An event knowledge graph system for the operation and maintenance of power equipment

IET Generation, Transmission & DistributionVolume 16, Issue 21 p. 4291-4303 ORIGINAL RESEARCHOpen Access An event knowledge graph system for the operation and maintenance of power equipment Jiapeng Tian, Jiapeng Tian orcid.org/0000-0002-8034-2515 School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China Contribution: Conceptualization, Formal analysis, ​Investigation, Methodology, Writing - original draft, Writing - review & editingSearch for more papers by this authorHui Song, Corresponding Author Hui Song [email protected] School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China Correspondence Hui Song, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. Email: [email protected] Contribution: Data curation, Project administration, SupervisionSearch for more papers by this authorGehao Sheng, Gehao Sheng School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaSearch for more papers by this authorXiuchen Jiang, Xiuchen Jiang School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaSearch for more papers by this author Jiapeng Tian, Jiapeng Tian orcid.org/0000-0002-8034-2515 School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China Contribution: Conceptualization, Formal analysis, ​Investigation, Methodology, Writing - original draft, Writing - review & editingSearch for more papers by this authorHui Song, Corresponding Author Hui Song [email protected] School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China Correspondence Hui Song, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. Email: [email protected] Contribution: Data curation, Project administration, SupervisionSearch for more papers by this authorGehao Sheng, Gehao Sheng School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaSearch for more papers by this authorXiuchen Jiang, Xiuchen Jiang School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaSearch for more papers by this author First published: 31 August 2022 https://doi.org/10.1049/gtd2.12598AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Power equipment operation and maintenance (O&M) requires plenty of domain knowledge to improve equipment security and power grid reliability. However, most knowledge is implicitly represented by the semantic text, which is hard to be comprehended by algorithms and restricts the intelligence level of equipment O&M. Therefore, this article proposes an event knowledge graph system to automatically extract event knowledge from O&M reports and represent the knowledge by an event knowledge graph (KG). First, a bidirectional long short-term memory network (BiLSTM) combined with conditional random field (CRF) is employed to recognize the entities. Next, an attention-based sequence-to-sequence model is proposed to detect multiple events and extract document-level representation vectors. Then, a tree-based table filling strategy is utilized to complete the artificially designed event tables. Finally, the proposed system renders the event tables into an event KG. Verified by the experiments, the proposed system outperforms the baselines with the F-measure of 83.5% for event extraction and 94.2% for event detection. When dealing with coexisting events and scattering arguments, the F-measure of the proposed system achieves 74.8% and 79.0%, respectively. Moreover, the proposed system has positive effects on intelligent retrieval, fault diagnosis, and condition assessment, verifying its potential value for equipment O&M. 1 INTRODUCTION The continuous expansion of power grids have witnessed exploding requirements to the intelligent operation and maintenance (O&M) of power equipment [1-3]. With the wide application of information technologies, massive data (both numerical and textual) is accumulated to support the security and stability of power equipment [4, 5]. Textual data (e.g. fault report, maintenance report, operation report) generally records important events, analyzes firsthand materials, and summarises experience [4]. These semantic reports provide sufficient domain knowledge for decision-making on the O&M of power equipment. Conventional equipment O&M, mostly carried out from the policy perspective [3], rely heavily on expert experience. Hence, even with the assistance of data-driven models (e.g. deep learning models), plenty of domain knowledge still must be queried and memorized manually to make rational decisions. Although many power domain knowledge systems have been formed [5], highly abstracted knowledge is still