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Who Ontologizes the World? (On Wikidata (Open Your Structured Data With…

- - - - We want more installations outside the Wikiverse
      - Not all data is in scope of the Wikidata project. How about a domain-specific repository?
      - Why would you want a Wikibase instance?
        
        We may need an ontology to say if the item contains bell pepper, onions
        
        FactGrid project https://wikimediafoundation.org/2018/08/30/wikibase-illuminati-history/
        
        Rhizome: https://wikimediafoundation.org/2018/09/06/rhizome-wikibase/
        
        LinguaLIbre : https://lingualibre.fr/wiki/LinguaLibre:Main_Page
    - - Jensen Nauber: Agree. In Germany there is some development towards a more open and collaborative authority file. The Integrated Authority File (GND) wants to open their database for GLAM institutions. How to do that will be discussed at the #GNDCon (https://wiki.dnb.de/x/E4GKC ).
      - Real federated Wikibase, with reuse of Wikidata items and properties along to your own ontology, will eventually come in 2019. For more info ask johl. So you will be able to run your own vandalism-free wikibase instance and still be in sync with wikidata.
    - - The two projects are therefore natural partners. By defining ways for Wikipedia’s huge dataset to be used within schema.org descriptions, we could reduce the pressure for schema.org to include large lists of things, or have comprehensive type lists for all topics. And by bridging Wikipedia’s data structures to data published elsewhere in the Web, we can show techniques that allow different parties to contribute data to the Wikipedia ecosystem without necessarily copying everything into the Wikidata database. This echoes the debate around deletionism/inclusionism in the wider Wikipedia community. If other sites (perhaps MediaWiki-backed) also publish structured data using schema.org + Wikidata markup, it may be possible to show some richer linking of information across sites.
      - Schema.org already has many classes for local businesses and services; various kinds of ‘FoodEstablishment’, ‘GovernmentOrganization‘. These don’t exhaust the possibilities. Schema.org aims to remain a central documentation hub for both search engines and publishers, showing simple practical markup for structured data. But schema.org is not the best place to manage lists of kinds of food or government establishment. It is a priority for schema.org to show how to integrate such external data - eg. country codes, categories etc. Wikidata, as it evolves, is a natural source of such content. If we define integration points, it should be possible for the Wikipedia community’s work to make richer schema.org descriptions possible. Meanwhile, sites that provide structured data using schema.org markup can provide data that helps grow Wikidata’s own descriptive databases.
  - - - DBpedia - extracted from wikipedia
      - FreeBase - originally Google
      - Wikidata -
      - Describe real world entities, their relationships, and attributes
      - YAGO - extracted from wikipedia
  - - - Ontology engineering perspective
        
        Wikidata softens the boundary between the conceptual and instance level - makes it challenging on modelling the conceptual level.
        
        Work on Items can either be 'Instances' or 'Classes' -> semantic closeness of the contributions cannot be easily differentiated
        
        Simplified user interface is very valuable for data contribution, less valuable for data modelling.
    - - Actions
        
        Normalisation
        
        Labelling
        
        Coding
        
        Aggregation
      - Pattern of Participation
        
        Reference Editor
        
        Item Creator
        
        Item Editor
        
        Item Expert
        
        Property Editor
        
        Property Engineer
  - - - links to wikipedia pages about the authors, in multiple languages
      - rich structured data about the authors
      - access to pictures from wikimedia commons, or quotations from wikiquote
      - links to multiple other authorities
- - - - Providing an in-depth and actionable introduction to Wikidata for the library community, including a method to get started. Like Wikipedia, Wikidata is both easy to use and complex to master.
      - Modeling the role of librarians as contributors as well as users of a global, largely volunteer network, by encouraging contribution of library services, workflows, tools, and software to the Wikidata community. This contribution can and should include research, development, and experimentation.
      - Identifying barriers for librarians and library staff to contribute, on a regular basis, to Wikidata/Wikibase. These barriers may include, among others, lack of sufficient time, skill, staff capacity, or institutional policy environment.
      - Creating a call for a community of practice around a metadata infrastructure for knowledge equity and diversity, with a focus on developing understanding of the culture and skills of contributing to a knowledge commons like Wikidata.
      - Elevating the visibility and accessibility in research libraries of linked open data in general, and Wikidata/Wikibase in particular as a decentralized, global, openly licensed linked data network for public good.
  - - - Ran from December 2017 through September 2018
      - 16 OCLC Member libraries participated
      - The objective was to evaluate a framework for creating and managing bibliographic and authority data as linked data entities and relationships
      - We used a federated instance of Wikibase because we wanted to:
        
