The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution. These tasks are usually required to build more advanced text processing services.
Corona-Warn-App
Corona-Warn-App was the official and open-source COVID-19 contact tracing app used for digital contact tracing in Germany made by SAP and Deutsche Telekom subsidiary T-Systems. It had been downloaded 22.8 million times as of 19 November 2020 and 26.2 million times as of 18 March 2021. The app has been promoted by billboard and broadcast advertisements, e.g. in cooperation with the German Football Association (DFB) and other prominent companies. The German government has announced that the app would no longer exchange tracing information as of April 30, 2023 & would enter hibernation as of June 1, 2023. == Effectiveness == Experts believe that time saved by using the app can be critical for improving the effectiveness contact tracing efforts. Some virologists say when at least 60% of people in Germany use it, it would be very effective. == Functioning == The app works with the Exposure Notification Framework (what is implemented in Google Play Services for Android and in iOS) by using Bluetooth to exchange codes with app users that are within 1.5 meters of each other for a period of at least 10 minutes. Anyone who tests positive for COVID-19 can share this information voluntarily with the app. Other app users are then notified about when, how long and at what distance they had contact with the infected person within a 14-day period. Testing is available for persons on a voluntary basis. === Server architecture === Based on the Client–server model five servers are operated within the app backend: the Corona-Warn-App server. It stores the authorized keys of infected users, referred to as diagnosis keys, from the past 14 days in its database. Stored diagnosis keys are grouped into regularly updated blocks which are transmitted to the Content Delivery Network. This interface supplies the keys for the app clients to download and locally compute a potential exposure risk. the Verification server. It is responsible for documenting the approval of the user to share their positive test result with the app and also to verify the test result. the Portal Server. It generates a so-called teleTAN token if the user did not give their consent to share their test result with the app at first but then changed their mind or if the local public health authority or test laboratory is not connected to the app system yet. the Test Result Server. It saves the test results provided by the local public health authorities or test laboratories for further use within the backend. the Federation Gateway Server. It connects to the national Corona-Warn-App servers of participating EU countries to enable transnational key exchange. By the distribution of the data on different servers the decoupling of the data becomes possible and results in an obstructed tracing of the app users. ==== Report of a positive COVID-19 test ==== The app provides a function to warn other app users by uploading their positive test result on a voluntarily and anonymous basis to the Corona-Warn-App server. In case the local public health authority or test laboratory is already connected to the app system, the user receives a QR-Code when the swab specimen is taken that can be scanned in the app. After scanning the QR-Code und the user getting authorized by the Verification server, the app receives an individual Registration token which gets stored locally and with which the status and the result of the test can be checked manually as well as automatically. If the local public health authority or test laboratory is not connected to the app system yet and the user wants to share their positive test result with other app users, it is required to request a teleTAN token by calling the verification hotline of the app. In both cases, the user can upload their diagnosis keys of the last 14 days to the Corona-Warn-App server in case their consent to share the information is given. The Corona-Warn-App server then verifies the uploaded keys by asking the Verification server if the keys are valid and if they are, the Corona-Warn-App server stores them in its database. == Privacy == The use of the app is voluntary. The app implements decentralized data storage to ensure data privacy. Employers can require that Corona-Warn be installed on company phones, but can not compel its use on private phones. == Funding == The open source app, which costs €20 million to develop is intended to supplement human contact tracing efforts, which Germany put in place during the early stages of the COVID-19 pandemic in Germany. In August 2022, a spokesperson for the German ministry of health announced that the total costs including all additional developments are now estimated to be closer to €150m. == Interoperability == At its start the app only worked in Germany, and Jens Spahn, than Federal Minister of Health (CDU), has said the development of a Europe-wide system is a future goal. With the update published on 19 October 2020 the app supports key-exchanges with the EU Interoperability Gateway and is therefore able to communicate with contact tracing apps from Ireland and Italy. Austria, Belgium, Czech Republic, Croatia, Cyprus, Denmark, Finland, Ireland, Italy, Latvia, Malta, Netherlands, Norway, Poland, Slovenia, Spain and Switzerland had joined the gateway as well and are also able to exchange keys with Corona-Warn-App. The app can be downloaded in many App stores outside of Germany. However, as of August 2021, the app is still unavailable for those of notable national German minorities like Turks, Russians or Ukrainians, who use App stores of their home countries. == Software variants == An unofficial Corona-Warn-App has been released on F-Droid, making the app available without proprietary components on Android phones. == Literature == Thomas Köllmann: Die Corona-Warn-App – Schnittstelle zwischen Datenschutz- und Arbeitsrecht. In: Neue Zeitschrift für Arbeitsrecht. Nr. 13, 10. Juli 2020, S. 831–836.
PL/Perl
PL/Perl (Procedural Language/Perl) is a procedural language supported by the PostgreSQL RDBMS. PL/Perl, as an imperative programming language, allows more control than the relational algebra of SQL. Programs created in the PL/Perl language are called functions and can use most of the features that the Perl programming language provides, including common flow control structures and syntax that has incorporated regular expressions directly. These functions can be evaluated as part of a SQL statement, or in response to a trigger or rule. The design goals of PL/Perl were to create a loadable procedural language that: can be used to create functions and trigger procedures, adds control structures to the SQL language, can perform complex computations, can be defined to be either trusted or untrusted by the server, is easy to use. PL/Perl is one of many "PL" languages available for PostgreSQL PL/pgSQL PL/Java, plPHP, PL/Python, PL/R, PL/Ruby, PL/sh, and PL/Tcl.
Knowledge organization system
Knowledge organization system (KOS), concept system, or concept scheme is the generic term used in knowledge organization (KO) for the selection of concepts with an indication of selected semantic relations. Despite their differences in type, coverage, and application, all KOS aim to support the organization of knowledge and information to facilitate their management and retrieval. KOS vary in complexity from simple sorted lists to complex relational networks. They represent both structural and functional features, and serve to eliminate ambiguity, control synonyms, establish relationships, and present properties. From their origins in library and information science (LIS), KOS have been applied to other domains and disciplines within science and industry, although scholarly research and debate remain primarily within the KO field. Challenges of KOS include ambiguity of terminology, repercussions of biased systems, and potential obsolescence. KOS can be expressed in RDF and RDFS as per the Simple Knowledge Organization System (SKOS) recommendation by W3C, which aims to enable the sharing and linking of KOS via the Web. One of the largest collections of KOS is the BARTOC registry. == Types == While different schema of KOS have been proposed, most are generally arranged in terms of the complexity of their construction and maintenance. Some scholars argue that organizing KOS on a spectrum oversimplifies the shared characteristics among them, and may even result in a non-ideal structure being chosen. The following types are not exhaustive, and are often not mutually-exclusive in practice. === Term lists === Term lists are the least structured form of KOS. They include lists, glossaries, dictionaries, and synonym rings. Authority files and gazetteers may also be considered term lists, however other scholars categorize them and directories as "metadata-like models". Examples include the Union List of Artist Names name authority file and the GeoNames gazetteer. === Categorization and classification === KOS that emphasize specific (and often hierarchical) structures include subject headings, taxonomies, categorization schema, and classification schema & systems. Despite inconsistent use of the terms "categorization" and "classification" in some literature, categorization is generally loosely-assembled grouping schema and may include attributes that are not mutually exclusive (or having fuzzy boundaries), while classification is related to the arrangement of non-overlapping and mutually-exclusive classes. Classification schema may be universal (such as Dewey Decimal Classification and Information Coding Classification) or domain-specific (such as the National Library of Medicine Classification). === Relationship models === The types of KOS with greatest complexity and which utilize connections between concepts include thesauri, semantic networks, and ontologies. One of the most prominent examples of a semantic network is WordNet. === Others === Certain structures proposed to be considered types of KOS—but are not consistently included in schema—include folksonomies, topic maps, web directory structures, publication organization systems, and bibliometric maps. Some KOS organize other KOS themselves—for instance, PeriodO is a gazetteer of periodization categories. == Applications == Some early KOS were developed as a support system for abstracting and indexing services to be used by specially-trained searchers. With the growth of information digitization, usability became increasingly accessible, and more complex structures were developed. Prominent examples of KOS outside of LIS include organism taxonomy in biology, the periodic table of elements in chemistry, SIC and NAICS classification systems for industry & business, and AGROVOC agricultural controlled vocabulary. == Challenges == The study and design of KOS is an ongoing topic of discussion among KO scholars. === Terminology === [There is] a serious lack of vocabulary control in the literature on controlled vocabulary. Inconsistency of terminology within the study of KOS is a common issue. For instance, "ontology" is used for both a specific type of KOS as well as a generic term for any KOS. The terms "taxonomy", "classification", and "categorization" are also sometimes used interchangeably. === Bias === As knowledge can be historically and culturally biased, scholars have also discussed how KOS themselves can perpetuate harmful practices or stereotypes. For example, a number of concerns and criticisms about the classification of mental disorders in the Diagnostic and Statistical Manual of Mental Disorders have been raised, contributing to ongoing revisions. Ethical and intentional design approaches have been proposed for multi-perspective KOS in efforts to mitigate bias and other harmful practices. === Obsolescence === The possible obsolescence of the thesaurus and other simpler KOS has been the topic of debate, especially in the face of increasingly complex ontologies, the growing usage of "Google-like retrieval systems", and the move of KO theory and research away from LIS and toward computer science. Supporters of thesauri argue its continued usefulness for metadata enrichment, vocabulary mapping, and web services, as well as its usage in specific domains such as corporate intranets and digital image libraries.