implicitly represented by semantic text and stored in disparate databases. Such inefficient knowledge representation and storage approach potentially spells incorrect heterogeneous knowledge processing and inappropriate O&M decision-making. These could trigger catastrophic failures. Moreover, data-driven models utilized for diagnosing power equipment are sensitive to the datasets [6, 7] and deficient in anti-interference, which restricts the comprehensive intelligence level of equipment O&M. Therefore, a knowledge system that extracts event knowledge from semantic text and intuitively represents the knowledge is imperative for power equipment O&M. Event extraction models are vital for constructing an equipment knowledge system. They extract event knowledge from the semantic text by detecting trigger words and corresponding event arguments [8]. Traditionally, event extraction is carried out by event templates and statistical features constructed by domain experts [9]. With the development of deep learning, pattern matching and statistical machine learning methods are replaced by representation-based methods to relieve human labour. Chen et al. [10] employed convolutional neural networks (CNN) to automatically extract word-level and document-level features, which is the first application of deep learning on event extraction. Inspired by this work, Nguyen et al. [11] proposed a joint approach based on recurrent neural networks (RNN), outperforming CNNs on the trigger labelling task. Besides, Liu et al. [12] developed an event detector based on attention mechanisms, which extracted global features from local information to improve the performance in event detection. Despite the excellent performance on short text, these methods suffer from document-level event extraction due to the scattering arguments and coexisting events. Yang et al. [13] utilized a sequence tagging model to conduct multi-event extraction, but the argument completion strategy employed is context-agnostic, limiting the resolution of the scattering argument problem. Although Zheng et al. [8] proposed a graph-based event table filling method to improve the performance in dealing with this problem, the binary classifiers they utilized ignore the correlations between events. This paper proposes a document-level event knowledge graph system for equipment O&M to automatically extract multiple events and represent them by an event knowledge graph (KG). The implementation procedure for the proposed system is as follows: 1) A bidirectional long short-term memory network (BiLSTM) combined with a conditional random field layer (CRF) is employed to extract entities from each sentence in the report. 2) The max-pooling technique is utilized to convert the corresponding slices of the extracted entities and sentences into entity-level embeddings and sentence embeddings. 3) The entity-level embeddings and sentence embeddings are fed into the event detector encoder, which employs another BiLSTM to extract document-level representation vectors. 4) The decoder and trigger of the event detector utilize an attention mechanism to analyze the document-level representation vectors and recognize multiple event categories. These event categories are composed into a sequence as the output of the event detector. 5) Analyzing the document-level representation vectors, recognized event categories, and the previously recognized arguments, the BiLSTM of the tree-based table filler obtains the classification features. 6) The binary classifier of the filler conducts a classification operation on the classification features to recognize the current arguments until the event table is completed. 7) The extracted event tables are rendered into an event KG for further knowledge-driven applications. The event KG constructed by the proposed system is a multi-relational graph composed of entities (nodes) and relations (edges) [14]. It is an efficient knowledge representation approach that can be modelled digitally and fused with numerical data collected by sensors. Since many researchers have proved the effectiveness of KG in the domain of power equipment [6, 15-17], the study of event KG systems is essential for achieving a self-understanding, self-decision-making behaviour of equipment O&M [5]. The main contributions of this paper are as follows: A document-level event extraction algorithm is proposed to automatically extract event knowledge from the O&M reports by recognizing entities, detecting events, and filling event tables. We employed an attention-based sequence-to-sequence model to detect multiple events without trigger words, fully considering the correlations between event categories. A tree-based table filling strategy is utilized to solve the challenge of argument-scattering and simplify the table filling task. This paper provides several application paradigms of event KG in power equipment O&M. The rest of the paper is organized as follows: Section 2 presents the event extraction task. Section 3 details the methodology of the proposed system. In Section 4, the experimental results are demonstrated and analyzed. Section 5 presents the applications of the proposed system. Finally, Section 6 draws the conclusion. 