        evaluate the software framework,
        
        control new and changing property entities,
        
        limit visibility of some participant data at their request.
- - - - “information,” infrastructure refers loosely to digital facilities and services usually associated with the internet: computational services, help desks, and data repositories to name a few.
      - Here we take infrastructure as a broad category referring to pervasive enabling resources in network form, and we argue that a theoretical understanding of infrastructure is crucial to its design, use, and maintenance. This understanding plays a critical role in associated fields such as informatics, library science, and new media – all fields that underpin communication in large-scale and long-term collaborative science. In our analysis we extend conventional understandings of infrastructure as “tubes and wires” to the technologies and organizations which enable knowledge work:
      - As Leigh Star has noted, infrastructure is relational: the daily work of one person is the infrastructure of another (Star & Ruhleder, 1996). Finally, we further open the conceptual umbrella of infrastructure to include the individuals – designers and developers, users and mediators, managers and administrators – in existing and emergent roles associated with information infrastructure.
      - An alternative vision of infrastructure may better take into account the social and organizational dimensions of infrastructure.
        It involves changing common views and metaphors on infrastructure: from transparency to vis- ibility, from substrate to substance, from short term to long term (Karasti, Baker, & Halkola, 2006; Star & Ruhleder, 1996).
    - - Star & Ruhleder’s now classic definition of infrastructure (Star & Ruhleder, 1996) originally composed for a paper on one of the early scientific collabora- tories, the Worm Community System. We show in Fig. 1 how their definitions can be ordered along two axes, one explicitly non-spatial and the other spatial: the social/technical and the individual/group or local/global.
        
        We think of infrastructures and their construction as distributions along these axes rather than as tensions between polar opposites. Our argument is an ecological one; it calls for investigating infrastructure building a set of distributed activities – technical, social and institutional.
        
        We cannot do the history of software without doing the history of their surrounding organizations.
        
        An everyday example comes from the problem of e-mail security. How do I dis- tribute my trust? I can delegate it to my machine, and deploy firewalls, password protection, and version controls. Or I can work socially and organizationally to make certain that members of my scientific community understand and police norms about data usage.
        
        What interoperability strategy best suits the design of shared databases with highly distributed and heterogeneous data sets? How to distribute work and responsibilities between databases, users, and institutions?
        
        Let us suppose the metadata standard approach was selected. Now how to dis- tribute between the local and the global, or between individual versus community needs? “An infrastructure occurs when the tension between local and global is resolved” (Star & Ruhleder, 1996, p. 114). While occurring, how are an infrastruc- ture’s qualities being distributed between the local and the global? For instance, to what extent is a metadata standard designed generic enough to represent a domain (“reach or scope”) while aiming at fitting local structures, social arrangements, and technologies (“embeddedness”)?
        
        For our purposes, cyberinfrastructure is the set of (1) organizational practices, (2) technical infrastructure, and (3) social norms that collectively provide for the smooth operation of scientific work at a distance (Latour, 1992; NSF CI Council, 2006).
        
        Being all three subjects to contingent distributive actions, these are objects of design and engineering; a cyberinfrastructure will fail if any one is ignored.
        
        Key to any new infrastructure is its ability to permit the distribution of action over space and time.
      - A more recent cyberinfrastructure vision document is similarly diffuse, though it regrettably somewhat sidelines the social and organizational in the definition1
        
        Cyberinfrastructure integrates hardware for computing, data and networks, digitally enabled sensors, observatories and experimental facilities, and an interoperable suite of software and middleware services and tools. Investments in interdisciplinary teams and cyberin- frastructure professionals with expertise in algorithm development, system operations, and applications development are also essential to exploit the full power of cyberinfrastructure to create, disseminate, and preserve scientific data, information, and knowledge. (NSF CI Council, 2006, p. 6)
      - Cyberinfrastructure projects come in many forms but they often seek to bring together, under a single umbrella, various domain sciences with novel information technologies.
      - The Long Now of Information Infrastructure
        
        For the development of cyberin- frastructure, the long now is about 200 years.
        
        two sets of changes began to occur in the organization of knowledge and the academies that have accompanied – slowly – the rise of an information infrastructure to support them:
        
        On the one hand is the exponential increase in information gathering activities by the state (statistics)
        
        on the other is the emergence of knowledge workers (the encyclopedists) and the accompanying development of technologies and organizational practices to sort, sift, and store information.
        
        organizations are (in part) information pro- cessors (Stinchcombe, 1990). People, routines, forms, and classification systems are as integral to information handling as computers, ethernet cables, and web protocols.
        
        The impacts of contemporary infrastructure are pervasive. In the “long now,” two key facets of scientific information infrastructures stand out:
        
        The 1st facet clusers on the nature of work in the social and natural sciences.
        
