, 15]. High-resolution urban canopy parameters databases are becoming more and more available for cities  [16, 17]. NWP models with different physics parameterizations have been widely used in recent decades to simulate air quality over urban regions, especially the widely used community mesoscale Advanced Research Weather Research and Forecasting (ARW-WRF) model.
The ground layer of the CHIMERE domains is at 999. 5 hPa from about 4 m up to over 5000 m, with 15 levels below 1000 m. Such a low ground level could effectively reproduce the transport and diffusion of pollutants in a real urban environment. Two types of observation data are used in the present study: (i) three meteorological observation stations operated by METEO FRANCE and SIRTA were used to evaluate the simulated surface meteorological variables from the model; and (ii) the hourly surface concentrations of criteria pollutants (NO2, PM2. 5, and PM10) issued from AIRPARIF, the official Paris air quality network. The location of the three meteorological observation stations (Montsouris, Roissy, Sirta) and the four air quality monitoring stations can be found in Figure  2 ; they are urban or sub-urban background stations.
Urban Parameters and Nudging Tests The urban canopy model in the WRF simulation affects the modeling of several important meteorological variables for air quality issues (e. g. , 10 m wind speed W10, 2 m surface temperature T2, and PBL height). It changes the vertical distribution of atmospheric pollutants by modifying the boundary layer height and mixing. Geometric and thermal parameters, including anthropogenic Two types of observation data are used in the present study: (i) three meteorological observation stations operated by METEO FRANCE and SIRTA were used to evaluate the simulated surface meteorological variables from the model; and (ii) the hourly surface concentrations of criteria pollutants (NO 2 , PM 2. 5 , and PM 10 ) issued from AIRPARIF, the official Paris air quality network.
The performance benefits in Paris from the assimilation of meteorological data at Roissy airport in the global meteorological fields. Thus, URBAN-PAM1 and spectral nudging are used for all our sensitivity tests, which are discussed in the following sections. Atmosphere 2020, 11, x FOR PEER REVIEW 6 of 25 heat, thermal conductivities, and heat capacity, have a significant effect on the transfer of momentum between the atmosphere and urban surface  [52]. However, the representativeness of these parameters aggregated over a CTM grid cell remains highly uncertain, especially with a limited number of urban land-use types. The urban parameters (Table  S1 ) used here are based on a previous study in Paris region  [53, 54]  and default urban parameterization (URBPARM). The TBL tables in the WRF code are defined as URBAN-PAM1 and URBAN-PAM2, respectively.
The RRTMG shortwave and longwave radiation scheme, MO similarity surface layer scheme, Noah land surface model, BEP urban canopy model, and MYJ boundary layer scheme are used for all simulations in this section. Figure  3  presents the time series of hourly-averaged T2 and W10 at Sirta station. The results show that simply modifying the values of the parameters has a strong impact on the outputs. The T2 presents a more positive correlation (0. 86 than 0. 81) in URBAN-PAM1 than in URBAN-PAM2, while the W10 has a more negative bias (-1. 51m than -1. 42 m s -1 ) in URBAN-PAM1 than in URBAN-PAM2. A weak spectral nudging above the boundary layer for all the domains is also tested in this part. Figure  4  indicates that the spectral nudging technique is effective in improving the model skills. The performance benefits in Paris from the assimilation of meteorological data at Roissy airport in the global meteorological fields.
All the other physics options are the same. For the observed PBL height, we have the complete dataset for Roissy station and the last four days data for Sirta station. Figure  14  displays a comparison between the observed Lidar data and the 15 min averaged modeled output of PBL height. Generally, all the PBL schemes systematically underestimate PBL height for the whole time series, especially during the PE and the beginning of RD (from 27th November 00:00 UTC). This explains the strong overestimations of pollutants during the PE in Section 3. 2 and is consistent with the positive biases of pollutant concentrations at the beginning of the RD in Figure  10. If we consider the simulation of PBL during the PE, the average observed PBL height was around 110 m and is only relatively well reproduced by the Boulac-BEP, scheme even though it still underestimates the PBL height by around 40 m on average.
Machine learning and weighted gene co-expression network analysis (WGCNA) have been widely used due to its well-known accuracy in the biological field. However, due to the nature of a gene's multiple functions, it is challenging to locate the exact genes involved in complex diseases such as asthma. In this study, we combined machine learning and WGCNA in order to analyze the gene expression data of asthma for better understanding of associated pathogenesis. Specifically, the role of machine learning is assigned to screen out the key genes in the asthma development, while the role of WGCNA is to set up gene co-expression network. Our results indicated that hormone secretion regulation, airway remodeling, and negative immune regulation, were all regulated by critical gene modules associated with pathogenesis of asthma progression.
, 2015; Corren et al. , 2011; Hanania et al. , 2015). These studies suggested that it is worthwhile to research the key genes and the pathogenic mechanisms of asthma. As such, one tool that can help researchers with the analysis of the relationships between key genes and the pathogenic mechanism is weight gene co-expression network analysis (WGCNA)  (Li et al. , 2016). The WGCNA method has been widely used in recent years  (Abu-Jamous & Kelly, 2018; Liu et al. , 2017; Mack et al. , 2018; Radulescu et al. , 2018). Instead of linking thousands of genes to the disease, this technology focuses on relationship between gene modules and disease traits. Through WGCNA, hidden biological models of the disease can be discovered  (Giulietti et al. , 2018; Vella et al. , 2017). Machine learning has shown great promise for mining linear or non-linear relationships in high-dimensional data through supervised  (Ahuja et al.