The _Journal of Folklore Research_ has posted a [review][] of George E. Lankford’s _Reachable Stars: Patterns in the Ethnoastronomy of Eastern North America_ (University of Alabama Press, 2007). Here’s the lead paragraph:

> For millennia, humans everywhere have created a diverse body of imaginative narratives and images to make sense of the night sky’s canopy of stars. In _Reachable Stars_, folklorist-anthropologist George Lankford explores the ways in which North Americans have attempted to find patterns and meaning in the mysterious lights in the sky, from myriad points of light in the Milky Way to the imaginary pictures we call constellations. Lankford’s ambitious and masterful study is marked by breadth, impressive research, and a purposeful, conversational writing style. A valuable contribution to folklore studies, as significant for its approach as for its content, this volume should also appeal to anthropologists, Native American scholars, historians of science, geomythologists, ethnologists, and scholars of archaeoastronomy. Lankford succeeds in writing for “any reader with an enthusiasm for the night sky and human ways of thinking about it” (19).


Our Ontological Future

Recently, the [National Institute for Standards and Technology][nist] hosted a conference to establish a word/logic bank for thinking machines. Out of that conference came an agreement:

> Information scientists announced an agreement last month on a “concept bank” programmers could use to build thinking machines that reason about complex problems at the frontiers of knowledge from advanced manufacturing to biomedicine.

> The agreement by ontologists — experts in word meanings and in using appropriate words to build actionable machine commands — outlined the critical functions of such a bank. It was reached at a two-day Ontology Summit held during NIST’s Interoperability Week in Gaithersburg, Md. The decision to create a unique Internet facility called the Open Ontology Repository (OOR) culminated more than three months of Internet discussion.
(Quote taken from [Science Blog report][sb]. The OOR proposal is [here][oor].)

When I was an undergraduate in college, I was both an English and Philosophy major. (I know, what hope for me, eh?) Studying philosophy in the 1980s, before the rise of artificial intelligence (AI), *ontology* meant only one thing: the study of existence to determine what entities (we called them *phenomena*) were present, what categories (or types) into which those entities prevailed, and the relationships between entities.

With the rise of AI, there has been a need to re-use ontology with a different vector: *ontology* can also be *a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents*. So far as I know, Tom Gruber and his colleagues were the first to re-use *ontology* in this way. Their use is not as far from the philosophical usage as they might believe: their goal is to establish a set of concept definitions expressly for knowledge sharing and re-use. To my mind, such a project isn’t that far from what philosophers were doing, especially within the phenomenological tradition. Their goal, at least in my reading of Heidegger and Bachelard and others, was a kind of concise mapping of the universe as humans understood it in order to understand the very principles of human understanding. (Levi-Strauss’ *structuralism* operated in much the same manner. Again, to my mind, which may now be proving itself divergent and/or errant.)

The point, for Gruber *et al.*, is that one commits to an ontology — their term is in fact “ontological commitment” — in order to create agents that can then engage in knowledge sharing. There were several levels (layers, dimensions) to what Project Bamboo participants aspired to, but one was definitely at the deep infrastructural level of *meta-data*. One of the groups in which I participated was tasked with the job of teasing out the notion of *foraging* which was something that the larger group perceived as being a *commonality* among humanities practitioners. We go out. We search for data. Faced with the forest of data to be found in libraries, which really do feel like being lost in the woods sometimes (in a good way), and on-line, we forage. Sometimes we find what we wanted. Sometimes we find not the berries we were looking for, but a root which is even better. That is the nature of foraging. All of us, however, yearn for better breadcrumbs through our proverbial forests. Better search devices would seem to be a key to better, more efficient searches — though one does have to wonder if efficiency, within the humanities paradigm, doesn’t also lead to impoverishment. Building better searches would seem to be founded on not only the data being accessible, which is one of the shiniest promises of the digital age, but also the data being searchable. Often what we want to know about something isn’t contained within the object itself. Take, for instance, a digital audio file of a performance by Varise Conner of his “Lake Arthur Stomp.” Nothing in the file itself will tell you the name of the tune — there are no words, no refrain. Nothing will tell you who originated the song or who is playing it now, unless you can recognize the tune and/or the style of its performance. Or that it’s a melody in the Cajun repertoire. Or that its author was of Irish descent. Or that he lived in Vermilion parish. Or that he was also a sawyer. None of this is the data itself. It’s all **meta-data** and it turns out that meta-data is sometimes more important than the data itself, especially when it comes to finding the data. The problem is committing to a meta-data set. Some of you live in places where there is a university library, which usually adheres to the Library of Congress call system, and a local public library, many of which still use the Dewey Decimal system. It’s not as easy as switching gears from letters to numbers, from PS to 800. The ordering of entities and their groupings are different. Philosophy, for example, occupies a different place and is near different things in the two systems. And that’s just to catalog — in order to house and then to find* — books and other printed materials. What do you do with other kinds of objects? Especially objects that will never actually be housed in a physical facility? (We begin to border on an infinite regression here, since what we are dealing with is housing data about objects which, it turns out, is really meta-data itself. Oh. My.) But that is the grail that humanists seek, because we really would like to get as much of the human universe into a form that is searchable and accessible. Why is that important? Well, precisely because so much human activity still lies outside the scope of libraries and archives. And that not only includes the majority of humanity on this planet, but the majority of the lives of even the hyper-connected. One could easily argue that any assessment of what humans are based on what is currently available is really only a small part of the story. And we’re looking to tell a big story. * I am the father of a toddler, after all, and so the idea of putting things away in a place where you can later find them is central to my existence.


