dataesr/scientific-paragraphs-categorization huggingface dataset:
• Two possible origins: ''calculated'' (generated from other events) or ''original'' (obtained directly using sensors). • A unique source called ''emitter. '' An emitter is associated with the hierarchical structure of every mobile system. All these parameters defining a SurfEvent can be modeled using an Entity-Relationship (ER) Unified Modeling Language (UML) diagram  [28]. Figure  7  describes such a model. The generic model of a SurfEvent, which takes into account the different types, status, and different locations (emitter) of SurfEvents and their related contexts, is provided in Fig. 8. In our work, the generic model of a Sur-fEvent is considered as a way of formalizing holon knowledge. This approach also enables the monitoring processes to be independent of the target systems and enables the development of applications that are optimized in terms of memory usage.
Deployment process and methodological aspects The EMH 2 architecture is designed to facilitate asynchronous and progressive deployments. We, therefore, adopted the edge computing paradigm and equipped the EMH 2 architecture with different edge computing nodes. The principles are as follows: • For mobile systems Iterative deployment of the different holonic levels for each layer (data acquisition, then data manipulation, then state detection, etc. ) can be achieved progressively and specifically depending on the mobile systems chosen and the equipment to be monitored. In addition, within a single holonic level, the progressive implementation and localization of hardware can be defined for every holon according to the constraints of embedded system calculators (available memory space, computing power, communication bandwidth, etc.
For mobile system 2, three holonic levels are already implemented for system 1, while only one (data acquisition) is implemented for system 2. Increasing the security of exchanges between mobile systems, the holons, and the wayside MC to fight cybercrime  [32]  is a major issue during deployment. Therefore, it is assumed that to deploy any EMH 2 , each mobile system must have a certificate signed by the wayside MC to allow it to communicate with other systems. The standard public key infrastructure (PKI)  [33]  can be used, for example. This ensures basic data authenticity, confidentiality, integrity, and availability. The PKI was chosen with respect to symmetric and asymmetric cryptography  [34]  because it associates the physical identity of a mobile system such as its identity, geographical location, or operator with a digital fingerprint consisting of a key pair.
• KPI#3 Time needed by the given EMADM to update data regarding the health status and monitoring of a Current exact values for these KPI are confidential but orders of magnitude can be provided and are indicated in Table  1. These indicators concern our industrial partner's current event management organization to be improved using the proposed EMH 2. The industrial partner has set the KPI objectives as described in Table  2. The application has been developed in C# using the Microsoft Asp. Net Core platform integrating different solutions such as SQL Relational, No SQL, and time series database (TSDB). This application uses microservices to implement data acquisition, data manipulation, state detection, and health assessment holonic levels. The whole application is compliant with modified standard ISO 13374.
The microservice approach aims to design a single solution as a series of small services where each service is a module that supports a specific need as well as its own execution and communication process. A microservice is easy to define and implement thanks to the use of lightweight mechanisms such as representational state transfer (REST), message queue (MQ), and WebSocket  [35]. A microservice approach then offers huge advantages for real-time monitoring, flexibility, modularity, automated deployment, scalability, and performance. This allows the  designer to overcome the limitations of other approaches such as service orientation  [36, 37].
The objectives are to provide the maintenance officer with the ability to communicate directly with the systems and subsystems of a mobile system, to investigate, and to execute commands in order to optimize maintenance operations and handle the maximum number of tasks possible and, therefore, be as reactive as possible when interacting with the EMH 2 architecture. Specific tools combining troubleshooting help tools and user guides using augmented reality and voice recognition to limit the need to access remote data and knowledge, as well as to facilitate maintenance activities, for example, must be designed for this purpose. Two limitations of our contribution have been identified from these experiments: • First, during the experiment, we encountered a lack of efficiency of the adaptation process that leads to new accurate knowledge regarding a newly integrated piece of equipment and the ability of the event management architecture to integrate this new knowledge.
