A ttri S age: Product Attribute Value Extraction Using Graph Neural Networks (2024)

Abstract: Extracting the attribute value of a product from the given product description is essential for ecommerce functions like product recommendations, search, and information retrieval. Therefore, understanding products in E-commerce. Greater accuracy certainly gives any retailer the edge. The burdensome aspect of this problem lies in the diversity of the products and their attributes and values. Existing solutions typically employ large language models or sequence-tagging approaches to capture the context of a given product description and extract attribute values. However, they do so with limited accuracy, which serves as the underlying motivation to explore a more comprehensive solution. Through this paper, we present a novel approach for attribute value extraction from product description leveraging graphs and graph neural networks. Our proposed method demonstrates improvements in attribute value extraction accuracy compared to the baseline sequence tagging approaches.

## introduction in the dynamic landscape of e-commerce, where a wide range of products are readily available to consumers, efficient and accurate product understanding plays a pivotal role in facilitating seamless user experiences. the attributes associated with products, including details such as color, material, brand, type, and more, hold the key to enabling users to find their desired items more efficiently. e-commerce platforms usually provide product descriptions but consumers prefer a quick and intuitive way to narrow down their search and make informed purchasing decisions. product titles usually contain attributes and their corresponding values but this data is mostly unstructured, noisy, and often contains missing values. for example, in figure 1 , a product along with its context (description) * these authors contributed equally to this work is provided. along with the description, there are attribute-value pairs for attributes including gender, brand, feature, etc ; but, there also missing attributes for values like model number (value: 849558), model name (value: air vapormax), etc. hence we need models that predict attribute values for the attributes that have not been seen before. the critical role of product attributes has driven extensive research efforts to explore innovative methods for their extraction and categorization. previous works, including those by @xcite @xcite , focused on attribute value extraction using a rule-based approach. in this methodology, a domain-specific seed dictionary played a crucial role in identifying key phrases and extracting attribute values. the rule-based systems relied on predefined patterns and heuristics to recognize and capture relevant information from unstructured data, providing a foundational approach to attribute extraction in the context of specific domains. other works proposed a named-entity recognition (ner) task @xcite for this problem; although ner relies on pre-existing knowledge of named entities. when faced with previously unseen brands, models, or attributes, the system struggles to identify and extract these values accurately. in such situations, a more context-aware approach, like question-answering-based techniques that employ sequence-to-sequence models, might be more effective for attribute value extraction from product descriptions. later introduced works that employed sequence-to-sequence models performed better than the former models, however, these approaches have a few shortcomings-(a) they do not exploit the structural relationships between product description and attribute values across the dataset. for example, assume product descriptions c 1 and c 2 share a common attribute value t 1 . if there is another attribute value t 2 relevant to c 2 and other similar product descriptions, we can infer that t 2 might also be relevant to c 1 . such transitive cues can be beneficial for identifying missing attribute values. (b) language models bring high computational costs at massive scales as any task not only involves predicting multiple missing attribute values but also requires precise organization of the most relevant attribute values specific to the product. graphs are naturally suitable to make the relationships explicit such as product description-attribute value networks. (c) with the growing popularity of llms, we tend to oversee the ecological impact they have on the environment. they consume vast computational resources, leading to significant energy use and high carbon emissions. in this work, our focus is on advancing the domain of product attribute value extraction through a novel approach that leverages graph models and graph neural networks (gnns). our primary goal is to enhance the generalizability of existing approaches and provide more interpretable predictions. we construct a product data graph using a dataset comprising 110k product title-attribute triples, enabling us to gain deeper insights into the data. leveraging graph-based neural network architecture we performed a node classification task to classify our title nodes with multiple attribute values. through this work, we aim to contribute the following: ## related work initial works focusing on the attribute value extraction task involved the use of domain-specific rules to detect attribute-value combinations from product descriptions @xcite . the first learning-based approaches required substantial feature engineering and were limited in their capacity to generalize to unknown features and attribute values. the initial application of the bidirectional lstm with a conditional random field layer (bilstm-crf model) for sequence tagging in attribute value extraction was introduced by @xcite proposed an end-to-end tagging model, opentag utilizing bil-stm, crf, and attention mechanisms, eliminating the need for dictionaries or hand-crafted features. however, this methodology poses scalability challenges when dealing with a large set of attributes and cannot identify emerging values for previously unseen attributes. an extension to opentag, su-opentag was proposed by @xcite which encodes both a target attribute and the product title using the pre-trained language model, bert @xcite . @xcite proposed aveqa which formulates the attribute value extraction from products task as a multi-task approach via question answering. with the advancements in the field of language models, recent works by @xcite leverage large language models to extract attribute values from product data. they formulated the attribute value extraction as an instance of text infilling task as well as an answer generation task for which they utilized infilling by language modeling (ilm) @xcite for the infilling approach and fine-tuned text-to-text transfer transformer (t5) @xcite ) as an answer generation task. these models outperform the existing models but they fail to capture the intricate relations between different products. ## problem formulation we can formulate this problem of attribute value extraction as follows: given a product description, c, such that, @xmath0, c 2 , c 3 , . . . , c i } and an attribute a, the goal is to predict the corresponding attribute value t associated with a where @xmath1, t 2 , t 3 , . . . , t j }, where i and j are the number of unique source and target nodes respectively. we consider the nodes representing c as the source node and t as the target node. for constructing the graph, the product description, c, and the attribute values, t are arranged in a graph @xmath2, e) where v , the nodes represent c ∪ t . @xmath3, c, and the attribute values, t . we formulate our problem as a multi-label node classification task which also takes into consideration transitive relations between the nodes. this formulation allows more comprehensive correlations to be inferred. for example, from figure 2 , we can infer that titles c 1 and c 2 share a common value t 1 . if there is another value t 2 relevant to c 2 , it can be inferred that t 2 might be relevant to c 1 as well, i.e., one of the labels for c 1 could be t 2 . this formulation helps us improve the interpretability of the obtained results. ## discussions our model's success in capturing the essence of the dataset can be attributed to its interpretability. we have utilized the structured format of a graph to restructure the data, which aligns with the analysis capabilities of a graph neural network. unlike an llm, which predicts the next token in the same dataset, our graph neural network excels in analyzing structured data and making accurate predictions leveraging a graph's ability to learn from its neighborhood. by capturing the relationships between data points through the graph, our model has achieved significant levels of accuracy. moreover, our model's interpretability allows us to comprehend the rationale behind its predictions, which is crucial for maintaining its dependability and credibility. ## conclusion and future work in this work, we have proposed a novel approach to extract attribute values from unstructured product data with the help of graphical representation. representing the e-commerce data as graphs and leveraging graph techniques to extract the attribute values helped in understanding the underlying relationships between different products and forming transitive relations between products and their corresponding values. we plan on extending this work to build an advanced multi-hop model architecture that can make better predictions under diverse scenarios, including handling missing values, exploring strategies for imputing the most frequent values, and addressing other issues and datasets.