        Scientific disciplines were formed in the early 1800s, a time Michel Serres describes as the era of x-ology, where “x” was “geo,” “socio,” “bio,” and so forth (Serres, 1990). Auguste Comte classified the division of labor in the sciences, placing mathematics and physics as the most developed and best models and sociology as the most complex and least developed
        
        more or less where Norbert Wiener placed them 130 years later in Cybernetics and Society (Weiner, 1951). This was also the period during which the object we now call the database came to be the lynchpin of the natural and social sciences.
        
        Statistics etymologically refers to “state-istics,” or the quan- titative study of societies (states); it arose along with censuses, medical records, climatology, and other increasingly powerful techniques for monitoring population composition and health (Porter, 1986)
        
        Equally, the natural sciences – moved by the spirit of the encyclopedists – began creating vast repositories of data.
        
        The second facet clusters around scientists’ communication patterns.
        
        From the early nineteenth century a complex structure of national and international conferences and publishing practices developed, including in par- ticular the peer-reviewed scientific journal – at the same time as personal communi- cation networks continued to develop (Desmond & Moore, 1991)
        
        today’s scientific infrastructures must be understood as an outgrowth of these developments rather than ahistorically as “revolutionary” or “radical.
        
        However, as scientific infrastructure goes digital, we can also identify a genuine discontinuity.
    - - new forms of sociality
        
        This is the social dimension of the new infrastructure.
        
        Mediation
        
        In contrast to large-scale physical infrastructures that may be viewed as the responsibility of a business, city, or government, the internet transitioned from a research environment into an open-bazaar, committee-run entity and today is being redefined in a marketplace of policies and regulations.
        
        Infrastructure studies is best considered as “process building,” involving both community building and system building. An infrastructure studies “process-building” framework must take account of both the use of technology for automation and effective disintermediation as well as the increased need for support, for tending by growing numbers and varieties of mediation professionals.
        
        Process Building
        
        Because of the broad implications for information infrastructures, the sweep of information systems compares to that of governance systems. The need for participation holds the same import for information infrastructures as for democratic systems.
        
        If participants have been active in the formation of infras- tructure elements, they are more likely to have a deeper awareness of alternatives and have had a voice in mediating choices inherent to issues such as standards for- mation and community goals (Millerand & Bowker, 2009).
        
        Once in place, information procedures and standards become a general requirement or driver that facilitate development for some, that misrepresent or perhaps ignore some, and that potentially alienate others (Berg & Timmermans, 2000).
        
        One aspect of infrastructure studies inquiry is consideration of new types of roles evolving with the process of building information infrastructure – roles such as digital librarians, information managers, and network specialists.
        
        System Sustaining
        
        With technology-related resources, issues of change, redesign, and support arise with respect to hardware, software, networks, and human resources and relations as we gain experience with technology, its mediation, and its interfaces. Here, mediation encompasses relations between people, between machines, or between communities as well as a vast vari- ety of human–technology interfaces.
        
        We note that infras- tructure work frequently entails frequent and ongoing articulation work in order to enable continued functionality.
        
        Infrastructure in Time
        
        Infrastructure building involves alignment work involving different time scales. one of the chief challenges is to move beyond a 1–6 year funding cycle of projects or a 30-year career cycle of the scientist to create baselines of data spanning multiple decades.
        
        Aligning what is naturally misaligned (funding cycles, scientists career trajectories, ecosystem cycles) is fundamentally an issue of distribution between technologies, communities, organizations, institutions, and participating individuals.
        
        Understanding cyberinfrastructure building requires understanding the timescales for operation of its different components and how they are articulated so that processes have an apparent continuity across time.
        
        the designers, developers, deployers, enactors, and users of data and information are at different phases of technological interest, awareness, and/or skills (Millerand and Bowker, forthcoming). Thus an individual’s interface with others about technol- ogy or with the system itself at any one time presents a scenario of differing local arrangements, conceptual development, and individual understandings.
      - social, ethical and political values
        
        are being built into these self-effacing, self- perpetuating infrastructures.
        
        What kind of a thing is the internet?
        
        let us for a moment imagine it as a very large database, an outcome of the late eighteenth-century encyclopedist impulse to record all of the world’s knowledge and make it freely accessible.
        
        It was rendered possible by not only the development of electricity but also by the develop- ment across the nineteenth century of large-scale classification systems in any of a number of domains, of library classifications above all (the MARC record turns elec- tronic and then morphs into the Dublin Core).
        
        Now it is clear that how we arrange information in encyclopedias has social and political dimensions (do we look under “t” for “terrorist,” “f” for freedom fighter, or “i” for “insurgent,” for example?) cite. The art and science of classifica- tion plays an underappreciated but recurring role in the organization of information (Bowker & Star, 1999; Epstein, 2007).
        
        to date, the internet is inclusive. It is a “dataspace” into which we can add a growing variety of artifacts (Franklin, Halevy, & Maier, 2005).
        