Acknowledgements The work presented in this paper was led within the context of a research project whose partners were Bombardier Transport, the  Polytechnic University of Hauts-de-France (UPHF)  and the  French National Center for Scientific Research (CNRS). It was also led with the financial support of the  Chadian National Centre for Research (CNRD). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
• We then (Section IV) extend the problem formulation to encourage coverage of a larger set of nodes. We show that the resulting problem is NP-hard even for the case of static graph. However, we show on static graphs a simple greedy algorithm leads to approximate solution due to submodularity; then we extend this greedy approach for the case of temporal networks. • Experiments on synthetic and real-world datasets (Section V), and a case study on Twitter data (Section VI) confirm that our methods are efficient and produce meaningful and high-quality results.
V. EXPERIMENTS We evaluate the performance of the proposed algorithms on synthetic graphs and real-world social networks. The datasets are described below. Unless specified, we post-process the output of all algorithms and report the optimal densest subgraphs in the output intervals. Our datasets and implementations are publicly available. 1. Synthetic data. We generate a temporal network with k planted communities and a background network. All graphs are Erdős-Rényi. The communities G have the same density, disjoint set of nodes, and are planted in non-overlapping intervals. The background network G includes nodes from all planted communities G. The edges of G are generated uniformly on the timeline. In the typical setup the length of the whole time interval T is |T | = 1000 time units, while the edges of each G are generated in intervals of length |T | = 100 time units.
The densities of the communities and the background network vary. The number of nodes in G is set to 100. We test the ability of our algorithms to discover planted communities in two settings. In the first setting (dataset family Synthetic1 ) we vary the average degree of the background network from 1 to 6 and fix the density of the planted 5-cliques to 4. Synthetic1 allows to test the robustness against background noise. In the second setting (dataset family Synthetic2 ) we vary the density of planted 8-node graphs from 2 to 7, while the average degree of the background network is fixed to 2.
B. Real-world data. We use the following real-world datasets: Facebook  [29]  is a subset of Facebook activity in the New Orleans regional community. Interactions are posts of users on each other walls. The data covers the time period from 9. 05. 06 to 20. 08. 06. The Twitter dataset tracks activity of Twitter users in Helsinki in year 2013. As interactions we consider tweets that contain mentions of other users. The Students 2 dataset logs activity in a student online network at the University of California, Irvine. Nodes represent students and edges represent messages with ignored directions. Enron: 3 is a popular dataset that contains email communication of senior management in a large company and spans several years. For a case study we create a hashtag network from Twitter dataset (the same tweets from users in Helsinki in year 2013): nodes represent hashtags -there is an interaction, if two hashtags occur in the same tweet.
C. Optimal baseline A natural baseline for KGAPPROX is OPTIMAL, which combines exact dynamic programming with finding the optimal densest subgraph for each candidate interval. Due to the high time complexity of OPTIMAL we generate a very small dataset with 60 timestamps, where each timestamp contains a random graph with 3-6 nodes and random density. We vary the number of intervals k and report the value of the solution (without any post-processing) and the running time in Figure  1. On this toy dataset KGAPPROX is able to find near-optimal solution, while it is significantly faster than OPTIMAL.
D. Results on synthetic datasets Next, we evaluate the performance of KGAPPROX on the synthetic datasets Synthetic1 and Synthetic2 by assessing how well the algorithm finds the planted subgraphs. We report mean precision, recall, and F -measure, calculated with respect to the ground-truth subgraphs. All results are averaged over 100 independent runs. First, Figure  2 (a) depicts the quality of the solution as a function of background noise. Recall, that the Synthetic1 dataset contains planted 8-node subgraphs with average degree 5. Precision and recall are generally high for all values of 2 http://toreopsahl. com/datasets/#online social network 3 http://www. cs. cmu. edu/ ∼. /enron/ objective function value running time (sec. ). average degree in the background network. However, precision degrades as the density of the background network increases, as then it becomes cost-beneficial to add more nodes in the discovered densest subgraphs.
