Information is costly to manipulate and retrieve. By finding the pattern, the logic of the series, you no longer need to memorize it all. You just store the pattern. And, as we can see here, a pattern is obviously more compact than raw information. We have a hunger for rules because we need to reduce the dimension of matters so they can get into our heads. A novel, a story, a myth, or a tale, all have the same function: they spare us from the complexity of the world. They help build in our mind an idea. And that’s what true narratives do. They don’t just paint pictures they build structures in our mind upon which logic is built.

The following materials have been suggested by participants in the workshop, and have been organized into rough groupings for ease of navigation. This is not intended as a comprehensive list of readings on data modeling in the humanities, but (at the moment) reflects the textual emphasis of the workshop.

The classic "geological time scale" is a hierarchical ordinal system, in which the eras are ranked: "stages" nest within "series" within "systems" within "eras" within "eons" (in the most common version of the ranking system).

The time positions of the start and end points or geological eras are not known precisely, except for most of the Precambrian, where the boundaries are defined chronometrically. Typically there will be a number of estimates available, based on dating specimens retrieved from particular localities believed to correspond to the boundary of interest. A locality ratified by ICS is known as a "Global Boundary Stratotype Section and Point (GSSP)" and is indicated by a "golden spike" on the chart.

The complete definition of a geological time scale requires a description of the hierarchical structure of named eras the temporal positions of the boundaries between the eras.

Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and data analysis for spatial and spatio-temporal data. Starting with overviews of the types of spatial data, the data analysis tools appropriate for each, and a brief review of the Bayesian approach to statistics, the authors discuss hierarchical modeling for univariate spatial response data, including Bayesian kriging and lattice (areal data) modeling. They then consider the problem of spatially misaligned data, methods for handling multivariate spatial responses, spatio-temporal models, and spatial survival models. The final chapter explores a variety of special topics, including spatially varying coefficient models.

... any idea of what an instance could be?

Looking closely at http://id.loc.gov/vocabulary/iso639-1/fr for the first
time seriously (shame on me, can't even tell since when this URI has been
available) ...
I read that it is a *rdfs:subClassOf*
http://id.loc.gov/vocabulary/iso639-1/iso639-1_Language

Well, why isn't it an *instance* of this class?
I can see the rationale : there is not "one" French language, one can
imagine further subclasses such as Canadian French, Middle-Age French etc.
so French is a class of languages OK.
But are there any subclasses of French defined at id.loc.gov ?

And if it were the case, where do one stop the subclasses recursion and
introduce instances, if any? Is it turtles all the way down?

Model theory began with the study of formal languages and their interpretations, and of the kinds of classification that a particular formal language can make. Mainstream model theory is now a sophisticated branch of mathematics (see the entry on first-order model theory). But in a broader sense, model theory is the study of the interpretation of any language, formal or natural, by means of set-theoretic structures, with Alfred Tarski's truth definition as a paradigm. In this broader sense, model theory meets philosophy at several points, for example in the theory of logical consequence and in the semantics of natural languages.

How do we explore? If you move to a new city, you might learn the territory by walking around. Or you might peruse a map. But far more effective than either is both together — a street-level experience with higher-level guidance.

Likewise, the most powerful way to gain insight into a system is by moving between levels of abstraction. Many designers do this instinctively. But it's easy to get stuck on the ground, experiencing concrete systems with no higher-level view. It's also easy to get stuck in the clouds, working entirely with abstract equations or aggregate statistics.

This interactive essay presents the ladder of abstraction, a technique for thinking explicitly about these levels, so a designer can move among them consciously and confidently.

The reactive document integrates spreadsheet-like models into authored text. It can be read at multiple levels, depending on the reader's level of interest. The hurried reader can skim it. The casual reader can read it as-is. The curious reader can adjust the author's scenarios. The engaged reader can explore scenarios of his own devising.

