• «From Knowledge Graphs to Knowledge Categories» interview de Ryan Wisneskey par Josh Shinavier, 17.11.2020 (vidéo 51mn)
    https://www.youtube.com/watch?v=-N33MZa3B9o

    For this third episode of The Graph Show, Josh Shinavier, Research Scientist at Uber, interviews Ryan Wisnesky, Co-Founder / CTO of Conexus.

    « The shift from graphs to categories,
    even though you are just adding
    little sets of equations,
    it gives you a formalism,
    that is Turing complete.
    If you like graphs
    you may like categories even better. »

    #enterprise #knowledge #graph #categories #categoryTheory

  • «Algebraic Property Graphs» par Joshua Shinavier et Ryan Wisnesky, 13.10.2020
    https://arxiv.org/abs/1909.04881

    « One of the main challenges of building an enterprise knowledge graph is guaranteeing interoperability between a single graph data model and the diverse and often changeable ecosystem of non-graph data models, languages, and tools which surround and support the graph. The simple and developer-friendly property graph family of data models lends itself to this task, yet the lack of a formal specification deprives the graph of well-defined semantics. In this paper, we observe that algebraic data types are a common foundation of most of the enterprise schema languages we deal with in practice, and are also a suitable basis for a property graph formalism. We introduce this formalism in terms of type theory, algebra, and category theory, also providing algorithms for query processing and data migration with guarantees of semantic consistency across supported languages and datasets. These results have clear connections to relational database theory, programming language theory, and graph theory, providing starting points for significant future work. Open research challenges described in the paper include adding constraints, query and schema languages, and logics on top of the basic type system, interfacing with specific graph and non-graph data models, and performing operations which are typically difficult or ill-defined on property graphs, such as graph merges. »

    #enterprise #knowledge #graph #addressability

  • Monitoring being pitched to fight Covid-19 was tested on refugees

    The pandemic has given a boost to controversial data-driven initiatives to track population movements

    In Italy, social media monitoring companies have been scouring Instagram to see who’s breaking the nationwide lockdown. In Israel, the government has made plans to “sift through geolocation data” collected by the Shin Bet intelligence agency and text people who have been in contact with an infected person. And in the UK, the government has asked mobile operators to share phone users’ aggregate location data to “help to predict broadly how the virus might move”.

    These efforts are just the most visible tip of a rapidly evolving industry combining the exploitation of data from the internet and mobile phones and the increasing number of sensors embedded on Earth and in space. Data scientists are intrigued by the new possibilities for behavioural prediction that such data offers. But they are also coming to terms with the complexity of actually using these data sets, and the ethical and practical problems that lurk within them.

    In the wake of the refugee crisis of 2015, tech companies and research consortiums pushed to develop projects using new data sources to predict movements of migrants into Europe. These ranged from broad efforts to extract intelligence from public social media profiles by hand, to more complex automated manipulation of big data sets through image recognition and machine learning. Two recent efforts have just been shut down, however, and others are yet to produce operational results.

    While IT companies and some areas of the humanitarian sector have applauded new possibilities, critics cite human rights concerns, or point to limitations in what such technological solutions can actually achieve.

    In September last year Frontex, the European border security agency, published a tender for “social media analysis services concerning irregular migration trends and forecasts”. The agency was offering the winning bidder up to €400,000 for “improved risk analysis regarding future irregular migratory movements” and support of Frontex’s anti-immigration operations.

    Frontex “wants to embrace” opportunities arising from the rapid growth of social media platforms, a contracting document outlined. The border agency believes that social media interactions drastically change the way people plan their routes, and thus examining would-be migrants’ online behaviour could help it get ahead of the curve, since these interactions typically occur “well before persons reach the external borders of the EU”.

    Frontex asked bidders to develop lists of key words that could be mined from platforms like Twitter, Facebook, Instagram and YouTube. The winning company would produce a monthly report containing “predictive intelligence ... of irregular flows”.

    Early this year, however, Frontex cancelled the opportunity. It followed swiftly on from another shutdown; Frontex’s sister agency, the European Asylum Support Office (EASO), had fallen foul of the European data protection watchdog, the EDPS, for searching social media content from would-be migrants.

