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As part of an insect resistance management plan to preserve Bt transgenic technology, annual monitoring of target pests is mandated to detect susceptibility changes to Bt toxins. Currently Helicoverpa zea (Boddie) monitoring involves investigating unexpected injury in Bt crop fields and collecting larvae from non-Bt host plants for laboratory diet bioassays to determine mortality responses to diagnostic concentrations of Bt toxins. To date, this monitoring approach has not detected any significant change from the known range of baseline susceptibility to Bt toxins, yet practical field-evolved resistance in H. zea populations and numerous occurrences of unexpected injury occur in Bt crops. In this study, we implemented a network of 73 sentinel sweet corn trials, spanning 16 U.S. states and 4 Canadian provinces, for monitoring changes in H. zea susceptibility to Cry and Vip3A toxins by measuring differences in ear damage and larval infestations between isogenic pairs of non-Bt and Bt hybrids over three years. This approach can monitor susceptibility changes and regional differences in other ear-feeding lepidopteran pests. Temporal changes in the field efficacy of each toxin were evidenced by comparing our current results with earlier published studies, including baseline data for each Bt trait when first commercialized. Changes in amount of ear damage showed significant increases in H. zea resistance to Cry toxins and possibly lower susceptibility to Vip3a. Our findings demonstrate that the sentinel plot approach as an in-field screen can effectively monitor phenotypic resistance and document field-evolved resistance in target pest populations, improving resistance monitoring for Bt crops. Accepted version

Typically crowdsourcing-based approaches to gather annotated data use inter-annotator agreement as a measure of quality. However, in many domains, there is ambiguity in the data, as well as a multitude of perspectives of the information examples. In this paper, we present ongoing work into the CrowdTruth metrics, that capture and interpret inter-annotator disagreement in crowdsourcing. The CrowdTruth metrics model the inter-dependency between the three main components of a crowdsourcing system – worker, input data, and annotation. The goal of the metrics is to capture the degree of ambiguity in each of these three components. The metrics are available online at https://github.com/CrowdTruth/CrowdTruth-core.

Field tests in 2010-2011 were performed in New York, Minnesota, Maryland, Ohio, and Georgia to compare Bt sweet corn lines expressing Cry1A.105 + Cry2Ab2 and Cry1Ab with their non-Bt isolines, with and without the use of foliar insecticides. The primary insect pest in all locations during the trial years was Heliocoverpa zea (Boddie), which is becoming the most serious insect pest of sweet corn in the United States. At harvest, the ears were measured for marketability according to fresh market and processing standards. For fresh market and processing, least squares regression showed significant effects of protein expression, state, and insecticide frequency. There was a significant effect of year for fresh market but not for processing. The model also showed significant effects of H. zea per ear by protein expression. Sweet corn containing two genes (Cry1A.105 + Cry2Ab2) and a single gene (Cry1Ab) provided high marketability, and both Bt varieties significantly outperformed the traditional non-Bt isolines in nearly all cases regardless of insecticide application frequency. For pest suppression of H. zea, plants expressing Bt proteins consistently performed better than non-Bt isoline plants, even those sprayed at conventional insecticide frequencies. Where comparisons in the same state were made between Cry1A.105 + Cry2Ab2 and Cry1Ab plants for fresh market, the product expressing Cry1A.105 + Cry2Ab2 provided better protection and resulted in less variability in control. Overall, these results indicate Cry1A.105 + Cry2Ab2 and Cry1Ab plants are suitable for fresh market and processing corn production across a diversity of growing regions and years. Our results demonstrate that Bt sweet corn has the potential to significantly reduce the use of conventional insecticides against lepidopteran pests and, in turn, reduce occupational and environmental risks that arise from intensive insecticide use

A widespread use of linked data for information extraction is distant supervision, in which relation tuples from a data source are found in sentences in a text corpus, and those sentences are treated as training data for relation extraction systems. Distant supervision is a cheap way to acquire training data, but that data can be quite noisy, which limits the performance of a system trained with it. Human annotators can be used to clean the data, but in some domains, such as medical NLP, it is widely believed that only medical experts can do this reliably. We have been investigating the use of crowdsourcing as an affordable alternative to using experts to clean noisy data, and have found that with the proper analysis, crowds can rival and even out-perform the precision and recall of experts, at a much lower cost. We have further found that the crowd, by virtue of its diversity, can help us find evidence of ambiguous sen-tences that are difficult to classify, and we have hypothesized that such sentences are likely just as difficult for machines to classify. In this pa-per we outline CrowdTruth, a previously presented method for scoring ambiguous sentences that suggests that existing modes of truth are in-adequate, and we present for the first time a set of weighted metrics for evaluating the performance of experts, the crowd, and a trained classiffier in light of ambiguity. We show that our theory of truth and our metrics are a more powerful way to evaluate NLP performance over traditional unweighted metrics like precision and recall, because they allow us to ac-count for the rather obvious fact that some sentences express the target relations more clearly than others.

More and more websites embed structured data describing for instance products, reviews, blog posts, people, organizations, events, and cooking recipes into their HTML pages using markup standards such as Microformats, Microdata and RDFa. This development has accelerated in the last two years as major Web companies, such as Google, Facebook, Yahoo!, and Microsoft, have started to use the embedded data within their applications. In this paper, we analyze the adoption of RDFa, Microdata, and Microformats across the Web. Our study is based on a large public Web crawl dating from early 2012 and consisting of 3 billion HTML pages which originate from over 40 million websites. The analysis reveals the deployment of the different markup standards, the main topical areas of the published data as well as the different vocabularies that are used within each topical area to represent data. What distinguishes our work from earlier studies, published by the large Web companies, is that the analyzed crawl as well as the extracted data are publicly available. This allows our findings to be verified and to be used as starting points for further domain-specific investigations as well as for focused information extraction endeavors.