E. Results on real-world datasets As the optimal partition algorithm OPTIMAL is not scalable for real datasets, we present comparative results of KGAPPROX with baselines KGOPTDP and KGOPTDS. The KGOPTDP algorithm performs exact dynamic programming, but uses an approximate incremental algorithm for the densest subgraph search (the incremental framework by Epasto et al. [18] ). Vice versa, KGOPTDS performs approximate dynamic programming while calculating the densest subgraph optimally for each candidate interval (by Goldberg's algorithm  [20] ). Note that KGOPTDP has 2(1 + DS ) 2 approximation guarantee and KGOPTDS has (1 + DP ) approximation guarantee. However, even these non-optimal baselines are quite slow on practice and we use a subset of 1 000 interactions of Students and Enron datasets for comparative reporting.
To ensure fairness, we report the total density of the optimal densest subgraphs in the intervals returned by the algorithms. In Table  I  we report the density of the solutions reported by KGAPPROX, KGOPTDP, and KGOPTDS, as well as their running time. We experiment with different parameters for the approximate densest-subgraph search ( DS ) and for approximate dynamic programming ( DP ). For both datasets the best solution was found by KGOPTDS. This is expected, as this algorithm has the best approximation factor. The solution cost decreases as DP increases. On the other hand, KGOPTDS has the largest running time, which decreases with increasing DP , but even with the largest parameter value ( DP = 2) KGOPTDS takes about an hour. The KGOPTDP algorithm typically finds the second-best solution, however it only marginally outperforms KGAPPROX (e.
F. Running time and scalability Figure  3  shows running time of KGAPPROX as a function of the approximation parameters DS and DP. The figure confirms the theory, that is, DS has significant impact on the running time, while the algorithm scales very well with DP. We demonstrate scalability in Figure  4 , plotting the running time for increasing number of interactions, for Facebook and Twitter datasets. Recall that the theoretical running time is O(k 2 m log n), where n is the number of nodes and m the number of interactions. In practice, the running time grows fast for the first thousand interactions and then saturates to linear dependence. This happens because in the beginning of the network history the number of nodes grows fast. In addition, new, denser than previously seen, subgraphs are more likely to occur.
G. Subgraphs with larger node coverage -static graphs Next we evaluate STATICGREEDY. To measure coverage, we simply count the number of distinct nodes in the output subgraphs. We use the 10K first interactions of Students dataset, set k = 20, and test different values of λ. Figure  5  shows the density and the pairwise Jaccard similarity of the node sets of the retrieved subgraphs. The subgraphs are shown in the order they are discovered. Smaller values of λ give larger density, and larger values of λ give more cover. We observe that, for all values of λ, in the beginning STATICGREEDY returns diverse and dense subgraphs, but soon after it returns identical graphs. We speculate that the algorithm finds all dense subgraphs that exist in the dataset. Regarding setting λ, we observe that λ = 0.
VI. CASE STUDY We present a case study using graphs of co-occurring hashtags from Twitter messages in the Helsinki region. We create two subsets of Twitter# dataset: one covering all tweets in November 2013 and another in December 2013. Figure  6  shows the dense subgraphs discovered by the KGAPPROX algorithm on these datasets, with k = 4 and DS = DP = 0. 1. For the November dataset, KGAPPROX creates a small 1day interval in the beginning and then splits the rest time almost evenly. This first interval includes the nodes movember, liiga, halloween, and digiexpo, which cover a broad range of global (e. g. , movember and Halloween) and local events (e. g. , game-industry event DigiExpo and Finnish icehockey league). The next interval is represented by a large variety of well-connected tags related to mtv and media, corresponding to the MTV Europe Music Awards'13 on November 10.