2 EVENT EXTRACTION TASK FOR O&M REPORTS The O&M reports of power equipment record plenty of events in the form of semantic text, which is a primitive representation method of domain knowledge and hard to be modelled. Therefore, it is vital for the equipment O&M to extract event knowledge from the reports and represent it by an event KG. The purpose of the event extraction task for O&M reports is to transform the unstructured semantic reports into structured event tables. The event extraction task is composed of the subtasks: entity recognition, event detection, and event table filling. Figure 1 illustrates the process of an event extraction task and shows the relationships between these three subtasks. FIGURE 1Open in figure viewerPowerPoint Event extraction from a generator O&M report The event mention, which refers to the description of the report, is split into several sentences before entity extraction is conducted. The entities, red tags in Figure 1, refer to the essential information (e.g. date, equipment, location) recorded in the event mentions. The entity extraction subtask is a sequence tagging task classifying each word in a sentence as five categories “B,” “I,” “E,” “O,” or “S.” The first four stand for the begin, inside, end, and outside of an entity, respectively, and “S” stands for the entity with a single word. The proposed system recognizes the entities by analyzing the comprehensive information of each sentence. These entities are seen as the candidates of the event arguments, which means further works should be carried out to confirm which entities are related to the anticipated events. Since multiple event categories (e.g. maintenance event, historical event, decision event) with different properties are recorded in the reports, the detection task, also seen as a multi-label classification task, is carried out to detect and classify different events. Traditionally, the detection task is conducted by detecting trigger words, normally predicates, which depends heavily on manually designed predicate dictionaries. Conversely, the proposed system automatically categories the events by analyzing the global document-level features and the local entity-level features. The system employs a sequence-to-sequence model to detect multiple event categories from a report. The detection result is represented by a tag sequence, which sorts the event categories by the frequency of the categories in the training set. Moreover, the tags b e g $beg$ and e n d $end$ are added to the head and tail of the tag sequence for annotating the beginning and end of the tag sequence. Finally, the event table, particularly designed for each event category, needs to be filled to implement the entire event extraction task. The event table has two different types of columns, as shown in Figure 1, indicating event argument and event role, respectively. The former refers to the entity that participate in the event, while the latter refers to the role that the argument plays. Therefore, the table filling subtask determines which entity belongs to the event arguments and which role it plays. This subtask is the most complex and essential because of the scattering arguments and coexisting events. To solve these challenges, we proposed a tree-based table filling strategy, which will be detailed in the next section. 