Current web-scale knowledge bases (KBs) incorporate a substantial amount of information in a structured format. Availability of this readily machine-digestible data has made KBs a desirable resource for other applications. This has motivated many to explore learning on KBs. Graph embeddings and inference rule learning are examples such methods. This paper concerns the later, mostly because

Xiaofeng Zhou Multiple web-scale knowledge bases (e.g., Freebase, YAGO, NELL) have been constructed using semi-supervised or unsupervised information extraction techniques and many of them, despite their large sizes, are continuously growing. Much research effort has been put into mining inference rules from these knowledge bases. To address the task of rule mining over evolving web-scale knowledge bases,

We present the ARCHIMEDES system for efficient query processing over probabilistic knowledge bases. We design ARCHIMEDES for knowledge bases containing incomplete and uncertain information due to limitations of information sources and human knowledge. Answering queries over these knowledge bases requires efficient probabilistic inference. In this paper, we describe ARCHIMEDES’s efficient knowledge expansion and query-driven inference over UDA-GIST, an

We consider the problem of automatically extracting visual objects from web images. Despite the extraordinary advancement in deep learning, visual object detection remains a challenging task. To overcome the deficiency of pure visual techniques, we propose to make use of meta text surrounding images on the Web for enhanced detection accuracy. In this work we present

Yang Chen, Xiaofeng Zhou Recent development in information extraction and data management systems arouses elevating efforts in constructing large knowledge bases (KBs). These knowledge bases store information in a structured format, facilitating efficient processing and querying. Examples of these knowledge bases include DBpedia, DeepDive, Freebase, Google Knowledge Graph, Knowledge Vault, NELL, OpenIE, ProBase, ProbKB, and

Daisy Zhe Wang MADlib is an open-source library (licensed under 2-clause BSD license) for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical and machine learning methods for structured and unstructured data. The MADlib mission is to foster widespread development of scalable analytic skills, by harnessing efforts from commercial practice, academic research, and open

Christan Grant One of the research theme of the UF Data Science Research lab is to adapt machine learning algorithm to real-time interactive query engines. Our prior work looked at implementing and optimizing information extraction and statistical machine learning operations in exploratory query processing systems. To push the envelop further, our newly completed paper (under-submission)