    The EASO had been using the data to flag “shifts in asylum and migration routes, smuggling offers and the discourse among social media community users on key issues – flights, human trafficking and asylum systems/processes”. The search covered a broad range of languages, including Arabic, Pashto, Dari, Urdu, Tigrinya, Amharic, Edo, Pidgin English, Russian, Kurmanji Kurdish, Hausa and French.

    Although the EASO’s mission, as its name suggests, is centred around support for the asylum system, its reports were widely circulated, including to organisations that attempt to limit illegal immigration – Europol, Interpol, member states and Frontex itself.

    In shutting down the EASO’s social media monitoring project, the watchdog cited numerous concerns about process, the impact on fundamental rights and the lack of a legal basis for the work.

    “This processing operation concerns a vast number of social media users,” the EDPS pointed out. Because EASO’s reports are read by border security forces, there was a significant risk that data shared by asylum seekers to help others travel safely to Europe could instead be unfairly used against them without their knowledge.

    Social media monitoring “poses high risks to individuals’ rights and freedoms,” the regulator concluded in an assessment it delivered last November. “It involves the use of personal data in a way that goes beyond their initial purpose, their initial context of publication and in ways that individuals could not reasonably anticipate. This may have a chilling effect on people’s ability and willingness to express themselves and form relationships freely.”

    EASO told the Bureau that the ban had “negative consequences” on “the ability of EU member states to adapt the preparedness, and increase the effectiveness, of their asylum systems” and also noted a “potential harmful impact on the safety of migrants and asylum seekers”.

    Frontex said that its social media analysis tender was cancelled after new European border regulations came into force, but added that it was considering modifying the tender in response to these rules.
    Coronavirus

    Drug shortages put worst-hit Covid-19 patients at risk
    European doctors running low on drugs needed to treat Covid-19 patients
    Big Tobacco criticised for ’coronavirus publicity stunt’ after donating ventilators

    The two shutdowns represented a stumbling block for efforts to track population movements via new technologies and sources of data. But the public health crisis precipitated by the Covid-19 virus has brought such efforts abruptly to wider attention. In doing so it has cast a spotlight on a complex knot of issues. What information is personal, and legally protected? How does that protection work? What do concepts like anonymisation, privacy and consent mean in an age of big data?
    The shape of things to come

    International humanitarian organisations have long been interested in whether they can use nontraditional data sources to help plan disaster responses. As they often operate in inaccessible regions with little available or accurate official data about population sizes and movements, they can benefit from using new big data sources to estimate how many people are moving where. In particular, as well as using social media, recent efforts have sought to combine insights from mobile phones – a vital possession for a refugee or disaster survivor – with images generated by “Earth observation” satellites.

    “Mobiles, satellites and social media are the holy trinity of movement prediction,” said Linnet Taylor, professor at the Tilburg Institute for Law, Technology and Society in the Netherlands, who has been studying the privacy implications of such new data sources. “It’s the shape of things to come.”

    As the devastating impact of the Syrian civil war worsened in 2015, Europe saw itself in crisis. Refugee movements dominated the headlines and while some countries, notably Germany, opened up to more arrivals than usual, others shut down. European agencies and tech companies started to team up with a new offering: a migration hotspot predictor.

    Controversially, they were importing a concept drawn from distant catastrophe zones into decision-making on what should happen within the borders of the EU.

    “Here’s the heart of the matter,” said Nathaniel Raymond, a lecturer at the Yale Jackson Institute for Global Affairs who focuses on the security implications of information communication technologies for vulnerable populations. “In ungoverned frontier cases [European data protection law] doesn’t apply. Use of these technologies might be ethically safer there, and in any case it’s the only thing that is available. When you enter governed space, data volume and ease of manipulation go up. Putting this technology to work in the EU is a total inversion.”
    “Mobiles, satellites and social media are the holy trinity of movement prediction”

    Justin Ginnetti, head of data and analysis at the Internal Displacement Monitoring Centre in Switzerland, made a similar point. His organisation monitors movements to help humanitarian groups provide food, shelter and aid to those forced from their homes, but he casts a skeptical eye on governments using the same technology in the context of migration.

    “Many governments – within the EU and elsewhere – are very interested in these technologies, for reasons that are not the same as ours,” he told the Bureau. He called such technologies “a nuclear fly swatter,” adding: “The key question is: What problem are you really trying to solve with it? For many governments, it’s not preparing to ‘better respond to inflow of people’ – it’s raising red flags, to identify those en route and prevent them from arriving.”
    Eye in the sky

    A key player in marketing this concept was the European Space Agency (ESA) – an organisation based in Paris, with a major spaceport in French Guiana. The ESA’s pitch was to combine its space assets with other people’s data. “Could you be leveraging space technology and data for the benefit of life on Earth?” a recent presentation from the organisation on “disruptive smart technologies” asked. “We’ll work together to make your idea commercially viable.”

