A systematic knowledge graph-based smart management method for operations: A case study of standardized management.

Standardized routine operation management (SROM) has been widely accepted and applied by kinds of enterprises and played a key supporting role. With full use of the emerging knowledge-based smart management technology, SROM will further increase comprehensive efficiency and save human resources greatly at the same time, especially for small and medium enterprises (SMEs). Hence, we propose a systematic knowledge-based smart management method to transfer SROM activities from human operations to automatic response by means of knowledge explicitation, organization, sharing and reusing, which can be further achieved by employing knowledge graph. We took a typical SROM instance, ISO 9000 implementation management, as an example to validate the transformation from human activities to knowledge graph-based automatic operation. We firstly analyzed characteristics of domain knowledge and constructed an ontology model according to the knowledge stability. Secondly, a hybrid knowledge graph construction and dynamic updating framework together with related algorithms were designed by deliberately integrating semantic similarity calculation and natural language processing. Thirdly, we developed a question-answering mechanism and reasoning system based on the ISO 9000 implementation knowledge graph to support automatic decision and feedback for ISO 9000 routine operation management including knowledge learning and processes auditing. Finally, the practicability and effectiveness of SROM knowledge graph has been validated in a SME in China, realizing the application of question-answering, job responsibility recommendation, conflict detection, semantic detection, multidimensional statistical analysis. The proposed method can also be generalized to support auxiliary optimization decision, vertical risk control, operation mode analysis, optimization model improvement experience and so on.

A hybrid Chinese word segmentation model for quality management-related texts based on transfer learning.

Text information mining is a key step to data-driven automatic/semi-automatic quality management (QM). For Chinese texts, a word segmentation algorithm is necessary for pre-processing since there are no explicit marks to define word boundaries. Because of intrinsic characteristics of QM-related texts, word segmentation algorithms for normal Chinese texts cannot be directly applied. Hence, based on the analysis of QM-related texts, we summarized six features, and proposed a hybrid Chinese word segmentation model by means of integrating transfer learning (TL), bidirectional long-short term memory (Bi-LSTM), multi-head attention (MA), and conditional random field (CRF) to construct the mTL-Bi-LSTM-MA-CRF model, considering insufficient samples of QM-related texts and excessive cutting of idioms. The mTL-Bi-LSTM-MA-CRF model is composed of two steps. Firstly, based on a word embedding space, the Bi-LSTM is introduced for context information learning, and the MA mechanism is selected to allocate attention among subspaces, and then the CRF is used to learn label sequence constraints. Secondly, a modified TL method is put forward for text feature extraction, adaptive layer weights learning, and loss function correction for selective learning. Experimental results show that the proposed model can achieve good word segmentation results with only a relatively small set of samples.