## 2 Related Work
These methods combine various techniques, algorithms, or resources to enhance ATE accuracy. This can involve synergistically integrating supervised, unsupervised, or other methods to address their individual limitations. The primary goal of these hybrid approaches is to achieve superior performance in aspect term extraction compared to using a single approach in isolation [35; 36; 37; 38; 39; 40; 41]. In their work, [PERSON] et al. [35] introduce a novel unsupervised approach for ATE that combines linguistic conjunction rules with pruning and a deep learning model employing a Gated Recurrent Unit (GRU) network. [PERSON] et al. [36] propose a two-stage unsupervised approach for ATE. This model harnesses both symbolic and neural techniques by integrating linguistic rules with an attention-based Bi-LSTM architecture. This integration aims to enhance the overall ATE performance. [PERSON] et al. [37] introduce a novel end-to-end framework for ABSA. It leverages a task-sharing layer to facilitate interaction between aspect term extraction and sentiment classification. Furthermore, the framework employs a multi-head attention mechanism to capture the relationships between different aspect items, addressing the issue of sentiment inconsistency within aspects observed in previous end-to-end models. [PERSON] et al. [38] introduced a model called SpanGCN, which is a Span-based dependency-enhanced Graph Convolutional Network. This model combines the Latent dependency Graph Convolutional Network (LGCN) with span enumeration to tackle the ABSA task. In this research, BERT and the Graph Convolutional Network (GCN) are employed to capture contextual semantic and dependency relationships. [PERSON] et al. [39] presented a new model called the Syntactic Dependency Graph Convolutional Network (SD-GCN). This model uses GCN to implement syntactic dependencies and yield enhanced aspect features for ATE. [PERSON] and [PERSON]. [40] introduced a novel model named Dependency-Type Weighted Graph Convolution Network (DTW-GCN) for the purpose of combining dependency-type messages with word embedding in the context of ABSA. [PERSON] et al. [41] presented Ensemble BiLSTM, a novel approach for aspect extraction. The Ensemble BiLSTM model utilizes the syntactic, semantic, and contextual properties of unstructured texts found in BERT word embeddings to extract aspect terms. Additionally, it incorporates the sequential properties of aspect term categories by employing an ensemble of BiLSTM model. In summary, supervised learning methods utilize pre-annotated data to train models for ATE, offering effectiveness but requiring substantial labeled data and struggling with unseen aspects. Unsupervised methods, conversely, operate without labeled data, providing domain independence but facing challenges with unseen aspects as well. Hybrid approaches combine supervised and unsupervised techniques or integrate them with other methods, showcasing greater adaptability across diverse data types and domains. These hybrid approaches face challenges about fixed lexicons and rules. This research proposes a new unsupervised ATE approach that addresses these limitations. It leverages the training dataset to represent knowledge and utilizes the semantic power of LOD to suggest synonymous aspect terms. This approach expands the knowledge base beyond the limitations of learning aspect terms solely from the training dataset, allowing the model to identify both single-word and multi-word aspect terms not explicitly present in the training dataset. This overcomes the restrictions of existing supervised, unsupervised, and hybrid methods. This research will establish hypotheses based on two observations, relying from machine learning methods in related works: (1) The model's performance can be enhanced by utilizing the single-word and multiple-word aspect terms. (2) The model's efficacy can be enhanced by aspect terms that have infer. This research displays the methodology of LOD used for aspect extraction in the following section, which corroborates the hypotheses.