        These design issues are playing out across the board in information infrastructure development as the problem of deciding what kind of objects to people your world with, how to describe data with metadata and how to build ontologies (Sowa, 2000).
        
        ontology-building specialists typically find that domain practitioners are not readily prepared for ontology build- ing.
        
        In short, the ontology specialist often finds that what partic- ipants in a domain consider their validated and structured knowledge is not readily compatible with ontology building.
        
        Ontology work is a quintessential act of distribution – taking knowledge out of a closed community of practice and allowing for its reuse and reshaping by others in different fields.
        
        ontologies are not static.
        
        ontology building is always coupled with the background work of identifying and informing a broader community of future ontology users.
        
        look at controversies around ethnic classification in the US census, debates around Wikipedia content and editing, or the classification system of the Library of Congress.
        
        However, they are also present across the range of natural sci- ences. As drug companies create databases of plants that may have medicinal value, they frequently relegate indigenous understanding of those plants to free text fields that are essentially useless – the political decision not to use local knowledge is enforced by a simple technical fix (Hayden, 2003; Latour, 1993)
        
        If a dominant sub- group within a given discipline gets to define the ontology of the database, they can reinforce their power
        
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      - Knowledge Work
        
        is changing with the introduction of new informa- tion technologies, modes of representation, and the accompanying shifts in work practice and systems for the accreditation of knowledge. This is the ontological dimension of the new infrastructure.
        
        techno hubris
        
        a wide range of cultural and organizational changes need to be made if the new infrastructure is going to bear fruit.
        
        We are dealing with a massively entrenched set of institutions, built around the last information age and fighting for its life.
        
        Much technological infrastructure develop- ment has been motivated by the belief that if we build it they will come.
        
        The methodological imperative becomes to treat “technical” dimensions of activity no differently than any other sociological object of interest: with appropriate access and determination of the researcher, human activity is fundamentally observable whether “scientific,” “lay,” or “technical.”
        
        Thus rather than the rhetoric of revolutionary fervor that permeates cyberinfras- tructure circles, infrastructure studies take as its object change at a much more mundane scale: as forms of practice, routine, or distributed cognition associated with knowledge work.
        
        Is this position against a possibility of “revolution?” Not at all. It is, rather, a research sensibility which seeks to make transformations of infrastructure visible relative to the everyday work of scientists, information technologists, or information managers.
        
        the new information infrastructure is fundamentally about distribution
        
        Now, over the past 200 years, our emergent information society has (gradually) moved to a further form of distribution where complex social, orga- nizational, and knowledge work can be practiced on a global scale, which leads to the question of how we study it.
    - - We need new images and mindsets. Only when we truly think the new technology can we use it to its fullest.
        
        “Infrastructure is an idea, a vision or an ideal, but it is also a practice, a commitment and a long term endeavor” (Ribes, 2006, p. 299).
      - The fields which have contributed to this new science include, but are not lim- ited to, computer science, information science, communication, organization theory, cognitive science, and science and technology studies.
        
        there is a fundamental scope and unity to the field that reflects the very nature of the new infrastructure – one in which the global and the local, the social and the technical are in flux in new and interesting ways for the first time in 500 years. The decisions we make over the next 50 years will have very long-term consequences.
      - We conclude with a few themes native to this emerging field that must be addressed in order to move the field to the next level:
        
        research organization
        
        we need new models for scalable qualitative research: is the appropriate sized research team more of the order of 15–20 rather than the 0–2 typical of many programs?
        
        One way to look at this is in terms of Ross Ashby’s Law of Requisite Variety (Ashby, 1956): we need as much span in our research teams as there is in the phenomena we are studying.
        
        multi-modal research
        
        For example, from social network analysis we have a semi-quantitative approach that brings together a great deal of data but which typi- cally ignores domain content, meanings, and practice.
        
        an integrative view
        
        we need to move beyond seeing the social, organizational, and cognitive sitting somehow on top of or beside the wires and gateways of the physical infrastructure.
        
        Each layer is riven through with each of these dimensions – and we need to train social scientists and information scientists to move freely between all of them.
        
        we need to be sensitive to the devel- opment and spread of new ways of knowing across information infrastructures.
        
        The development of information infrastructures facilitates the viral spread of ontologies and classifications
        
        To the extent that we train social scientists to look within particular fields or projects, we are missing one of the most interesting features of information infrastructures: their ability to promote combined (if uneven) development across a very broad range of work practices.
        
        We are convinced that we are in the midst of developing fundamentally new ways of knowing – though we would put this in a 200 year span rather than a
        
        Information infrastructure is a great tool for distribution of knowledge, culture, and practice. Homesteading the space it has slowly opened out over the past two cen- turies involves building new kinds of community, new kinds of disciplinary homes, and new understandings of ourselves.