The Linked Data cloud has grown to become the largest knowledge base ever constructed. Its size is now turning into a major bottleneck for many applications. In order to facilitate access to this structured information, this paper proposes an automatic sampling method targeted at maximizing answer coverage for applications using SPARQL querying. The approach presented in this paper is novel: no similar RDF sampling approach exist. Additionally, the concept of creating a sample aimed at maximizing SPARQL answer coverage, is unique. We empirically show that the relevance of triples for sampling (a semantic notion) is influenced by the topology of the graph (purely structural), and can be determined without prior knowledge of the queries. Experiments show a significantly higher recall of topology based sampling methods over ran- dom and naive baseline approaches (e.g. up to 90% for Open-BioMed at a sample size of 6%).

When are two entries about a small molecule in different datasets the same? If they have the same drug name, chemical structure, or some other criteria? The choice depends upon the application to which the data will be put. However, existing Linked Data approaches provide a single global view over the data with no way of varying the notion of equivalence to be applied. In this paper, we present an approach to enable applications to choose the equivalence criteria to apply between datasets. Thus, supporting multiple dynamic views over the Linked Data. For chemical data, we show that multiple sets of links can be automatically generated according to different equivalence criteria and published with semantic descriptions capturing their context and interpretation. This approach has been applied within a large scale public-private data integration platform for drug discovery. To cater for different use cases, the platform allows the application of different lenses which vary the equivalence rules to be applied based on the context and interpretation of the links.

We describe an optimised consequence-based procedure for classification of ontologies expressed in a polynomial fragment ℰℒℋ ℛ+ of the OWL 2 EL profile. A distinguishing property of our procedure is that it can take advantage of multiple processors/cores, which increasingly prevail in computer systems. Our solution is based on a variant of the 'given clause' saturation algorithm for first-order theorem proving, where we assign derived axioms to 'contexts' within which they can be used and which can be processed independently. We describe an implementation of our procedure within the Java-based reasoner ELK. Our implementation is light-weight in the sense that an overhead of managing concurrent computations is minimal. This is achieved by employing lock-free data structures and operations such as 'compare-and-swap'. We report on preliminary experimental results demonstrating a substantial speedup of ontology classification on multi-core systems. In particular, one of the largest and widely-used medical ontologies SNOMED CT can be classified in as little as 5 seconds. © 2011 Springer-Verlag.

This paper introduces the MetaLex Document Server (MDS), an ongoing project to improve access to legal sources (regulations, court rulings) by means of a generic legal XML syntax (CEN MetaLex) and Linked Data. The MDS defines a generic conversion mechanism from legacy legal XML syntaxes to CEN MetaLex, RDF and Pajek network files, and discloses content by means of HTTP-based content negotiation, a SPARQL endpoint and a basic search interface. MDS combines a transparent (versioned) and opaque (content-based) naming scheme for URIs of parts of legal texts, allowing for tracking of version information at the URI-level, as well as reverse engineering of versioned metadata from sources that provide only partial information, such as many web-based legal content services. The MDS hosts all 28k national regulations of the Netherlands available since May 2011, comprising some 100M triples.

When ontological knowledge is acquired automatically, quality control is essential. We consider the tightest possible approach - an exhaustive manual inspection of the acquired data. By using automated reasoning, we partially automate the process: after each expert decision, axioms that are entailed by the already approved statements are automatically approved, whereas axioms that would lead to an inconsistency are declined. Adequate axiom ranking strategies are essential in this setting to minimize the amount of expert decisions. In this paper, we present a generalization of the previously proposed ranking techniques which works well for arbitrary validity ratios - the proportion of valid statements within a dataset - whereas the previously described ranking functions were either tailored towards validity ratios of exactly 100% and 0% or were optimizing the worst case. The validity ratio - generally not known a priori - is continuously estimated over the course of the inspection process. We further employ partitioning techniques to significantly reduce the computational effort. We provide an implementation supporting all these optimizations as well as featuring a user front-end for successive axiom evaluation, thereby making our proposed strategy applicable to practical scenarios. This is witnessed by our evaluation showing that the novel parameterized ranking function almost achieves the maximum possible automation and that the computation time needed for each reasoning-based, automatic decision is reduced to less than one second on average for our test dataset of over 25,000 statements. © 2011 Springer-Verlag.

The need for scalable query answering often forces Semantic Web applications to use incomplete OWL 2 reasoners, which in some cases fail to derive all answers to a query. This is clearly undesirable, and in some applications may even be unacceptable. To address this problem, we investigate the problem of 'repairing' an ontology T - that is, computing an ontology R such that a reasoner that is incomplete for T becomes complete when used with T ∪ R. We identify conditions on T and the reasoner that make this possible, present a practical algorithm for computing R, and present a preliminary evaluation which shows that, in some realistic cases, repairs are feasible to compute, reasonable in size, and do not significantly affect reasoner performance. © 2011 Springer-Verlag.

The process of building ontologies for use in information systems remains an arcane art form, which mustbecome a rigorous engineering discipline. One of the requirements for developing a true ontology engineering practice is a general, domain-independent methodology that provides guidance not only on what kinds of ontological decisions need to be made, buyt on how these decisions can be evaluated. The OntoClean methodology seeks to provide this kind of guidance based on highly general ontological notions drawn from philosophical ontology, especially what is now called "analytic metaphysics." Among other things, we use our methodology to validate taxonomies by exposing inappropriate and inconsistent modeling choices.

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