Probase

Wu, Wentao; Li, Hongsong; Wang, Haixun; Zhu, Kenny Q.

doi:10.1145/2213836.2213891

Cited by 606 publications

(60 citation statements)

References 31 publications

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“…Enormous, quite accurate hierarchies of this kind have been assembled through web mining; for instance Wu et al (2012) report that the Probase project had 2.6 million categories and 20.7 million isA pairs, with an accuracy of 92.8 percent.…”

Section: Taxonomymentioning

confidence: 99%

How to Write Science Questions That Are Easy for People and Hard for Computers

Davis

2016

AI Magazine

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As a challenge problem for AI systems, I propose the use of hand-constructed multiple-choice tests, with problems that are easy for people but hard for computers. Specifically, I discuss techniques for constructing such problems at the level of a fourth-grade child and at the level of a high-school student. For the fourth grade level questions, I argue that questions that require the understanding of time, impossible or pointless scenarios, of causality, of the human body, or of sets of objects, and questions that require combining facts or require simple inductive arguments of indeterminate length can be chosen to be easy for people, and are likely to be hard for AI programs, in the current state of the art. For the high-school level, I argue that questions that relate the formal science to the realia of laboratory experiments or of real-world observations are likely to be easy for people and hard for AI programs. I argue that these are more useful benchmarks than existing standardized tests such as the SATs or Regents tests. Since the questions in standardized tests are designed to be hard for people, they often leave many aspects of what is hard for computers but easy for people untested

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Section: Taxonomymentioning

confidence: 99%

How to Write Science Questions That Are Easy for People and Hard for Computers

Davis

2016

AI Magazine

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“…We introduce this data model and the most salient KB projects, which include KnowItAll [10,11], BabelNet [22], ConceptNet [28], DBpedia [3,18], DeepDive [24], Freebase [4], ImageNet [7], NELL [5], Wikidata [31], WikiNet [21], WikiTaxonomy [26], and YAGO [29,15]. We briefly discuss industrial projects like the Google Knowledge Graph and related work at Google [9,13,25], the EntityCube and Probase projects at Microsoft Research [23,32], and IBM's Watson project [17].…”

Section: Building Knowledge Basesmentioning

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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“…At present, there are many open source English knowledge base, including DBpeida [2], Freebase, Probase [3] and YAGO [4,5,6] etc. DBpeida is extracted from the structured information in Wikipedia and its associated data in the form of data sharing in Web [7].…”

Section: Introductionmentioning

confidence: 99%

The method of Constructing Chinese Knowledge Base based on open source English Knowledge Base

He¹,

Gong²,

Gan³

et al. 2016

Proceedings of the 2016 International Conference on Economics, Social Science, Arts, Education and Management Engineering

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Abstract. Building a large-scale knowledge base is a hot spot in the current research of big data, and it is the basis of Internet search, Question Answering (Q&A), Machine Translation (MT) etc. At present, it has formed serval mature English knowledge base, such as YAGO, Freebase, DBpedia, etc. but we still lack a large-scale Chinese knowledge base. This paper presents an approach to construct the Chinese knowledge base by using the automatic translation technique and the method of building the Chinese knowledge base with the source of English knowledge base. And proposing a quality calibration method based on the search engine. In order to verify the above method, we take the YAGO English knowledge base as an example, the formation of Chinese Knowledge most quality of knowledge is up to 95%, and a few low quality data can be discarded after being calibrated. The method is an important supplement to building a large-scale knowledge base by using text information extraction, crowdsourcing and other methods.

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Probase

Cited by 606 publications

References 31 publications

How to Write Science Questions That Are Easy for People and Hard for Computers

How to Write Science Questions That Are Easy for People and Hard for Computers

Knowledge bases in the age of big data analytics

The method of Constructing Chinese Knowledge Base based on open source English Knowledge Base

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