Users on the internet usually have conversations on interesting facts or topics along with diverse knowledge from the web. However, most existing knowledge-grounded conversation models consider only a single document regarding the topic of a conversation. The recently proposed retrieval-augmented models generate a response based on multiple documents; however, they ignore the given topic and use only the local context of the conversation. To this end, we introduce a novel retrieval-augmented response generation model that retrieves an appropriate range of documents relevant to both the topic and local context of a conversation and uses them for generating a knowledge-grounded response. Our model first accepts both topic words extracted from the whole conversation and the tokens before the response to yield multiple representations. It then chooses representations of the first N token and ones of keywords from the conversation and document encoders and compares the two groups of representation from the conversation with those groups of the document, respectively. For training, we introduce a new data-weighting scheme to encourage the model to produce knowledge-grounded responses without ground truth knowledge. Both automatic and human evaluation results with a large-scale dataset show that our models can generate more knowledgeable, diverse, and relevant responses compared to the state-of-the-art models.

An ontology-based product-recommender system can help catalog administrators in B2B marketplaces maintain up-to-date product databases by acquiring mapping information between the new product data and existing data. The proposed approach is keyword-based and independent of the underlying physical structure of product ontology. With a Bayesian belief network as its basis, the ranking algorithm utilizes semantics embedded within relationships defined in ontology to probabilistically determine the ranking scores. The methodology is implemented on a practical ontology system powerful enough to assist users in B2B marketplaces. Its effectiveness is demonstrated in comparison to the conventional search engines.

The response time of DSS (Decision Support System) queries is typically several orders of magnitude higher than the response time of OLTP (OnLine Transaction Processing) queries. Since DSS queries are often submitted interactively, techniques for reducing their response time are becoming increasingly important. We argue that caching of query results is one such technique particularly well suited to the DSS environment. We have designed a query cache manager for such an environment. The cache manager can lookup query results from the cache either based on an exact query match or using a query split algorithm to efficiently find query results which subsume the submitted query. The cache manager dynamically maintains the cache content by deciding whether a newly generated query result should be admitted to the cache and if so, which query results should be evicted from the cache to free space for the new query result. The decisions are aimed at minimizing the query response time. The decisions are explicitly based on a cost function that considers the execution cost of each query, the size of each query result, the reference frequency to each result, the cost of maintenance of each result due to updates of the base tables, and the frequency of such updates. Experimental evaluation shows that our cache manager can improve performance on TPC-D like workloads.

The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed to safeguard healthcare data. However, these techniques are inadequate for genomic data management because of their large size. Additionally, privacy problems due to the sharing of gene data are yet to be addressed. In this study, we propose a secure genomic data management system using blockchain and local differential privacy (LDP). The proposed system employs two types of storage: private storage for internal staff and semi-private storage for external users. In private storage, because encrypted gene data are stored, only internal employees can access the data. Meanwhile, in semi-private storage, gene data are irreversibly modified by LDP. Through LDP, different noises are added to each section of the genomic data. Therefore, even though the third party uses or exposes the shared data, the owner’s privacy is guaranteed. Furthermore, the access control for each storage is ensured by the blockchain, and the gene owner can trace the usage and sharing status using a decentralized application in a mobile device.