2,161 research outputs found
Large-scale linked data integration using probabilistic reasoning and crowdsourcing
We tackle the problems of semiautomatically matching linked data sets and of linking large collections of Web pages to linked data. Our system, ZenCrowd, (1) uses a three-stage blocking technique in order to obtain the best possible instance matches while minimizing both computational complexity and latency, and (2) identifies entities from natural language text using state-of-the-art techniques and automatically connects them to the linked open data cloud. First, we use structured inverted indices to quickly find potential candidate results from entities that have been indexed in our system. Our system then analyzes the candidate matches and refines them whenever deemed necessary using computationally more expensive queries on a graph database. Finally, we resort to human computation by dynamically generating crowdsourcing tasks in case the algorithmic components fail to come up with convincing results. We integrate all results from the inverted indices, from the graph database and from the crowd using a probabilistic framework in order to make sensible decisions about candidate matches and to identify unreliable human workers. In the following, we give an overview of the architecture of our system and describe in detail our novel three-stage blocking technique and our probabilistic decision framework. We also report on a series of experimental results on a standard data set, showing that our system can achieve a 95% average accuracy on instance matching (as compared to the initial 88% average accuracy of the purely automatic baseline) while drastically limiting the amount of work performed by the crowd. The experimental evaluation of our system on the entity linking task shows an average relative improvement of 14% over our best automatic approac
Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems
Crowdsourcing systems commonly face the problem of aggregating multiple
judgments provided by potentially unreliable workers. In addition, several
aspects of the design of efficient crowdsourcing processes, such as defining
worker's bonuses, fair prices and time limits of the tasks, involve knowledge
of the likely duration of the task at hand. Bringing this together, in this
work we introduce a new time--sensitive Bayesian aggregation method that
simultaneously estimates a task's duration and obtains reliable aggregations of
crowdsourced judgments. Our method, called BCCTime, builds on the key insight
that the time taken by a worker to perform a task is an important indicator of
the likely quality of the produced judgment. To capture this, BCCTime uses
latent variables to represent the uncertainty about the workers' completion
time, the tasks' duration and the workers' accuracy. To relate the quality of a
judgment to the time a worker spends on a task, our model assumes that each
task is completed within a latent time window within which all workers with a
propensity to genuinely attempt the labelling task (i.e., no spammers) are
expected to submit their judgments. In contrast, workers with a lower
propensity to valid labeling, such as spammers, bots or lazy labelers, are
assumed to perform tasks considerably faster or slower than the time required
by normal workers. Specifically, we use efficient message-passing Bayesian
inference to learn approximate posterior probabilities of (i) the confusion
matrix of each worker, (ii) the propensity to valid labeling of each worker,
(iii) the unbiased duration of each task and (iv) the true label of each task.
Using two real-world public datasets for entity linking tasks, we show that
BCCTime produces up to 11% more accurate classifications and up to 100% more
informative estimates of a task's duration compared to state-of-the-art
methods
A survey on the development status and application prospects of knowledge graph in smart grids
With the advent of the electric power big data era, semantic interoperability
and interconnection of power data have received extensive attention. Knowledge
graph technology is a new method describing the complex relationships between
concepts and entities in the objective world, which is widely concerned because
of its robust knowledge inference ability. Especially with the proliferation of
measurement devices and exponential growth of electric power data empowers,
electric power knowledge graph provides new opportunities to solve the
contradictions between the massive power resources and the continuously
increasing demands for intelligent applications. In an attempt to fulfil the
potential of knowledge graph and deal with the various challenges faced, as
well as to obtain insights to achieve business applications of smart grids,
this work first presents a holistic study of knowledge-driven intelligent
application integration. Specifically, a detailed overview of electric power
knowledge mining is provided. Then, the overview of the knowledge graph in
smart grids is introduced. Moreover, the architecture of the big knowledge
graph platform for smart grids and critical technologies are described.
Furthermore, this paper comprehensively elaborates on the application prospects
leveraged by knowledge graph oriented to smart grids, power consumer service,
decision-making in dispatching, and operation and maintenance of power
equipment. Finally, issues and challenges are summarised.Comment: IET Generation, Transmission & Distributio
Using microtasks to crowdsource DBpedia entity classification: A study in workflow design
DBpedia is at the core of the Linked Open Data Cloud and widely used in research and applications. However, it is far from being perfect. Its content suffers from many flaws, as a result of factual errors inherited from Wikipedia or incomplete mappings from Wikipedia infobox to DBpedia ontology. In this work we focus on one class of such problems, un-typed entities. We propose a hierarchical tree-based approach to categorize DBpedia entities according to the DBpedia ontology using human computation and paid microtasks. We analyse the main dimensions of the crowdsourcing exercise in depth in order to come up with suggestions for workflow design and study three different workflows with automatic and hybrid prediction mechanisms to select possible candidates for the most specific category from the DBpedia ontology. To test our approach, we run experiments on CrowdFlower using a gold standard dataset of 120 previously unclassified entities. In our studies human-computation driven approaches generally achieved higher precision at lower cost when compared to workflows with automatic predictors. However, each of the tested workflows has its merit and none of them seems to perform exceptionally well on the entities that the DBpedia Extraction Framework fails to classify. We discuss these findings and their potential implications for the design of effective crowdsourced entity classification in DBpedia and beyond
Knowledge-based Biomedical Data Science 2019
Knowledge-based biomedical data science (KBDS) involves the design and
implementation of computer systems that act as if they knew about biomedicine.
Such systems depend on formally represented knowledge in computer systems,
often in the form of knowledge graphs. Here we survey the progress in the last
year in systems that use formally represented knowledge to address data science
problems in both clinical and biological domains, as well as on approaches for
creating knowledge graphs. Major themes include the relationships between
knowledge graphs and machine learning, the use of natural language processing,
and the expansion of knowledge-based approaches to novel domains, such as
Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages
with 3 table
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