    By 2016, technologists at the ESA had spotted an opportunity. “Europe is being confronted with the most significant influxes of migrants and refugees in its history,” a presentation for their Advanced Research in Telecommunications Systems Programme stated. “One burning issue is the lack of timely information on migration trends, flows and rates. Big data applications have been recognised as a potentially powerful tool.” It decided to assess how it could harness such data.

    The ESA reached out to various European agencies, including EASO and Frontex, to offer a stake in what it called “big data applications to boost preparedness and response to migration”. The space agency would fund initial feasibility stages, but wanted any operational work to be jointly funded.

    One such feasibility study was carried out by GMV, a privately owned tech group covering banking, defence, health, telecommunications and satellites. GMV announced in a press release in August 2017 that the study would “assess the added value of big data solutions in the migration sector, namely the reduction of safety risks for migrants, the enhancement of border controls, as well as prevention and response to security issues related with unexpected migration movements”. It would do this by integrating “multiple space assets” with other sources including mobile phones and social media.

    When contacted by the Bureau, a spokeswoman from GMV said that, contrary to the press release, “nothing in the feasibility study related to the enhancement of border controls”.

    In the same year, the technology multinational CGI teamed up with the Dutch Statistics Office to explore similar questions. They started by looking at data around asylum flows from Syria and at how satellite images and social media could indicate changes in migration patterns in Niger, a key route into Europe. Following this experiment, they approached EASO in October 2017. CGI’s presentation of the work noted that at the time EASO was looking for a social media analysis tool that could monitor Facebook groups, predict arrivals of migrants at EU borders, and determine the number of “hotspots” and migrant shelters. CGI pitched a combined project, co-funded by the ESA, to start in 2019 and expand to serve more organisations in 2020.
    The proposal was to identify “hotspot activities”, using phone data to group individuals “according to where they spend the night”

    The idea was called Migration Radar 2.0. The ESA wrote that “analysing social media data allows for better understanding of the behaviour and sentiments of crowds at a particular geographic location and a specific moment in time, which can be indicators of possible migration movements in the immediate future”. Combined with continuous monitoring from space, the result would be an “early warning system” that offered potential future movements and routes, “as well as information about the composition of people in terms of origin, age, gender”.

    Internal notes released by EASO to the Bureau show the sheer range of companies trying to get a slice of the action. The agency had considered offers of services not only from the ESA, GMV, the Dutch Statistics Office and CGI, but also from BIP, a consulting firm, the aerospace group Thales Alenia, the geoinformation specialist EGEOS and Vodafone.

    Some of the pitches were better received than others. An EASO analyst who took notes on the various proposals remarked that “most oversell a bit”. They went on: “Some claimed they could trace GSM [ie mobile networks] but then clarified they could do it for Venezuelans only, and maybe one or two countries in Africa.” Financial implications were not always clearly provided. On the other hand, the official noted, the ESA and its consortium would pay 80% of costs and “we can get collaboration on something we plan to do anyway”.

    The features on offer included automatic alerts, a social media timeline, sentiment analysis, “animated bubbles with asylum applications from countries of origin over time”, the detection and monitoring of smuggling sites, hotspot maps, change detection and border monitoring.

    The document notes a group of services available from Vodafone, for example, in the context of a proposed project to monitor asylum centres in Italy. The proposal was to identify “hotspot activities”, using phone data to group individuals either by nationality or “according to where they spend the night”, and also to test if their movements into the country from abroad could be back-tracked. A tentative estimate for the cost of a pilot project, spread over four municipalities, came to €250,000 – of which an unspecified amount was for “regulatory (privacy) issues”.

    Stumbling blocks

    Elsewhere, efforts to harness social media data for similar purposes were proving problematic. A September 2017 UN study tried to establish whether analysing social media posts, specifically on Twitter, “could provide insights into ... altered routes, or the conversations PoC [“persons of concern”] are having with service providers, including smugglers”. The hypothesis was that this could “better inform the orientation of resource allocations, and advocacy efforts” - but the study was unable to conclude either way, after failing to identify enough relevant data on Twitter.