Acknowledgments Work supported by  ANR-16-CE33-0020  MultiFracs, France. G. D. was partially supported by the prime award No. W911NF-14-1-0475  from the  Biomathematics subdivision of the Army Research Office. G. D. 's long-term visits to France were supported by the  "ENS de Lyon" , the  CNRS , and the  Carol Lavin Bernick  faculty grant.
Introduction Knowledge graphs (KGs) provide effective well-structured relational information between entities. A typical KG usually consists of a huge amount of knowledge triples in the form of (head entity, relationship, tail entity) (denoted (h, r, t)), e. g. , (Barack Obama, was_born_in, Hawaii). KG embedding aims at learning embeddings of all entities and relationships, which usually are used to promote down-stream knowledge-driven artificial intelligence (AI) and natural language processing (NLP) tasks, such as human-like reasoning, semantic parsing  [1] , question answering  [2, 3] , relation extraction  [4, 5] , speech generation  [6] , etc. The past decade has witnessed great surge in building web-scale KGs, such as Freebase [7], WordNet  [8] , YAGO  [9] , DBpedia  [10] , Google Knowledge Graph  [11] , and other domain-specific KGs.
• We present a cross entropy based approach for training model. The experimental results on three noisy datasets including FB15K-N1, FB15K-N2 and FB15K-N3 demonstrate the effectiveness of our proposed model. The structure of the paper is as following. In Section 2, we will provide a brief review of related works. In Section 3, we describe the methodology of our model. In Section 4, we present cross entropy learning method. Section 5 presents experimental results followed by their discussion. Finally, Section 6 gives the conclusion and future directions of this research.
Methodology Notation. For each triple (h, r, t), the head entity and tail entity h, t ∈ E and the relation r ∈ R, where E and R represent the sets of entities and relations respectively. D = {(h, r, t)} stands for the overall training dataset with noises. τ h and τ t represent the hierarchical types of head and tail respectively. T represents the set of all types, τ h , τ t ∈ T. d h and d t denote the descriptions of head and tail respectively. w represents the keyword in entity descriptions. W represents the set of keywords, w ∈ W. To learn better embeddings on a noisy knowledge graph, we propose a concept triple trustiness for each triple fact. Triple trustiness denotes the evidential reliability of a triple which can be measured with the favor of external nonstructural auxiliary information.
Datasets Our experiments are conducted on three public benchmark datasets FB15K-N1, FB15K-N2, and FB15K-N3 (The datasets can be accessed at https://github. com/thunlp/CKRL) which are generated based on FB15K with different noise rates (i. e. , 10%, 20%, and 40% respectively) to simulate the real-world KG construction with errors  [23]. FB15K  [17]  is a typical experimental dataset extracted from Freebase. FB15K contains 14951 entities and 1345 relationships, in which all entities possess descriptions. Moreover, we collect 3851 entity types from FB15kET (The FB15kET can be accessed at https://github. ncsu. edu/cmoon2/kg) [49]. Given a positive triple (h, r, t) in KG, the head or tail is randomly replaced to form a negative one (h , r, t) or (h, r, t ). In order to generate harder and more confusing noises, h (or t ) should have appeared in the head (or tail) position with the same relation, which means that the tail entity of relation was_born_of in negative triples should also be a place.
Kg Noise Detection To verify the capability of our TransT models in identifying noises in KGs, we conduct a comparative experiment -KG noise detection according to their triple scores. Evaluation Protocol. We utilize translation-assumption method TransE: M(h, r, t)= h + rt 2 2 as our triple model. Following the triple classification protocol in  [19] , we rank all triples in training set with their model score. Therefore, the higher the model score, the more likely the triple is noise. We use precision/recall curves to show the performances. Experimental Results. Figure  4  demonstrates the evaluation results of KG noise detection, from which we can observe that: (1) Our proposed trustiness-aware model TransTs broadly achieves the best performances on all three datasets with different noise rates, which confirms the capability of our TransT models in modeling tripe trustiness and detecting errors in knowledge graphs.