Harold Borko
Harold Borko (1922-2012) was an American psychologist and researcher working primarily in the field of information science. == Biography == Borko was born in 1922 in New York City, New York. After serving in the US Army from 1942 to 1946 he obtained a BA in Psychology from the University of California, Los Angeles in 1948 and both his MA and PhD from the University of Southern California in Psychology in 1952. He returned to the army as a psychologist until 1956 after which he began a career working in and teaching information science. He died in California in 2012. == Information Science Career == After leaving the military Borko began working at the RAND Corporation as a Systems Training Specialist in 1956 and moved to the Systems Development Corporation a year later working in the Language Processing and Retrieval department. Alongside this work he taught Psychology at USC from 1957-65 and then moved into teaching Library Science at UCLA from 1965. In 1967 Borko left his role at the Systems Development Corporation and continued as a full-time professor at UCLA until his retirement in 1993.. From 1961 to 1995 Borko authored and co-authored over 100 articles on new developments in the field as well as the historiography of information science. He served as an editor of the Journal of Educational Data Processing from 1963-1975 and as President of the American Society for Information Science in 1966 == Partial list of works == Borko, H. (1962, May). The construction of an empirically based mathematically derived classification system. In Proceedings of the May 1-3, 1962, spring joint computer conference (pp. 279-289). Borko, H., & Bernick, M. (1963). Automatic document classification. Journal of the ACM (JACM), 10(2), 151-162. Borko, H. (1964). The Storage and Retrieval of Educational Information. Journal of Teacher Education, 15(4), 449-452. Borko, H. (1964). Measuring the reliability of subject classification by men and machines. American Documentation, 15(4), 268-273. Borko, H. (1965). The conceptual foundations of information systems. Borko, H. (1968), Information science: What is it?†. Amer. Doc., 19: 3-5. https://doi.org/10.1002/asi.5090190103 Borko, H. (1970). Experiments in book indexing by computer. Information storage and retrieval, 6(1), 5-16. Borko, H. (1985). An introduction to computer-based library systems (Lucy A. Tedd). Education for Information, 3(1), 61.
ASR-complete
ASR-complete is, by analogy to "NP-completeness" in complexity theory, a term to indicate that the difficulty of a computational problem is equivalent to solving the central automatic speech recognition problem, i.e. recognize and understanding spoken language. Unlike "NP-completeness", this term is typically used informally. Such problems are hypothesised to include: Spoken natural language understanding Understanding speech from far-field microphones, i.e. handling the reverbation and background noise These problems are easy for humans to do (in fact, they are described directly in terms of imitating humans). Some systems can solve very simple restricted versions of these problems, but none can solve them in their full generality.
Metadata management
Metadata management involves managing metadata about other data, whereby this "other data" is generally referred to as content data. The term is used most often in relation to digital media, but older forms of metadata are catalogs, dictionaries, and taxonomies. For example, the Dewey Decimal Classification is a metadata management system developed in 1876 for libraries. == Metadata schema == Metadata management goes by the end-to-end process and governance framework for creating, controlling, enhancing, attributing, defining and managing a metadata schema, model or other structured aggregation system, either independently or within a repository and the associated supporting processes (often to enable the management of content). For web-based systems, URLs, images, video etc. may be referenced from a triples table of object, attribute and value. == Scope == With specific knowledge domains, the boundaries of the metadata for each must be managed, since a general ontology is not useful to experts in one field whose language is knowledge-domain specific. == Metadata Manager == In the process of developing a knowledge management solution, creating a metadata schema, and a system in which metadata is managed, a dedicated resource may be appointed to maintain adherence to metadata standards as defined by data owners as well as general best practice. This person is responsible for curation of the business and technical layers of the metadata schema, and commonly involved with strategy and implementation. A metadata manager is not required to master all aspects, or be involved with everything concerning the solution, but an understanding of as much of the process as possible to ensure a relevant schema is developed. == Metadata management over time == Managing the metadata in a knowledge management solution is an important step in a metadata strategy. It is part of the strategy to make sure that the metadata are complete, current and correct at any given time. Managing a metadata project is also about making sure that users of the system are aware of the possibilities allowed by a well-designed metadata system and how to maximize the benefits of metadata. Regularly monitoring the metadata to ensure that the schema remains relevant is advised. === Wikipedia metadata === Wikipedia is a project that actively manages metadata for its articles and files. For example, volunteer editors carefully curate new biographical articles based on the notability (claim to fame), name, birth, and/or death dates. Similarly, volunteer editors carefully curate new architectural articles based on name, municipality, or geo coordinates. When new articles with a valid alternate spelling are added to Wikipedia that match up to existing articles based on metadata, these are then manually checked and if needed, tagged for merging. When new articles are added that are considered out of scope or otherwise unfit for Wikipedia, these are nominated for deletion. To help keep track of metadata on Wikipedia, the new Wikimedia project Wikidata was established in 2012. Click on the pictures to view more metadata about these images: