The color of the products is one of the most prevalent aspects in many e-commerce domains, and it is one of the decisive purchasing factors. Besides having thousands of color variations and shades, many brands continuously develop proprietary colors and color names to attract more customers. This often leads to color ambiguity (textual and visual), and vocabulary mismatch between buyers and sellers. Therefore, it is crucial for any e-commerce search engine to correctly identify the buyer's color intent and match it to the corresponding product listings. To address this challenge, in this work, we introduce a color query expansion approach using color Knowledge Graphs. We use Knowledge Graphs to unambiguously identify all the colors based on their properties, and the relationships to other colors, which allows us to perform semantic query expansion. Similar expansion concepts could be applied to domains outside of color.

The Fashion domain is one of the most profitable domains in most of the e-commerce shops, shoes being one of the top-selling categories within this domain. When shopping for shoes, one of the most important aspects for the buyers is the shoe size. Shoe size charts differ between different brands, geographical regions, genders and age groups. Not providing some of these details, as a buyer or a seller, could lead to a query intent to inventory mismatch and reduced or wrong search results. Furthermore, buying the wrong shoe size is one of the top reasons for product returns, which causes shipping delays and loss in revenue. To address this issue, we propose an approach for shoe size resolution and normalization in search queries and product listings using Knowledge Graphs.

Entity linking is a fundamental task for a successful use of knowledge graphs in many information systems. It maps textual mentions to their corresponding entities in a given knowledge graph. However, with the rapid evolution of knowledge graphs, a large number of entities is continuously added over time. Performing entity linking on new, or unseen, entities poses a great challenge, as standard entity linking approaches require large amounts of labeled data for all new entities, and the underlying model must be regularly updated. To address this challenge, several zero-shot entity linking approaches have been proposed, which don't require additional labeled data to perform entity linking over unseen entities and new domains. Most of these approaches use large language models, such as BERT, to encode the textual description of the mentions and entities in a common embedding space, which allows linking mentions to unseen entities. While such approaches have shown good performance, one big drawback is that they are not able to exploit the entity symbolic information from the knowledge graph, such as entity types, relations, popularity scores and graph embeddings. In this paper, we present KG-ZESHEL, a knowledge graph-enhanced zero-shot entity linking approach, which extends an existing BERT-based zero-shot entity linking approach with mention and entity auxiliary information. Experiments on two benchmark entity linking datasets, show that our proposed approach outperforms the related BERT-based state-of-the-art entity linking models.

Emerging Big Data applications in areas like ecommerce and energy industry require both online and on-demand queries to be performed over vast and fast data arriving as streams. These present novel challenges to Big Data management systems. Complex Event Processing (CEP) is recognized as a high performance online query scheme which in particular deals with the velocity aspect of the 3-V's of Big Data. However, traditional CEP systems do not consider data variety and lack the capability to embed ad hoc queries over the volume of data streams. In this paper, we propose H2O, a stateful complex event processing framework, to support hybrid online and on-demand queries over realtime data. We propose a semantically enriched event and query model to address data variety. A formal query algebra is developed to precisely capture the stateful and containment semantics of online and on-demand queries. We describe techniques to achieve the interactive query processing over realtime data featured by efficient online querying, dynamic stream data persistence and on-demand access. The system architecture is presented and the current implementation status reported.

A proper tire friction model is essential to model overall vehicle dynamics for simulation, analysis, or control purposes since a ground vehicle's motion is primarily determined by the friction forces transferred from roads via tires. Motivated by the developments of high-performance antilock brake systems (ABSs), traction control, and steering systems, significant research efforts had been put into tire/road friction modeling during the past 40 years. In this paper, a review of recent developments and trends in this area is presented, with attempts to provide a broad perspective of the initiatives and multidisciplinary techniques for related research. Different longitudinal, lateral, and integrated tire/road friction models are examined. The associated friction-situation monitoring and control synthesis are discussed with a special emphasis on ABS design