(  2 ) TransT (TT+DT) has an impressive improvement in error detection compared to TransT (TT). It indicates that the triple trustiness with entity descriptions can provide significant help for error detection. (3) In addition, TransT (TT+DT) has 60∼78% in precision with different noise rates when the recall approximately equals to 40%, which demonstrates the triple trustiness strategy could help for noisy KG embedding. (  4 ) With the noises level rising, TransT (TT+DT) performs better regarding to noise detection. We believe the main reason is that the triples in FB15k-N3 has lower confidence than in FB15k-N1, considering the datasets as bipartite graphs. For instance, there are 671,067 training triples and 187,925 noises in FB15k-N3, while FB15k-N1 only has 529,550 triples and 46,408 noisy triples.
Finally, we use two evaluation metrics for comparison: the mean of those predicted ranks (Mean Rank) and the proportion of correct entities ranked in the top 10 (Hits@10(%)). We also follow the different evaluation settings of "Raw" and "Filter" utilized in  [17]. Experimental Results. Table  3  shows the results of entity prediction with different noise rates, from which we observe that: (1) All TransT models achieve better performance compared with the baseline on all noisy datasets, which confirms the capability of our models in KG completion beyond KG noise detection. (2) Our methods achieve more significant improvement as the noise rate increases, compared with basic mode TransE between the three noisy datasets. It verifies that considering the trustiness in noisy KG embedding is very essential especially when KGs have a high rate of noises.
Triple Classification Triple classification aims to judge whether a triple in test data holds or not, which could be viewed as a binary classification problem, and also can be regarded as a noise detection task in test data. Evaluation Protocol. Since there are no explicit negative triples in existing KGs, we build negative triples in validation and test set with an equal number of positive and negative examples. Following the same protocol in  [19] , we use the validation set to find a threshold ζ. If the model score ||h + r -t|| ≤ ζ in classification, the triple will be classified to be true, otherwise to be false. The final accuracy is based on how many triples are classified correctly. Experimental Results. Table  4  shows the accuracy of the evaluation result of different models. We can find that: (1) The TransT models perform better than the baseline on three datasets, and the improvements become more larger with higher noise rates, which prove that triple trustiness can be helpful for relation triple classification as well.
Conclusions and Future Work In this paper, we concentrate on noisy knowledge graph embedding with triple trustiness. We consider to estimate the triple trustiness according to the conventional external nonstructural auxiliary information, i. e. , entity type instances and entity descriptions. Correspondingly, we propose two sub-models for calculating triple trustiness with entity types and entity descriptions respectively. Through extensive experiments on three real-world datasets, we demonstrate TransT's effectiveness over the baselines. In the future, we will explore the following directions: (1) More external resources can further improve our model. We will explore to combine more external heterogeneous information with internal structural information to further enhance the performance.
Funding: This research was funded in part by the  National Natural Science Foundation of China  under Grant No. 61906159 , in part by the  Sichuan Science and Technology Program  under Grant No. 2018JY0607 , in part by the  Fundamental Research Funds for the Central Universities  under Grant No. JBK1901030 , No. JBK1801080. Acknowledgments: Thanks to the anonymous reviewers for their helpful comments for improving this work.
INTRODUCTION RDF graphs are increasingly being published and shared, as part of the Linked Open Data (LOD) movement. However, given their size, heterogeneity and complexity, their information content is hard to grasp, in particular for nonexpert users. Figure  1  shows a portion of an RDF graph about food, recipes, ingredients, etc. from the Foodista dataset  1. It illustrates the heterogeneity frequently encountered in RDF. Here, n4 and n9 are labeled with the Recipe type; n2 and n11 are Food; n3 lacks a type, but its outgoing properties suggest that it is a Recipe; n8's outgoing properties hint that it is probably Food. This example also shows that logically similar resources do not always have the same structure, e. g. , n4, n5 and n9 are all Recipe, have a title and a country, but only n5 has a depiction.