This paper provides a summary of 35 years of data model proposals, grouped into 9
different eras. We discuss the proposals of each era, and show that there are only a few
basic data modeling ideas, and most have been around a long time. Later proposals
inevitably bear a strong resemblance to certain earlier proposals. Hence, it is a
worthwhile exercise to study previous proposals.

In recent years, researchers in several communities involved in aspects of the web have begun to realise the potential benefits of assigning an important role to events in the representation and organisation of knowledge and media. While a good deal of relevant research has been done in the semantic web community (for example on the modeling of events), a lot of complementary research has been done in other communities, such as multimedia processing and information retrieval. The goal of this workshop is to advance research on this general topic within the semantic web community, by both building on existing semantic web work and integrating results and methods from other areas, with a particular focus on issues that are central to the semantic web.

With the proliferation of citizen reporting, smart mobile devices, and social media, an increasing number of people are beginning to generate information about events they observe and participate in. A significant fraction of this information contains multimedia data to share the experience with their audience. A systematic information modeling and management framework is necessary to capture this widely heterogeneous, schemaless, potentially humongous information produced by many different people. This book is an attempt to examine the modeling, storage, querying, and applications of such an event management system in a holistic manner. It uses a semantic-web style graph-based view of events, and shows how this event model, together with its query facility, can be used toward emerging applications like semi-automated storytelling.

Narratives are ubiquitous in human experience. We use them to communicate, convince, explain, and entertain. As far as we know, every society in the world has narratives, which suggests they are rooted in our psychology and serve an important cognitive function. It is becoming increasingly clear that, to truly understand and explain human intelligence, beliefs, and behaviors, we will have to understand why narrative is universal and explain (or explain away) the function it serves. The aim of this workshop series is to address key, fundamental questions about narrative, using computational techniques, so to advance our understanding of cognition, culture, and society.

This course is a graduate level introduction to automatic discourse processing. The emphasis will be on methods and models that have applicability to natural language and speech processing.

The class will cover the following topics: discourse structure, models of coherence and cohesion, plan recognition algorithms, and text segmentation. We will study symbolic as well as machine learning methods for discourse analysis. We will also discuss the use of these methods in a variety of applications ranging from dialogue systems to automatic essay writing.

Temporal information is one of the essential components in many domains, especially those related to history. Up until the twentieth century, the Chinese used a lunisolar calendar with the title of an Emperor and a reign period to express temporal information. When describing a historical event in Chinese history, it is inadequate to use existing time ontologies as presented in the traditional Chinese way of thinking to capture and encode time. To date, no attention in the field has been given to modelling ancient Chinese time. In this paper, we identify the problems encountered when modelling Chinese time resulting from the distinctive nature of a non-western time scale. We design a new model of temporal information with combined approaches, which are more appropriate for Chinese dynasties, emperors, and reign periods, and apply the OWL-Time ontology onto the ancient Chinese lunisolar calendar. This approach can also be applied to other ancient time-keeping methods in non-western time scales.

We generalise the notion of a bubble beyond the financial domain, by showing how a single social mechanism, based on an information feedback-loop, explains both financial bubbles and other seemingly disparate social phenomena, such as the recognition of academic articles, website popularity, and the spread of rumours.
We discuss examples of phenomena explained by this bubble mechanism, as well as other phenomena that exhibit certain bubble characteristics, yet are not bubbles according to our model. Finally, we present mathematical mechanisms for two phenomena that conform with our model, and show by computer simulation how they exhibit bubble behaviour.

"As Bruno Bachimont uses to say, an ontology is mainly a tool to explicit inconsistencies of our knowledge, pointing to new questions for research. After that, you can throw it away."

Because of the recent, shared emphasis in many fields on digital methods, scholars in the humanities, arts, social sciences, and sciences increasingly need to collaborate across disciplines. This course reflects theoretically and practically on the new digitally facilitated interdisciplinarity by asking students to choose a literary work and treat it according to one or more of the research paradigms prevalent in other fields of study.

Class and ObjectProperty punning might be convenient for when one wants to model attributes of relations themselves.