    The ESA pressed ahead, with four feasibility studies concluding in 2018 and 2019. The Migration Radar project produced a dashboard that showcased the use of satellite imagery for automatically detecting changes in temporary settlement, as well as tools to analyse sentiment on social media. The prototype received positive reviews, its backers wrote, encouraging them to keep developing the product.

    CGI was effusive about the predictive power of its technology, which could automatically detect “groups of people, traces of trucks at unexpected places, tent camps, waste heaps and boats” while offering insight into “the sentiments of migrants at certain moments” and “information that is shared about routes and motives for taking certain routes”. Armed with this data, the company argued that it could create a service which could predict the possible outcomes of migration movements before they happened.

    The ESA’s other “big data applications” study had identified a demand among EU agencies and other potential customers for predictive analyses to ensure “preparedness” and alert systems for migration events. A package of services was proposed, using data drawn from social media and satellites.

    Both projects were slated to evolve into a second, operational phase. But this seems to have never become reality. CGI told the Bureau that “since the completion of the [Migration Radar] project, we have not carried out any extra activities in this domain”.

    The ESA told the Bureau that its studies had “confirmed the usefulness” of combining space technology and big data for monitoring migration movements. The agency added that its corporate partners were working on follow-on projects despite “internal delays”.

    EASO itself told the Bureau that it “took a decision not to get involved” in the various proposals it had received.

    Specialists found a “striking absence” of agreed upon core principles when using the new technologies

    But even as these efforts slowed, others have been pursuing similar goals. The European Commission’s Knowledge Centre on Migration and Demography has proposed a “Big Data for Migration Alliance” to address data access, security and ethics concerns. A new partnership between the ESA and GMV – “Bigmig" – aims to support “migration management and prevention” through a combination of satellite observation and machine-learning techniques (the company emphasised to the Bureau that its focus was humanitarian). And a consortium of universities and private sector partners – GMV among them – has just launched a €3 million EU-funded project, named Hummingbird, to improve predictions of migration patterns, including through analysing phone call records, satellite imagery and social media.

    At a conference in Berlin in October 2019, dozens of specialists from academia, government and the humanitarian sector debated the use of these new technologies for “forecasting human mobility in contexts of crises”. Their conclusions raised numerous red flags. They found a “striking absence” of agreed upon core principles. It was hard to balance the potential good with ethical concerns, because the most useful data tended to be more specific, leading to greater risks of misuse and even, in the worst case scenario, weaponisation of the data. Partnerships with corporations introduced transparency complications. Communication of predictive findings to decision makers, and particularly the “miscommunication of the scope and limitations associated with such findings”, was identified as a particular problem.

    The full consequences of relying on artificial intelligence and “employing large scale, automated, and combined analysis of datasets of different sources” to predict movements in a crisis could not be foreseen, the workshop report concluded. “Humanitarian and political actors who base their decisions on such analytics must therefore carefully reflect on the potential risks.”

    A fresh crisis

    Until recently, discussion of such risks remained mostly confined to scientific papers and NGO workshops. The Covid-19 pandemic has brought it crashing into the mainstream.

    Some see critical advantages to using call data records to trace movements and map the spread of the virus. “Using our mobile technology, we have the potential to build models that help to predict broadly how the virus might move,” an O2 spokesperson said in March. But others believe that it is too late for this to be useful. The UK’s chief scientific officer, Patrick Vallance, told a press conference in March that using this type of data “would have been a good idea in January”.

    Like the 2015 refugee crisis, the global emergency offers an opportunity for industry to get ahead of the curve with innovative uses of big data. At a summit in Downing Street on 11 March, Dominic Cummings asked tech firms “what [they] could bring to the table” to help the fight against Covid-19.

    Human rights advocates worry about the longer term effects of such efforts, however. “Right now, we’re seeing states around the world roll out powerful new surveillance measures and strike up hasty partnerships with tech companies,” Anna Bacciarelli, a technology researcher at Amnesty International, told the Bureau. “While states must act to protect people in this pandemic, it is vital that we ensure that invasive surveillance measures do not become normalised and permanent, beyond their emergency status.”

    More creative methods of surveillance and prediction are not necessarily answering the right question, others warn.

