Category Archives: ontologies
Edit: slides
On Thursday, I will be giving a talk at the “The Lived Logics of Database Machinery” workshop, organized by computational culture, which will take place at the Wellcome Collection Conference Centre in London, from 10h to 17h30. I am very much looking forward to this, although I’ll be missing a couple of days from the currently ongoing DMI summer school. This is what I will be talking about:
ORDER BY column_name. The Relational Database as Pervasive Cultural Form
This contribution starts from the observation that, in a way similar to the computational equivalence of programming languages, the major types of database models (network, relational, object-oriented, etc.) and implementations are all able to store and manage a very large variety of data structures. This means that most data structures could be modeled, in one way or another, in almost any existing database system. So why have there been so many intense debates about how to conceive and build database systems? Just like with programming languages, the specific way a database system embeds an abstract concept in a set of concrete methods and mechanisms for specifying, accessing, and manipulating datasets is significant. Different database models and implementations imply different ways of “thinking” data organization, they vary in performance, robustness, and “logistics” (one of the reasons why Oracle’s product succeeded well in the enterprise sector in the 1980s, despite its lack of certain features, was the ability to make backups of a running database), and they provide different modes of interaction with both the data and the system.
The central vector of differentiation, however, is the question how users “see” the data: during the “database debates” of the 1970s and 1980s the idea of the database as a set of tables (relational model) was put in opposition to the vision of the database as a network of records (network model). The difference between the two concerned not only performance, flexibility, and complexity, but also the crucial question who the users of these systems would be in the first place. The supporters of the network model clearly saw the programmer as the target audience for database systems but the promoters of the much simpler relational model and its variants imagined “accountants, engineers, architects, and urban planners” (Chamberlin and Boyce 1974) to directly interact with data by means of a simple query language. While this vision has not played out, according to Michael Stonebreaker’s famous observation, SQL (the most popular, albeit impure implementation of Codd’s relational ideas) has indeed become “intergalactic data-speak” (most packages on the market provide SQL interfaces) and this standardization has strongly facilitated the penetration of database systems into all corners of society and contributed to a widespread “relational view” of data organization and manipulation, even if data modeling is still mostly in expert hands.
The goal of this contribution is to examine this “relational view” in terms of what Jack Goody called the “modes of thought” associated with writing, and in particular with the list form, which “encourages the ordering of the items, by number, by initial sound, by category, etc.” (Goody 1977). As with most modern technologies, the relational model implies a complex set of constraining and enabling elements. The basic structural unit, the “relation” (what most people would simply call a table) disciplines data modeling practices into logical consistency (tables only accept tuples/rows with the same attributes) while remaining “semantically impoverished” (Stonebreaker 1993). Heterogeneity is purged from the relational model on the level of modeling, especially if compared to navigational approaches (e.g. XPath or DOM), but the “set-at-a-time” retrieval concept, combined with a declarative query language, affords remarkable flexibility and expressiveness on the level of data selection. The relational view thus implies an “ontology” consisting of regular, uniform, and only loosely connected objects that can be ordered in a potentially unlimited number of ways at the time of retrieval (by means of the query language, i.e. without having to program explicit retrieval routines). In this sense, the relational model perfectly fits the qualities that Callon and Muniesa (2005) attribute to “powerful” calculative agency: handle a long list of diverse entities, keep the space of possible classifications and reclassifications largely open, multiply possible hierarchies and classifications. What database systems then do, is bridging the gap between these calculative capacities and other forms of agency by relating them to different forms of performativity (e.g., in SQL speak, to SELECT, TRIGGER, and VIEW).
While the relational model’s simplicity has led to many efforts to extend or replace it in certain application areas, its near universal uptake in business and government means that the logistics of knowledge and ordering implied by the relational ontology resonate through the technological layers and database schemas into the domains of management, governance, and everyday practices.
I will argue that the vision of the “programmer as navigator” trough a database (Bachman 1973) has, in fact, given way to a setting where database consultants, analysts, and modelers sit between software engineering on the one side and management on the other, (re)defining procedures and practices in terms of the relational model. Especially in business and government sectors, central forms of management and evaluation (reporting, different forms of data analysis, but also reasoning in terms of key performance indicators and, more generally, “evidence based” management) are directly related to the technological and cognitive standardization effects derived from the pervasiveness of relational databases. At the risk of overstretching my argument, I would like to propose that Thrift’s (2005) “knowing capitalism” indeed knows (largely) in terms of the relational model.
