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
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Mining Rules Incrementally over Large Knowledge Bases
Comment Closed publications, research directions
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,
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Archimedes: Efficient Query Processing over Probabilistic Knowledge Bases
Comment Closed publications, research directions
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
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Extracting Visual Knowledge from the Web with Multimodal Learning
Comment Closed publications, research directions
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
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The ArchimedesOne Knowledge Base System
Comment Closed publications, research directions
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
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NIST 2015 Knowledge Base Population – Ensemble
Comment Closed NIST and open eval, research directions
Miguel Rodriguez The Text Analysis Conference (TAC) is a series of evaluation workshops organized by NIST to encourage research in Natural Language Processing and related applications. TAC is focused on Knowledge Base Population (KBP), automated systems that discover information about entities found in a large corpus and incorporate them into a knowledge base. The TAC-KBP evaluation is composed
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NIST 2015 Pre-Pilot Data Science Evaluation
Comment Closed courses, NIST and open eval, research directions
Dihong Gong and Daisy Zhe Wang We participated in the 2015 Pre-Pilot Data Science Evaluation organized by the National Institute of Standards and Technology (NIST). The primary goal of the pre-pilot evaluation is to develop and exercise the evaluation process in the context of data science. The evaluation consists of four tasks including data cleaning,
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SMART Electronic Discovery: System Evaluation
Comment Closed research directions
Clint P. George This is an extension to our earlier post, SMART Electronic Discovery (SMARTeR), which describes a framework for electronic discovery (e-discovery). Discovery is a pre-trial procedure in a lawsuit or legal investigation in which each party can obtain evidence from other parties (typically via a request for production) according to the laws of civil procedure in the
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Multimodal Ensemble Fusion for Disambiguation and Retrieval
Comment Closed publications, research directions
Yang Peng Huge amount of multimedia data is generated on the Internet everyday. In order to utilize multimodal data, researchers have developed a lot of multimodal machine learning models to integrate data of multiple modailities, including text, images, audios and videos. In the multimedia analysis community, multimodal fusion is greatly employed for various multimedia analysis
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MADlib now an ASF incubator project! Do you have MAD Skills?
Daisy Zhe Wang Comment Closed publications, research directions
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
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