    “The single largest determinant of Covid-19 mortality is healthcare system capacity,” said Sean McDonald, a senior fellow at the Centre for International Governance Innovation, who studied the use of phone data in the west African Ebola outbreak of 2014-5. “But governments are focusing on the pandemic as a problem of people management rather than a problem of building response capacity. More broadly, there is nowhere near enough proof that the science or math underlying the technologies being deployed meaningfully contribute to controlling the virus at all.”

    Legally, this type of data processing raises complicated questions. While European data protection law - the GDPR - generally prohibits processing of “special categories of personal data”, including ethnicity, beliefs, sexual orientation, biometrics and health, it allows such processing in a number of instances (among them public health emergencies). In the case of refugee movement prediction, there are signs that the law is cracking at the seams.
    “There is nowhere near enough proof that the science or math underlying the technologies being deployed meaningfully contribute to controlling the virus at all.”

    Under GDPR, researchers are supposed to make “impact assessments” of how their data processing can affect fundamental rights. If they find potential for concern they should consult their national information commissioner. There is no simple way to know whether such assessments have been produced, however, or whether they were thoroughly carried out.

    Researchers engaged with crunching mobile phone data point to anonymisation and aggregation as effective tools for ensuring privacy is maintained. But the solution is not straightforward, either technically or legally.

    “If telcos are using individual call records or location data to provide intel on the whereabouts, movements or activities of migrants and refugees, they still need a legal basis to use that data for that purpose in the first place – even if the final intelligence report itself does not contain any personal data,” said Ben Hayes, director of AWO, a data rights law firm and consultancy. “The more likely it is that the people concerned may be identified or affected, the more serious this matter becomes.”

    More broadly, experts worry that, faced with the potential of big data technology to illuminate movements of groups of people, the law’s provisions on privacy begin to seem outdated.

    “We’re paying more attention now to privacy under its traditional definition,” Nathaniel Raymond said. “But privacy is not the same as group legibility.” Simply put, while issues around the sensitivity of personal data can be obvious, the combinations of seemingly unrelated data that offer insights about what small groups of people are doing can be hard to foresee, and hard to mitigate. Raymond argues that the concept of privacy as enshrined in the newly minted data protection law is anachronistic. As he puts it, “GDPR is already dead, stuffed and mounted. We’re increasing vulnerability under the colour of law.”

    https://www.thebureauinvestigates.com/stories/2020-04-28/monitoring-being-pitched-to-fight-covid-19-was-first-tested-o
    #cobaye #surveillance #réfugiés #covid-19 #coronavirus #test #smartphone #téléphones_portables #Frontex #frontières #contrôles_frontaliers #Shin_Bet #internet #big_data #droits_humains #réseaux_sociaux #intelligence_prédictive #European_Asylum_Support_Office (#EASO) #EDPS #protection_des_données #humanitaire #images_satellites #technologie #European_Space_Agency (#ESA) #GMV #CGI #Niger #Facebook #Migration_Radar_2.0 #early_warning_system #BIP #Thales_Alenia #EGEOS #complexe_militaro-industriel #Vodafone #GSM #Italie #twitter #détection #routes_migratoires #systèmes_d'alerte #satellites #Knowledge_Centre_on_Migration_and_Demography #Big_Data for_Migration_Alliance #Bigmig #machine-learning #Hummingbird #weaponisation_of_the_data #IA #intelligence_artificielle #données_personnelles

    ping @etraces @isskein @karine4 @reka

    signalé ici par @sinehebdo :
    https://seenthis.net/messages/849167

  • VerbNet — A Computational Lexical Resource for Verbs

    « VerbNet is a hierarchical domain-independent, broad-coverage verb lexicon with mappings to other lexical resources such as WordNet (Miller, 1990; Fellbaum, 1998), Xtag (XTAG Research Group, 2001), and FrameNet (Baker et al., 1998). VerbNet is organized into verb classes extending Levin (1993) classes through refinement and addition of subclasses to achieve syntactic and semantic coherence among members of a class. Each verb class in VN is completely described by thematic roles, selectional restrictions on the arguments, and frames consisting of a syntactic description and semantic predicates with a temporal function, in a manner similar to the event decomposition of Moens and Steedman (1988). »

    https://verbs.colorado.edu/verbnet #knowledge #graph #thesaurus #text #xml

  • WordNet — A Lexical Database for English

    « WordNet® is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations. The resulting network of meaningfully related words and concepts can be navigated with the browser. WordNet is also freely and publicly available for download. WordNet’s structure makes it a useful tool for computational linguistics and natural language processing. »

