Most of the existing techniques to product discovery rely on syntactic approaches, thus ignoring valuable and specific semantic information of the underlying standards during the process. The product data comes from different heterogeneous sources and formats giving rise to the problem of interoperability. Above all, due to the continuously increasing influx of data, the manual labeling is getting costlier. Integrating the descriptions of different products into a single representation requires organizing all the products across vendors in a single taxonomy. Practically relevant and quality product categorization standards are still limited in number; and that too in academic research projects where we can majorly see only prototypes as compared to industry. This work presents a cost-effective solution for e-commerce on the Data Web by employing an unsupervised approach for data classification and exploiting the knowledge graphs for matching. The proposed architecture describes available products in web ontology language OWL and stores them in a triple store. User input specifications for certain products are matched against the available product categories to generate a knowledge graph. This mullti-phased top-down approach to develop and improve existing, if any, tailored product recommendations will be able to connect users with the exact product/service of their choice.

中文翻译：

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利用知识图谱促进产品/服务发现

大多数现有的产品发现技术依赖于句法方法，从而在过程中忽略了底层标准的有价值和特定的语义信息。产品数据来自不同的异构来源和格式，导致互操作性问题。最重要的是，由于数据的不断涌入，手动标记的成本越来越高。将不同产品的描述集成到单个表示中需要在单个分类法中组织跨供应商的所有产品。实际相关和优质的产品分类标准数量仍然有限；在学术研究项目中也是如此，与工业相比，我们主要只能看到原型。这项工作通过采用无监督方法进行数据分类并利用知识图进行匹配，为数据 Web 上的电子商务提供了一种经济高效的解决方案。提议的架构用网络本体语言 OWL 描述可用产品，并将它们存储在三元组中。某些产品的用户输入规范与可用的产品类别相匹配以生成知识图。这种用于开发和改进现有（如果有的话）定制产品推荐的多阶段自上而下的方法将能够将用户与他们选择的确切产品/服务联系起来。某些产品的用户输入规范与可用的产品类别相匹配以生成知识图。这种用于开发和改进现有（如果有的话）定制产品推荐的多阶段自上而下的方法将能够将用户与他们选择的确切产品/服务联系起来。某些产品的用户输入规范与可用的产品类别相匹配以生成知识图。这种用于开发和改进现有（如果有的话）定制产品推荐的多阶段自上而下的方法将能够将用户与他们选择的确切产品/服务联系起来。