3 EVENT KG SYSTEM FOR POWER EQUIPMENT O&M The proposed event KG system automatically constructs the event KG by extracting events from power equipment O&M reports and rendering them into a KG, as shown in Figure 2. A BiLSTM layer combined with a CRF layer recognizes the entities from each sentence in the report. The corresponding slices of the sentences and entities are converted into sentence embeddings and entity-level embeddings. Then, the event detector encoder, another BiLSTM layer, extracts document-level information for the attention decoder and the event table filling subtask. Analyzing the document-level information, the decoder and trigger recognize multiple event categories. Next, the document-level information, recognized event categories, and the previously recognized arguments are fed into the third BiLSTM layer to obtain the classification features. Classified by a binary classifier, the current arguments are recognized. After extracting the events, the completed event tables are finally rendered into an event KG. FIGURE 2Open in figure viewerPowerPoint Methodology of the Event KG system for power equipment O&M Entity recognition via BiLSTM and CRF To convert the words recorded in the reports into embedding vectors, a language representation model known as the Bidirectional Encoder Representation from Transformers (BERT), more details in [18], is employed due to its excellent comprehension ability of natural language. The BERT embeds each sentence of a report into an embedding matrix s i ∈ R d e × l s $s_i\in \mathbb {R}^{d_e\times l_s}$ , where the column vector indicates the word embedding with a length of d e $d_e$ , and l s $l_s$ denotes the sentence length. Then, the embedding tensor S = { s 1 ; s 2 ; ⋯ ; s l d } $S=\lbrace s_1;s_2;\dots ;s_{l_d}\rbrace$ of the report, which has l d $l_d$ sentences, is given to the system for the entity recognition subtask. The proposed system employs the BiLSTM and CRF to extract entities from sentences. Take s i = { x 1 ; x 2 ; ⋯ ; x l s } $s_i=\lbrace x_1;x_2;\dots ;x_{l_s}\rbrace$ as an example, where x t ∈ R d e $x_t\in \mathbb {R}^{d_e}$ indicates the word embedding. The BiLSTM is utilized to extract the sentence-level features of x t $x_t$ due to its ability to learn long-term dependencies. In the entity recognition subtask, BiLSTM bidirectionally transfers the information between words by the input gate i t $i_t$ , the forget gate f t $f_t$ , and the output gate o t $o_t$ . The gates are given by (1), with the information transferring in positive order: i t = σ ( W i · [ x t , h t − 1 ] + b i ) f t = σ ( W f · [ x t , h t − 1 ] + b f ) o t = σ ( W o · [ x t , h t − 1 ] + b o ) , \begin{equation} {\left\lbrace \def\eqcellsep{&}\begin{array}{c}i_t=\sigma (W_i\cdot [x_t,h_{t-1}]+b_i)\\[8pt] f_t=\sigma (W_f\cdot [x_t,h_{t-1}]+b_f)\\[8pt] o_t=\sigma (W_o\cdot [x_t,h_{t-1}]+b_o) \end{array} \right.} ,\end{equation} (1)where W i $W_i$ , W f $W_f$ , W o ∈ R d 1 × ( d e + d 1 ) $W_o \in \mathbb {R}^{d_1 \times (d_e+d_1)}$ are the weights; b i $b_i$ , b f $b_f$ , b o ∈ R d 1 $b_o \in \mathbb {R}^{d_1}$ are the biases; h t − 1 $h_{t-1}$ indicates the previous output with x t − 1 $x_{t-1}$ as input; σ ( · ) $\sigma (\cdot )$ indicates the sigmoid function; and [ · , · ] $[\cdot , \cdot ]$ indicates the concatenate operation. In the BiLSTM, a cell unit C t $C_t$ given by (2) is proposed to memorize the long-term information: C t ∼ = t a n h ( W c · [ x t , h t − 1 ] + b c ) C t = f t ⊙ C t − 1 + i t ⊙ C t ∼ , \begin{equation} {\left\lbrace \def\eqcellsep{&}\begin{array}{l}\tilde{C_t}=tanh(W_c\cdot [x_t,h_{t-1}]+b_c)\\[8pt] C_t=f_t\odot C_{t-1}+i_t\odot \tilde{C_t} \end{array} \right.} ,\end{equation} (2)where t a n h ( · ) $tanh(\cdot )$ denotes the hyperbolic tangent activity function and ⊙ denotes the hadamard product. The forget gate f t $f_t$ decides what information to forget from the previous cell memory C t − 1 $C_{t-1}$ , while the input gate i t $i_t$ decides what information in C t ∼ $\tilde{C_t}$ to save for updating the cell unit C t $C_t$ . Then, the output h t ∈ R d 1 $h_t \in \mathbb {R}^{d_1}$ of the positive order is given by (3): h t = o t ⊙ t a n h ( C t ) . \begin{equation} h_t=o_t\odot tanh(C_t).\end{equation} (3) Similarly, the output h t ′ $h_{t}^{\prime }$ of the negative order is obtained by replacing h t − 1 $h_{t-1}$ and C t − 1 $C_{t-1}$ with h t + 1 $h_{t+1}$ and C t + 1 $C_{t+1}$ . Therefore, the BiLSTM layer is reduced to c i = b i l s t m 1 ( s i ) $c_i=bilstm1(s_i)$ , where c i = { y 1 ; y 2 ; ⋯ ; y l s } $c_i= \lbrace y_1;y_2;\dots ;y_{l_s}\rbrace$ , and y t = [ h t , h t ′ ] $y_t=[h_t,h_t^{\prime }]$ indicates the sentence-level feature vector of word x t $x_t$ . Finally, a dimensional conversion operation c i ∼ = W 1 · c i + b 1 $\tilde{c_i}= W_1\cdot c_i +b_1$ is employed to reduce the dimension of y t $y_t$ to the category number, where c i ∼ = { y 1 ∼ ; y 2 ∼ ; ⋯ ; y l s ∼ } $\tilde{c_i}= \lbrace \tilde{y_1};\tilde{y_2};\dots ;\tilde{y_{l_s}}\rbrace$ , W 1 ∈ R 5 × 2 d 1 $W_1 \in \mathbb {R}^{5\times 2d_1}$ denotes the weight, and b 1 ∈ R 5 $b_1\in \mathbb {R}^5$ denotes the bias. However, since the tag sequence should obey certain conditions that the BiLSTM cannot meet (e.g. the tag “I” should be between the tags “B” and “E”), a CRF layer is employed to constrain the recognition results. CRF is a probabilistic graphical model (more details in [19]), which models the conditional probability P ( c i ∼ | s i ) $P(\tilde{c_i}|s_i)$ as follows: P ( c i ∼ | s i ) = 1 Z exp ∑ j , k λ k t k ( y ∼ j − 1 , y j ∼ , s i , j ) + ∑ j , l μ l s l ( y j ∼ , s i , j ) , \begin{equation} \begin{aligned} P(\tilde{c_i}|s_i)=&\frac{1}{Z}\exp \left(\sum _{j,k}\lambda _kt_k(\tilde{y}_{j-1},\tilde{y_j},s_i,j)\right.\\ &+\left.\sum _{j,l}\mu _ls_l(\tilde{y_j},s_i,j)\right) \end{aligned} ,\end{equation} (4)where Z indicates the normalization factor, λ k $\lambda _k$ and μ l $\mu _l$ are the weights, t k ( · ) $t_k(\cdot )$ indicates the transfer eigenfunction , and s l ( · ) $s_l(\cdot )$ indicates the state eigenfunction. Therefore, the loss function of the entity recognition subtask is given by (5) expecting to maximize the probability of predicting the ground truth tag sequence: L e r = − P r e a l P t o t a l , \begin{equation} L_{er} = -\frac{P_{real}}{P_{total}} ,\end{equation} (5)where P r e a l $P_{real}$ denotes the conditional probability of the real tag sequence, and P t o t a l $P_{total}$ denotes the sum of probabilities of all potential tag sequences. Both of them are obtained by (4). Moreover, the max-pooling technique, given by (6), is utilized to obtain the sentence embeddings for detecting events: ( c i s ) j = max { ( y 1 ) j , ( y 2 ) j , ⋯ , ( y l s ) j } , \begin{equation} (c_i^s)_j=\max \lbrace (y_1)_j,(y_2)_j,\dots ,(y_{l_s})_j\rbrace ,\end{equation} (6)where ( c i s ) j $(c_i^s)_j$ denotes the jth entry of the sentence embedding c i s ∈ R 2 d 1 $c_i^s\in \mathbb {R}^{2d_1}$ . With the same operation, the entity embedding e i ∈ R 2 d 1 $e_i\in \mathbb {R}^{2d_1}$ is obtained from the corresponding slices of c i $c_i$ . Furthermore, since an entity could appear more than once in the report, e i $e_i$ and e j $e_j$ , which represent the same entity, are merged into an entity-level embedding e i e ∈ R 2 d 1 $e_i^e\in \mathbb {R}^{2d_1}$ by the max-pooling operation. Event detection without trigger words A sequence-to-sequence model, shown in Figure 3, is proposed to detect and correlate multiple event categories by outputting a category sequence with a length of l c $l_c$ . The detection model is composed of an encoder, an attention-based decoder, and an event trigger. FIGURE 3Open in figure viewerPowerPoint Sequence-to-sequence event detection model for classifying multiple event categories The encoder transfers the information between sentences and entities to extract document-level features by a BiLSTM layer H ̂ = b i l s t m 2 ( [ C s , E e ] ) $\hat{H}=bilstm2([C^s,E^e])$ , where b i l s t m 2 ( · ) $bilstm2(\cdot )$ denotes the single-layer BiLSTM with a hidden size of d2. The b i l s t m 2 ( · ) $bilstm2(\cdot )$ is implemented by the same principle as b i l s t m 1 ( · ) $bilstm1(\cdot )$ . C s = { c 1 s ; ⋯ ; c l d s } $C^s=\lbrace c_1^s; \dots ; c_{l_d}^s\rbrace$ denotes the sentence embedding matrix, E e = { e 1 e ; ⋯ ; e l e e } $E^e=\lbrace e_1^e; \dots ; e_{l_e}^e\rbrace$ denotes the entity embedding matrix, and l e $l_e$ indicates the recognized entity number. The document-level feature matrix H ̂ ∈ R 2 d 2 × ( l d + l e ) $\hat{H}\in \mathbb {R}^{2d_2\times (l_d+l_e) }$ is split into the document-level context matrix C d ∈ R 2 d 2 × l d $C^d\in \mathbb {R}^{2d_2 \times {l_d}}$ and the document-level entity matrix E d ∈ R 2 d 2 × l e $E^d\in \mathbb {R}^{2d_2 \times {l_e}}$ for the table filling subtask. Since different event categories render unfair preference on different sentences or entities in the report, an attention-based decoder is proposed to decode the document-level features. Suppose α i j $\alpha _{ij}$ is the preference weight of the predicted category t j $t_j$ on the feature vector h i ̂ $\hat{h_i}$ , then the preference weight is given by the follows: λ i j = v α T · t a n h ( W α z j + U α h i ̂ + b α ) , \begin{equation} \lambda _{ij}=v_\alpha ^T\cdot tanh(W_\alpha z_j+U_\alpha \hat{h_i}+b_\alpha ) ,\end{equation} (7) α i j = exp ( λ i j ) ∑ n = 1 l d + l e exp ( λ n j ) , \begin{equation} \alpha _{ij}=\frac{\exp (\lambda _{ij})}{\sum _{n=1}^{l_d+l_e}\exp (\lambda _{nj})} ,\end{equation} (8)where v α ∈ R d α $v_\alpha \in \mathbb {R}^{d_\alpha }$ , W α ∈ R d α × d 3 $W_\alpha \in \mathbb {R}^{d_\alpha \times d_3}$ , U α ∈ R d α × 2 d 2 $U_\alpha \in \mathbb {R}^{d_\alpha \times 2d_2}$ are the weights; b α ∈ R d α $b_\alpha \in \mathbb {R}^{d_\alpha }$ is the bias; and z j ∈ R d 3 $z_j\in \mathbb {R}^{d_3}$ indicates the jth hidden state of the decoder for classifying event categories. The weighted feature vector u j $u_j$ is obtained by (9): u j = ∑ n = 1 l d + l e α n j h n ̂ . \begin{equation} u_j=\sum _{n=1}^{l_d+l_e} \alpha _{nj}\hat{h_n} .\end{equation} (9) Considering the correlations of event categories, we utilize a unidirectional LSTM layer, the same principle as the positive ordered BiLSTM, to fuse the information of the previous classification result t j − 1 $t_{j-1}$ into z j $z_j$ , as follows: z j = l s t m ( [ u j − 1 , g c ( t j − 1 ) ] ) \begin{equation} z_j=lstm([u_{j-1},g_c(t_{j-1})]) \end{equation} (10)where g c ( · ) $g_c(\cdot )$ is the weighted category embedding unit in the trigger. The trigger, composed of a classifier and an embedding unit, classifies event categories to decide which event tables will be filled. During the detecting task, the classifier categorizes multiple events sequentially by analyzing the information obtained by the decoder, and the embedding unit feeds back the previous classification results to the decoder. The classifier is given as follows: t i = s o f t m a x ( W d · z i + V d · u i + b d ) , \begin{equation} t_i=softmax(W_d\cdot z_i+V_d\cdot u_i+b_d) ,\end{equation} (11)where W d ∈ R d c × d 3 $W_d\in \mathbb {R}^{d_c\times d_3}$ and V d ∈ R d c × 2 d 2 $V_d\in \mathbb {R}^{d_c\times 2d_2}$ are the weights, b d ∈ R d c $b_d\in \mathbb {R}^{d_c}$ is the bias, s o f t m a x ( · ) $softmax(\cdot )$ indicates the normalized exponential function, and d c $d_c$ indicates the number of event category. The output t i ∈ R d c $t_i\in \mathbb {R}^{d_c}$ is a probability distribution, where the category represented by the highest probability is considered the predicting result. However, if the trigger only feeds back the category with the highest probability to the decoder, the classification error will be accumulated rapidly once a predicting result is incorrect. Therefore, in the embedding unit g c ( · ) $g_c(\cdot )$ , the embeddings of all the categories are weighted according to t i $t_i$ , as follows: g c ( t i ) = W e c · t i , \begin{equation} g_c(t_i)=W_e^c\cdot t_i ,\end{equation} (12)where W e c ∈ R 2 d 2 × d c $W_e^c\in \mathbb {R}^{2d_2\times d_c}$ is the trainable embedding matrix with the category embeddings as the columns. Event table filling based on event tree The proposed system conducts the table filling subtask to convert the unstructured reports into structured event tables. However, tables are difficult to flexibly change the structure based on the number of the recognized events, making it challenging to fill multi-events into a table directly. Therefore, the event table filler generates event trees before converting them into tables. This tree-based strategy reduces the difficulty of the table filling task by splitting it into several more straightforward path expanding tasks. The generated tree T $\mathcal {T}$ is shown in Figure 4, where multiple paths are derived from the tree root denoting the event category. Each path P $\mathcal {P}$ of the tree represents an event table. The nodes with the same depth represent the event arguments playing the same role. The proposed system employs the breadth-first search (BFS), a strategy of traversing tree nodes, to sequentially predict the arguments until the depths of all paths reach the role number of the event table. Since multiple event categories could be detected in a report, the event trees representing different categories constitute an event forest. FIGURE 4Open in figure viewerPowerPoint A maintenance event tree with event category as the root ALGORITHM 1. Pseudo code of tree path expanding During the table filling task, a memory matrix M = [ C d , P ] $M=[C^d,\mathcal {P}]$ is utilized to save the information of the generated path. The path P = { g c ( t i ) ; a r g α ; a r g β ; ⋯ } $\mathcal {P}=\lbrace g_c(t_i);arg_\alpha ;arg_\beta ;\dots \rbrace$ is a matrix, where the columns indicate the event category or previously predicted event arguments. Then, a BiLSTM given by (13) is employed to transfer the information between the context, tree path, and entities: H c = b i l s t m 3 ( [ M , E d ] + g r ( R o l e k ) ) , \begin{equation} H^c=bilstm3([M,E^d]+g_r(Role_k)) ,\end{equation} (13)where b i l s t m 3 ( · ) $bilstm3(\cdot )$ denotes the two-layer BiLSTM with a hidden size of d4, and each layer of b i l s t m 3 ( · ) $bilstm3(\cdot )$ is bidirectional.