Abstract: Disclosed is a method for efficiently removing fluorine from a spent lithium battery. The method comprises: mixing aluminum and a sodium hydroxide solution for reaction to obtain a sodium metaaluminate solution; introducing sulfuric acid into the sodium metaaluminate solution, and stirring to react at a certain temperature to obtain a fluorine removal agent; adding a sodium fluoroaluminate seed crystal and the fluorine removal agent into an impurity-removed battery powder leaching solution, introducing a sodium carbonate solution at the same time, performing reaction at a certain temperature, controlling the pH value of a reaction endpoint, and performing solid-liquid separation after the reaction is finished to obtain a fluorine-removed liquid and filter residues; and adding the sodium hydroxide solution into the filter residues for reaction, and performing solid-liquid separation to obtain a filtrate containing fluorine and aluminum, and insoluble residues.

Abstract: Provided is an automatic construction method of a software bug knowledge graph. The method includes extraction of a relationship triple of a bug and domain classification of the bug. Specifically, the method includes: collecting bug information in a bug library and processing bug description information, obtaining a verb phrase and a noun phrase in a description sentence by means of natural language processing, and then obtaining a relationship triple of the bug according to a dependency relationship between words related to the bug information, extracting a domain feature of the bug, performing learning and training with a semi-supervised classifier to enable the classifier automatically to classify unlabeled triples, storing all the classified relationship triples in a graph database, and thus constructing a software bug knowledge graph.

Abstract: Provided is an entity and relationship joint extraction method oriented to software bug knowledge. The method includes collecting text data of an open-source bug library and preprocessing the text data to obtain a bug text data corpus; extracting, from the bug text data corpus, a statement S for describing a bug, and then processing S, and using the processed S as a subsequent input statement; constructing an entity and relationship joint extraction model; obtaining, in conjunction with the constructed entity and relationship joint extraction model based on a transition system, an entity set E and a relationship set R corresponding to the input statement; and outputting the entity set E and the relationship set R to complete joint extraction of entities and relationships.

Abstract: Provided is an entity and relationship joint extraction method oriented to software bug knowledge. The method includes collecting text data of an open-source bug library and preprocessing the text data to obtain a bug text data corpus; extracting, from the bug text data corpus, a statement S for describing a bug, and then processing S, and using the processed S as a subsequent input statement; constructing an entity and relationship joint extraction model; obtaining, in conjunction with the constructed entity and relationship joint extraction model based on a transition system, an entity set E and a relationship set R corresponding to the input statement; and outputting the entity set E and the relationship set R to complete joint extraction of entities and relationships.

Abstract: Provided is an automatic construction method of a software bug knowledge graph. The method includes extraction of a relationship triple of a bug and domain classification of the bug. Specifically, the method includes: collecting bug information in a bug library and processing bug description information, obtaining a verb phrase and a noun phrase in a description sentence by means of natural language processing, and then obtaining a relationship triple of the bug according to a dependency relationship between words related to the bug information, extracting a domain feature of the bug, performing learning and training with a semi-supervised classifier to enable the classifier automatically to classify unlabeled triples, storing all the classified relationship triples in a graph database, and thus constructing a software bug knowledge graph.