Building, maintaining, and using knowledge bases

Deshpande, Omkar; Lamba, Digvijay S.; Tourn, Michel; Das, Sanjib; Subramaniam, Sri; Rajaraman, Anand; Harinarayan, Venky; Doan, AnHai

doi:10.1145/2463676.2465297

Cited by 67 publications

(42 citation statements)

References 20 publications

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“…Knowledge Graph has become a powerful tool for representing knowledge in the form of labelled digraphs and gives textual information semantics. Knowledge base contains a set of concepts, examples, and relationships [23]. In [24], Duan et al clarified the architecture of Knowledge Graph in terms of data, information, knowledge, and wisdom.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Processing Optimization of Typed Resources with Synchronized Storage and Computation Adaptation in Fog Computing

Song

Duan

Wan

et al. 2018

Wireless Communications and Mobile Computing

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Wide application of the Internet of Things (IoT) system has been increasingly demanding more hardware facilities for processing various resources including data, information, and knowledge. With the rapid growth of generated resource quantity, it is difficult to adapt to this situation by using traditional cloud computing models. Fog computing enables storage and computing services to perform at the edge of the network to extend cloud computing. However, there are some problems such as restricted computation, limited storage, and expensive network bandwidth in Fog computing applications. It is a challenge to balance the distribution of network resources. We propose a processing optimization mechanism of typed resources with synchronized storage and computation adaptation in Fog computing. In this mechanism, we process typed resources in a wireless-network-based threetier architecture consisting of Data Graph, Information Graph, and Knowledge Graph. The proposed mechanism aims to minimize processing cost over network, computation, and storage while maximizing the performance of processing in a business value driven manner. Simulation results show that the proposed approach improves the ratio of performance over user investment. Meanwhile, conversions between resource types deliver support for dynamically allocating network resources.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Processing Optimization of Typed Resources with Synchronized Storage and Computation Adaptation in Fog Computing

Song

Duan

Wan

et al. 2018

Wireless Communications and Mobile Computing

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“…Moreover, several KB's are interlinked at the entity level, forming the backbone of the Web of Linked Data [14]. Such world knowledge in turn enables cognitive applications and knowledge-centric services like disambiguating natural-language text, entity linking, text summarization, deep question answering, and semantic search and analytics over entities and relations in Web and enterprise data (e.g., [2,6,8,13]). Prominent examples of how KB's can be harnessed include the Google Knowledge Graph [27] and the IBM Watson question answering system [17].…”

Section: Motivation and Scopementioning

confidence: 99%

Knowledge bases in the age of big data analytics

Suchanek

Weikum

2014

Proc. VLDB Endow.

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This tutorial gives an overview on state-of-the-art methods for the automatic construction of large knowledge bases and harnessing them for data and text analytics. It covers both big-data methods for building knowledge bases and knowledge bases being assets for big-data applications. The tutorial also points out challenges and research opportunities. MOTIVATION AND SCOPEComphrehensive machine-readable knowledge bases (KB's) have been pursued since the seminal projects Cyc [19,20] and WordNet [12]. In contrast to these manually created KB's, great advances have recently been made on automating the building and curation of large KB's [1,16] This tutorial presents state-of-the-art methods, recent advances, research opportunities, and open challenges along this avenue of knowledge harvesting and its applications. Particular emphasis is on the twofold role of KB's for bigdata analytics: using scalable distributed algorithms for harvesting knowledge from Web and text sources, and leveraging entity-centric knowledge for deeper interpretation of and BUILDING KNOWLEDGE BASESDigital Knowledge: Today's KB's represent their data mostly in RDF-style SPO (subject-predicate-object) triples. We introduce this data model and the most salient KB projects, which include KnowItAll [10,11] Harvesting Knowledge on Entities and Classes: Every entity in a KB (e.g., Steve Jobs) belongs to one or multiple classes (e.g., computer pioneer, entrepreneur). These classes are organized into a taxonomy, where more special classes are subsumed by more general classes (e.g., person). We discuss two families of methods to harvest such information: Wikipedia-based approaches that analyze the category system, and Web-based approaches that use techniques like set expansion. HARVESTING FACTS AT WEB SCALEHarvesting Relational Facts: Relational facts express properties of and relationships between entities. There is a large spectrum of methods to extract such facts from Web documents. We give an overview on methods from pattern matching (e.g., regular expressions), computational linguistics (e.g., dependency parsing), statistical learning (e.g., factor graphs and MLN's), and logical consistency reasoning (e.g., weighted MaxSat or ILP solvers). We also discuss to what extent these approaches scale to handle big data.Open Information Extraction: Alternatively to methods that operate on a pre-specified set of relations and entities, open information extraction harvests arbitrary SPO triples from natural language documents. It aggressively taps into noun phrases as entity candidates and verbal phrases as prototypic patterns for relations. We discuss recent methods that follow this direction. Some methods along these lines make clever use of big-data techniques like frequent sequence mining and map-reduce computation.Temporal and Multilingual Knowledge: Properly interpreting entities and facts in a KB often requires additional meta-information like entity names in different languages and the temporal scope of facts. We discuss tech-1713

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“…Knowledge base construction: There is also work that describes the challenges incorporating and evaluating the human contributions in a knowledge base [2,17,36,11,27,21]. For sites like Wikipedia, a challenge is to even measure the quality of contributions; longevity of the contributions is typically a good proxy for a high-quality contribution.…”

Section: Related Workmentioning

confidence: 99%

Trust, but verify

Tan

Agichtein

Ipeirotis

et al. 2014

Proceedings of the 7th ACM International Conference on Web Search and Data Mining

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The largest publicly available knowledge repositories, such as Wikipedia and Freebase, owe their existence and growth to volunteer contributors around the globe. While the majority of contributions are correct, errors can still creep in, due to editors' carelessness, misunderstanding of the schema, malice, or even lack of accepted ground truth. If left undetected, inaccuracies often degrade the experience of users and the performance of applications that rely on these knowledge repositories. We present a new method, CQUAL, for automatically predicting the quality of contributions submitted to a knowledge base. Significantly expanding upon previous work, our method holistically exploits a variety of signals, including the user's domains of expertise as reflected in her prior contribution history, and the historical accuracy rates of different types of facts. In a large-scale human evaluation, our method exhibits precision of 91% at 80% recall. Our model verifies whether a contribution is correct immediately after it is submitted, significantly alleviating the need for post-submission human reviewing.

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Building, maintaining, and using knowledge bases

Cited by 67 publications

References 20 publications

Processing Optimization of Typed Resources with Synchronized Storage and Computation Adaptation in Fog Computing

Processing Optimization of Typed Resources with Synchronized Storage and Computation Adaptation in Fog Computing

Knowledge bases in the age of big data analytics

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