Last week Google introduced its new, ontology driven infobox and I have just been included into the roll-out. The whole knowledge modelling thing is quite a slippery slope though. Just compare these two boxes:
I don’t know about you, but I guess that Hitler l’auteur is perhaps not the most fitting template.
Yesterday, Google introduced a new feature, which represents a substantial extension to how their search engine presents information and marks a significant departure from some of the principles that have underpinned their conceptual and technological approach since 1998. The “knowledge graph” basically adds a layer to the search engine that is based on formal knowledge modelling rather than word statistics (relevance measures) and link analysis (authority measures). As the title of the post on Google’s search blog aptly points out, the new features work by searching “things not strings”, because what they call the knowledge graph is simply a – very large – ontology, a formal description of objects in the world. Unfortunately, the roll-out is progressive and I have not yet been able to access the new features, but the descriptions, pictures, and video paint a rather clear picture of what product manager Johanna Wright calls the move “from an information engine to a knowledge engine”. In terms of the DIKW model (Data-Information-Knowledge-Wisdom), the new feature proposes to move up a layer by adding a box of factual information on a recognized object (the examples Google uses are the Taj Mahal, Marie Curie, Matt Groening, etc.) next to the search results. From the presentation, we can gather that the 500 million objects already referenced will include a large variety of things, such as movies, events, organizations, ideas, and so on.
This is really a very significant extension to the current logic and although we’ll need more time to try things out and get a better understanding of what this actually means, there are a couple of things that we can already single out:
- On a feature level, the fact box brings Google closer to “knowledge engines” such as Wolfram Alpha and as we learn from the explanatory video, this explicitly includes semantic or computational queries, such as “how many women won the Nobel Prize?” type of questions.
- If we consider Wikipedia to be a similar “description layer”, the fact box can also be seen as a competitor to everybody’s favorite encyclopedia, which is a further step into the direction of bringing information directly to the surface of the results page instead of simply referring to a location. This means that users do not have to leave the Google garden to find a quick answer. It will be interesting to see whether this will actually show up in Wikipedia traffic stats.
- The introduction of an ontology layer is a significant departure from the largely statistical and graph theoretical methods favored by Google in the past. While features based on knowledge modelling have proliferated around the margins (e.g. in Google Maps and Local Search), the company is now bringing them to the center stage. From what I understand, the selection of “facts” to display will be largely driven by user statistics but the facts themselves come from places like Freebase, which Google bought in 2010. While large scale ontologies were prohibitive in the past, a combination of the availability of crowd-sourced databases (Wikipedia, etc.), the open data movement, better knowledge extraction mechanisms, and simply the resources to hire people to do manual repairs has apparently made them a viable option for a company of Google’s size.
- Competing with the dominant search engine has just become a lot harder (again). If users like the new feature, the threshold for market entry moves up because this is not a trivial technical gimmick that can be easily replicated.
- The knowledge graph will most certainly spread out into many other services (it’s already implemented in the new Google Docs research bar), further boosting the company’s economies of scale and enhancing cross-navigation between the different services.
- If the fact box – and the features that may follow – becomes a pervasive and popular feature, Google’s participation in making information and knowledge accessible, in defining its shape, scope, and relevance, will be further extended. This is a reason to worry a bit more, not because the Google tools as such are a danger, but simply because of the levels of institutional and economic concentration the Internet has enabled. The company has become what Michel Callon calls an “obligatory passage point” in our relation to the Web and beyond; the knowledge graph has the potential to exacerbate the situation even further.
This is a development that looks like another element in the war for dominance on the Web that is currently fought at a frenetic pace. Since the introduction of actions into Facebook’s social graph, it has become clear that approaches based on ontologies and concept modelling will play an increasing role in this. In a world mediated by screens, the technological control of meaning – the one true metamedium – is the new battleground. I guess that this is not what Berners-Lee had in mind for the Semantic Web…
In the beginning, it was all about the algorithm. PageRank and its “no humans involved” mantra dominated Google since its inception. In recent years however, Google has started to expand the role of “conceptual” knowledge in different areas of its services. The main search bar and its capacity to do all kinds of little tricks is a good example, but I was really quite astounded how seamless concept integration has become on my last trip to Google Translate:
