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If you are looking for solutions that allow you not only to manage all of your data (structured, semi-structured and unstructured) but to also make the most out of them, using a common language is critical. Adding Semantic Technology to data integration is the glue that holds together all your enterprise data and their relationships in a meaningful way. Learn how you can quickly design data processing jobs and integrate massive amounts of data and see what semantic integration can do for your data and your business. www.ontotext.com

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Despite an improved digital access to scientific publications in the last decades, the fundamental principles of scholarly communication remain unchanged and continue to be largely document-based. The document-oriented workflows in science have reached the limits of adequacy as highlighted by recent discussions on the increasing proliferation of scientific literature, the deficiency of peer-review and the reproducibility crisis. We need to represent, analyse, augment and exploit scholarly communication in a knowledge-based way by expressing and linking scientific contributions and related artefacts through semantically rich, interlinked knowledge graphs. This should be based on deep semantic representation of scientific contributions, their manual, crowd-sourced and automatic augmentation and finally the intuitive exploration and interaction employing question answering on the resulting scientific knowledge base. We need to synergistically combine automated extraction and augmentation techniques, with large-scale collaboration to reach an unprecedented level of knowledge graph breadth and depth. As a result, knowledge-based information flows can facilitate completely new ways of search and exploration. The efficiency and effectiveness of scholarly communication will significant increase, since ambiguities are reduced, reproducibility is facilitated, redundancy is avoided, provenance and contributions can be better traced and the interconnections of research contributions are made more explicit and transparent. In this talk we will present first steps in this direction in the context of our Open Research Knowledge Graph initiative and the ScienceGRAPH project.

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The document-oriented workflows in science have reached (or already exceeded) the limits of adequacy as highlighted for example by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. Now it is possible to rethink this dominant paradigm of document-centered knowledge exchange and transform it into knowledge-based information flows by representing and expressing knowledge through semantically rich, interlinked knowledge graphs. The core of the establishment of knowledge-based information flows is the creation and evolution of information models for the establishment of a common understanding of data and information between the various stakeholders as well as the integration of these technologies into the infrastructure and processes of search and knowledge exchange in the research library of the future. By integrating these information models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This has the potential to revolutionize scientific work because information and research results can be seamlessly interlinked with each other and better mapped to complex information needs. Also research results become directly comparable and easier to reuse.

Python for Data Science

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In agile software development world, we are dealing with many test tasks such as user story testing, exploratory testing, check-list based testing, regression testing, performance testing, security testing in each sprint. Besides these testing activities, one of the test types which is considerably getting crucial is visual regression testing. Visual regression testing focuses on to check visual contents and animations, page layout, and responsive design of a website/app. Because of the limits of human vision, human-based visual regression testing is generally error-prone and cumbersome. Hence, automation is inevitable. It enables us to run the tests much more precisely in a short time period. Also, it saves us a significant amount of time to deal with other manual test activities in each sprint. In this talk, we will walk through well-known open-source and commercial solutions for visual test automation. We will learn which technologies they use, what type of visual tests they are suitable for, and their major differences between each other. Besides this overview, we will also make a real-life visual test automation demo by using Selenium, ImageMagick, and AShot.

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Slides from my talk at Selenium Camp Test Automation Conference - 2017 https://seleniumcamp.com/talk/end-to-end-test-automation-for-both-horizontal-and-vertical-scale/ Test automation (TA) activity has become a key critical work to guarantee the quality of system under test (SUT) by driving test and also development effort effectively. To bring this efficiency to projects, companies are investing on TA projects in a more motivated way. The question here is how we should design the automation strategy to handle complex TA projects together effectively. It can be done by automating test scenarios as E2E (end to end). Vertical E2E TA consists of; automating Test Data Preparation Phase and Unit, Integration and UI tests. For horizontal E2E TA; UI and Integration test cases, which are automated, designed as integrated real user scenarios. I will tell about the prerequisites, principles and key factors to have E2E automated tests. And also I will share hands on experienced E2E test automation projects that Selenium was the key tool.

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Building a semantic search system - one that can correctly parse and interpret end-user intent and return the ideal results for users’ queries - is not an easy task. It requires semantically parsing the terms, phrases, and structure within queries, disambiguating polysemous terms, correcting misspellings, expanding to conceptually synonymous or related concepts, and rewriting queries in a way that maps the correct interpretation of each end user’s query into the ideal representation of features and weights that will return the best results for that user. Not only that, but the above must often be done within the confines of a very specific domain - ripe with its own jargon and linguistic and conceptual nuances. This talk will walk through the anatomy of a semantic search system and how each of the pieces described above fit together to deliver a final solution. We'll leverage several recently-released capabilities in Apache Solr (the Semantic Knowledge Graph, Solr Text Tagger, Statistical Phrase Identifier) and Lucidworks Fusion (query log mining, misspelling job, word2vec job, query pipelines, relevancy experiment backtesting) to show you an end-to-end working Semantic Search system that can automatically learn the nuances of any domain and deliver a substantially more relevant search experience.

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