    https://wordnet.princeton.edu #knowledge #graph #thesaurus #text

  • DBpedia – A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia

    « DBpedia is a crowd-sourced community effort to extract structured content from the information created in various Wikimedia projects. This structured information resembles an open knowledge graph (OKG) which is available for everyone on the Web. […] DBpedia data is served as Linked Data, which is revolutionizing the way applications interact with the Web. One can navigate this Web of facts with standard Web browsers, automated crawlers or pose complex queries with SQL-like query languages (e.g. SPARQL). Have you thought of asking the Web about all cities with low criminality, warm weather and open jobs? That’s the kind of query we are talking about. »

    https://wiki.dbpedia.org/about #datasets #knowledge #graph #rdf

  • The Goldilocks Zone: How #everipedia Will Dominate the Future of #knowledge
    https://hackernoon.com/the-goldilocks-zone-how-everipedia-will-dominate-the-future-of-knowledge

    Goldilocks drinking the bowl of porridge that is “just right”In astronomy, the circumstellar habitable zone is defined as the range of orbits around a star in which a planet has the ability to support liquid water given sufficient atmospheric pressure. This habitable zone of a planet is determined by the distance it is from a star and the amount of radiant energy it receives from said star. The notion states that if a planet is in this zone and can support liquid water, then it has the capacity to support life.The habitable zone for life is better known to many as the “Goldilocks zone”, a metaphor taken from the classic children’s fairy tale Goldilocks and the Three Bears where a little girl chooses three items (such as soup), ignoring the ones that are extreme (too hot, too cold), and (...)

    #everipedia-partnership #wikipedia #content

  • Why “FREE” Isn’t Your Answer to Help Authoring Tools
    https://hackernoon.com/why-free-isnt-your-answer-to-help-authoring-tools-512bd86015b1?source=rs

    Originally published by Catherine Heath at document360.ioInvesting in your documentation is now essential. 39% of millennials will check a company’s documentation before contacting support for help, and self-service is now an expected part of your support strategy. Help Authoring Tools (HATs) are a useful addition to any business — whether you have teams of technical writers, just one, or other employees pitching in, producing documentation remains essential for teams who want to improve their products or comply with regulations.HATs are typically used to help you produce large amounts of documentation that is centrally managed by your technical writing team.Some common Help Authoring Tools are industry standards like Madcap Flare or Adobe RoboHelp, and they enable you to single source (...)

    #open-source #madcap-flare #authoring-tool #knowledge-base

  • 10 Knowledge Base Software Best Practice Examples
    https://hackernoon.com/10-knowledge-base-software-best-practice-examples-24f2c9ae845e?source=rs

    A great knowledge base usually follows a set of best practices. These will help you deliver useful content to your customers, and empower them to help themselves.We’ll now go through 10 real-life examples of outstanding knowledge bases to illustrate each of the best practices.Contents1. Getting Started Tutorials — MailChimpFor SaaS (Software as a Service) companies, your software onboarding stage is crucial for customer engagement and retention. It’s also significant when customers are signing up for a free trial and deciding if they like your product.Your knowledge base must be accessible to new users. These types of users need to get up and running with your product quickly, and this is where #documentation comes in.Email marketing software company MailChimp makes its Getting Started (...)

    #knowledge-base-software #ux #saas-tools #knowledge-base

  • Google Knowledge Graph — The Best Guide For Beginners
    https://hackernoon.com/google-knowledge-graph-the-best-guide-for-beginners-d513c1180abf?source=

    Google Knowledge Graph — The Best Guide For BeginnersDo you want to learn about the latest gadgets in the market? Perhaps you’d like to check out the best Chinese restaurants in your town? Or are you conducting research on the life of an entrepreneur?Regardless of the nature of your search query, Google tries its best to deliver the most relevant and meaningful answers to you.There is no doubt that Google has become an indispensable part of every internet user’s life. From acquiring knowledge to buying products, you run quick Google searches for almost every need, big or small. In fact, more than 71,000 search queries are conducted by Google every second.HANDPICKED RELATED CONTENT:8 Inbound Marketing Strategies Your Startup Needs to Start Using Today8 Website Navigation Mistakes You Might Be (...)

    #google-knowledge-graph #seo #knowledge-graph #google-analytics #guides-and-tutorials

  • Knowledge Era #leadership Tips
    https://hackernoon.com/knowledge-era-leadership-tips-752b1104e48f?source=rss----3a8144eabfe3---

    By Dave Dame, Professional #scrum Trainer, Scrum.orgIn working in today’s knowledge work economy, it surprises me that many leaders still use traditional ways of leading their people. Leaders still put most of the focus on the work to be done instead of focusing on the people to create an environment where great work can be done.My suspicion it has to do with legacy industries and businesses. They still see work as straightforward and the leader has the most experience to break down and explain the work so many people can execute on it to finish it.In today’s complex, rapidly changing landscape the team you build better be supported to rapidly learn and have an environment that fosters continual validated learning if they have any hope to survive. The team needs to be more knowledgeable (...)

    #knowledge-era-leadership #agile #knowledge-era

  • Machine Learning #notes 1
    https://hackernoon.com/machine-learning-notes-1-c496591f49b8?source=rss----3a8144eabfe3---4

    From Machine Learning -Tom M. MitchellMachine Learning is at the forefront of advancements in Artificial Intelligence. It’s moving fast with new research coming out each and every day. This Series, along with the other posts includes some of the important concepts and notes right from the basics to advance, from the book Machine Learning, by Tom M. Mitchell. And will be updated for each chapter of the book.CHAPTER 1: INTRODUCTION1.1 Well posed learning problem“A computer is said to learn from experience E with respect to some class of task T and performance measure P, if it’s performance at tasks in T, as measured by P, improves with experience E.”Learning to recognise spoken words, SPHINX systemLearning to drive an autonomous vehicle, ALVINN systemLearning to classify new astronomical (...)

    #concept #knowledge #study-notes #machine-learning

  • Open Source Knowledge Base Software Vs #saas Solution
    https://hackernoon.com/open-source-knowledge-base-software-vs-saas-solution-61f68db44d4d?source

    by Jade Emmons at Document360Open source software is a huge industry with many active projects. As a result, companies are interested in finding out more about open source knowledge base software. Using the open source model is a popular way to leverage the abilities of the crowd to produce software.There are many definitions of “open source”, but here is one from opensource.com:The term “open source” refers to something people can modify and share because its design is publicly accessible.“Open” can refer to an absence of traditional hierarchical team structures, as well as a publicly accessible design.In the case of software development, open source tends to refer to the source code of the software in that users can directly access the code. This code may be hosted in a GitHub repository (...)

    #knowledge-management #saas-tools #open-source #customer-service

  • Forget ownership, focus on access
    https://hackernoon.com/forget-ownership-focus-on-access-bc3420a6caef?source=rss----3a8144eabfe3

    Or — why you shouldn’t try to know everythingI’ve really gotten into podcasts lately — they are accessible, they are free, and there’s a ton of them. A couple of days back The Minimalists caught my attention. Podcast authors spread the ideas of how to live meaningful lives with less. Just to make it clear — I’m not even close to being a minimalist. If there’s something to buy, I’m on it. However, one of their episodes, mysteriously named “Access”, sucked me in completely. I got so affected by the topic I wasn’t able to get it out of my head ever since.In the aforementioned episode, hosts Joshua Fields Millburn and Ryan Nicodemus discuss ownership of things versus access to things with Ryan Delk from Omni. The basic idea behind the topic is that people of our generation are trying to own everything while (...)

    #manufacturing #focus-on-access #acces #knowledge #forget-ownership

  • #hacking Your Way Through the #freelance Economy Through Knowledge Commerce
    https://hackernoon.com/hacking-your-way-through-the-freelance-economy-through-knowledge-commerc

    In the last year, more than 57 million Americans worked as freelancers, adding $1.4 trillion to the economy. #freelancing is becoming much more common, and freelancers are having an easier time finding work. If you’re thinking about freelancing, consider becoming an infopreneur, and selling your expertise in a topic through teaching, consulting, or merchandise.Chances are you have a hobby or are passionate about a subject that most people know very little about. Infopreneurs take that topic, whatever it is, and turn it into a revenue stream. Whether your teaching karate, leadership skills, or how to make a quilt, people are hungry for information and willing to pay to learn.The place to start is with your topic. Find the right topic for your skill set and become an expert. Take online (...)

    #knowledge-commerce #freelance-economy