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WebPIE: A Web-scale parallel inference engine using
"... The large amount of Semantic Web data and its fast growth pose a significant computational challenge in performing efficient and scalable reasoning. On a large scale, the resources of single machines are no longer sufficient and we are required to distribute the process to improve performance. In th ..."
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through a set of algorithms which, combined, significantly increase performance. We have implemented WebPIE (Web-scale Inference En-gine) and we demonstrate its performance on a cluster of up to 64 nodes. We have evaluated our system using very large real-world datasets (Bio2RDF, LLD, LDSR) and the LUBM
Web-scale taxonomy learning
- Proceedings of Workshop on Extending and Learning Lexical Ontologies using Machine Learning, ICML05
, 2005
"... In this paper, we propose an automatic and unsupervised methodology to obtain taxonomies of terms from the Web and represent retrieved web sites into a meaningful organization for a desired domain without previous knowledge. It is based on the intensive use of web search engines to retrieve domain s ..."
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Cited by 7 (0 self)
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suitable resources from which extract knowledge, and to obtain web scale statistics from which infer knowledge relevancy. Results can be useful for easing the access to the web resources or as the first step for constructing ontologies suitable for the Semantic Web. 1.
ConceptNet: A Practical Commonsense Reasoning Toolkit
- BT TECHNOLOGY JOURNAL
, 2004
"... ConceptNet is a freely available commonsense knowledgebase and natural-language-processing toolkit which supports many practical textual-reasoning tasks over real-world documents including topic-jisting (e.g. a news article containing the concepts, "gun," "convenience store," &qu ..."
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Cited by 343 (7 self)
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, temporal, and psychological aspects of everyday life. Whereas similar large-scale semantic knowledgebases like Cyc and WordNet are carefully handcrafted, ConceptNet is generated automatically from the 700,000 sentences of the Open Mind Common Sense Project -- a World Wide Web based collaboration with over
Web-scale information extraction with vertex
- In ICDE
, 2011
"... Abstract Vertex is a Wrapper Induction system developed at Yahoo! for extracting structured records from template-based Web pages. To operate at Web scale, Vertex employs a host of novel algorithms for (1) Grouping similar structured pages in a Web site, (2) Picking the appropriate sample pages for ..."
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Cited by 10 (0 self)
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Abstract Vertex is a Wrapper Induction system developed at Yahoo! for extracting structured records from template-based Web pages. To operate at Web scale, Vertex employs a host of novel algorithms for (1) Grouping similar structured pages in a Web site, (2) Picking the appropriate sample pages
H.: WebPIE: A Web-Scale Parallel Inference Engine
- In: Third IEEE International Scalable Computing Challenge (SCALE2010), held in conjunction with the 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid
, 2010
"... The Semantic Web [1] extends the World Wide Web by providing well-defined semantics to information and services. Through these semantics machines can “understand ” the Web, making it possible to query and reason over Web information, treating the Web as if it were a giant semi-structured database. ..."
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Cited by 32 (5 self)
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The Semantic Web [1] extends the World Wide Web by providing well-defined semantics to information and services. Through these semantics machines can “understand ” the Web, making it possible to query and reason over Web information, treating the Web as if it were a giant semi-structured database.
WebPIE: A Web-scale parallel inference engine using
"... The large amount of Semantic Web data and its fast growth pose a significant computational challenge in performing efficient and scalable reasoning. On a large scale, the resources of single machines are no longer sufficient and we are required to distribute the process to improve performance. In th ..."
Abstract
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through a set of algorithms which, combined, significantly increase performance. We have implemented WebPIE (Web-scale Inference Engine) and we demonstrate its performance on a cluster of up to 64 nodes. We have evaluated our system using very large real-world datasets (Bio2RDF, LLD, LDSR) and the LUBM
Knowledge Vault: A Web-scale approach to probabilistic knowledge fusion
- In submission
, 2014
"... Recent years have witnessed a proliferation of large-scale knowledge bases, including Wikipedia, Freebase, YAGO, Mi-crosoft’s Satori, and Google’s Knowledge Graph. To in-crease the scale even further, we need to explore automatic methods for constructing knowledge bases. Previous ap-proaches have pr ..."
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Cited by 49 (6 self)
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primarily focused on text-based extraction, which can be very noisy. Here we introduce Knowledge Vault, a Web-scale probabilistic knowledge base that com-bines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from
Global and regional climate changes due to black carbon,
- Nat. Geosci.,
, 2008
"... Figure 1: Global distribution of BC sources and radiative forcing. a, BC emission strength in tons per year from a study by Bond et al. Full size image (42 KB) Review Nature Geoscience 1, 221 -227 (2008 Black carbon in soot is the dominant absorber of visible solar radiation in the atmosphere. Ant ..."
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Cited by 228 (5 self)
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. The uncertainty in the published estimates for BC emissions is a factor of two to five on regional scales and at least 50% on global scales. High BC emissions ( Regional hotspots Until about the 1950s, North America and Western Europe were the major sources of soot emissions, but now developing nations
DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference
"... We present an end-to-end (live) demonstration system called DeepDive that performs knowledge-base construction (KBC) from hundreds of millions of web pages. DeepDive employs statistical learning and inference to combine diverse data resources and best-of-breed algorithms. A key challenge of this app ..."
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Cited by 17 (1 self)
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of this approach is scalability, i.e., how to deal with terabytes of imperfect data efficiently. We describe how we address the scalability challenges to achieve web-scale KBC and the lessons we have learned from building DeepDive. 1.
Web-Scale Multi-Task Feature Selection for Behavioral Targeting
"... A typical behavioral targeting system optimizing purchase activities, called conversions, faces two main challenges: the web-scale amounts of user histories to process on a daily basis, and the relative sparsity of conversions. In this paper, we try to address these challenges through feature select ..."
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Cited by 2 (1 self)
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for distributed parameter estimation. Our algorithm relies on a variant of the well known Fast Iterative Thresholding Algorithm (FISTA), a closed-form solution for mixed norm programming and a distributed subgradient oracle. To efficiently handle web-scale user histories, we present a distributed inference
Results 1 - 10
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328