Updating Gems

I wasn’t sure if I had ever updated Rails on my MBP, and so, as I begin developing my first real project, I thought it was time. A regular `gem update` didn’t work. I had to use `sudo`:

sudo gem update rails –include-dependencies

The same applied for `gem cleanup`, which deleted the following items — after asking me for confirmation:

Successfully uninstalled rails-1.2.6
Successfully uninstalled rake-0.7.3
Successfully uninstalled actionwebservice-1.2.3
Successfully uninstalled activerecord-1.15.3
Successfully uninstalled actionmailer-1.3.3
Successfully uninstalled actionpack-1.13.3
Successfully uninstalled activesupport-1.4.2

I now have to do the same on the iMac.

Problem Space

In problem solving, the **problem space** is the set of all possible operations that can be performed in an attempt to reach a solution. The idea is credited, at least in one place, to A. Newell, who defined the *problem space principle* as “The rational activity in which people engage to solve a problem can be described in terms of (1) a set of states of knowledge, (2) operators for changing one state into another, (3) constraints on applying operators and (4) control knowledge for deciding which operator to apply next.”


From Tom Gruber’s web page:

> Short answer: an ontology is a specification of a conceptualization.

> The word “ontology” seems to generate a lot of controversy in discussions about AI. It has a long history in philosophy, in which it refers to the subject of existence. It is also often confused with epistemology, which is about knowledge and knowing.

> In the context of knowledge sharing, I use the term ontology to mean a specification of a conceptualization. That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set-of-concept-definitions, but more general. And it is certainly a different sense of the word than its use in philosophy.

> What is important is what an ontology is for. My colleagues and I have been designing ontologies for the purpose of enabling knowledge sharing and reuse. In that context, an ontology is a specification used for making ontological commitments. The formal definition of ontological commitment is given below. For pragmetic reasons, we choose to write an ontology as a set of definitions of formal vocabulary. Although this isn’t the only way to specify a conceptualization, it has some nice properties for knowledge sharing among AI software (e.g., semantics independent of reader and context). Practically, an ontological commitment is an agreement to use a vocabulary (i.e., ask queries and make assertions) in a way that is consistent (but not complete) with respect to the theory specified by an ontology. We build agents that commit to ontologies. We design ontologies so we can share knowledge with and among these agents.

> This definition is given in the article: > T. R. Gruber. A translation approach to portable ontologies. _Knowledge Acquisition_ 5(2):199-220, 1993. Available [on line][]. A more detailed description is given in T. R. Gruber. 1995. Toward principles for the design of ontologies used for knowledge sharing. Presented at the Padua workshop on Formal Ontology, March 1993, later published in _International Journal of Human-Computer Studies_ 43(4-5): 907-928. Available [online][].

### Ontologies as a specification mechanism

A body of formally represented knowledge is based on a conceptualization: the objects, concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among them (Genesereth & Nilsson, 1987) . A conceptualization is an abstract, simplified view of the world that we wish to represent for some purpose. Every knowledge base, knowledge-based system, or knowledge-level agent is committed to some conceptualization, explicitly or implicitly. An ontology is an explicit specification of a conceptualization. The term is borrowed from philosophy, where an Ontology is a systematic account of Existence. For AI systems, what “exists” is that which can be represented. When the knowledge of a domain is represented in a declarative formalism, the set of objects that can be represented is called the universe of discourse. This set of objects, and the describable relationships among them, are reflected in the representational vocabulary with which a knowledge-based program represents knowledge. Thus, in the context of AI, we can describe the ontology of a program by defining a set of representational terms. In such an ontology, definitions associate the names of entities in the universe of discourse (e.g., classes, relations, functions, or other objects) with human-readable text describing what the names mean, and formal axioms that constrain the interpretation and well-formed use of these terms. Formally, an ontology is the statement of a logical theory.[^1] We use common ontologies to describe ontological commitments for a set of agents so that they can communicate about a domain of discourse without necessarily operating on a globally shared theory. We say that an agent commits to an ontology if its observable actions are consistent with the definitions in the ontology. The idea of ontological commitments is based on the Knowledge-Level perspective (Newell, 1982) . The Knowledge Level is a level of description of the knowledge of an agent that is independent of the symbol-level representation used internally by the agent. Knowledge is attributed to agents by observing their actions; an agent “knows” something if it acts as if it had the information and is acting rationally to achieve its goals. The “actions” of agents—including knowledge base servers and knowledge-based systems— can be seen through a tell and ask functional interface (Levesque, 1984) , where a client interacts with an agent by making logical assertions (tell), and posing queries (ask). Pragmatically, a common ontology defines the vocabulary with which queries and assertions are exchanged among agents. Ontological commitments are agreements to use the shared vocabulary in a coherent and consistent manner. The agents sharing a vocabulary need not share a knowledge base; each knows things the other does not, and an agent that commits to an ontology is not required to answer all queries that can be formulated in the shared vocabulary. In short, a commitment to a common ontology is a guarantee of consistency, but not completeness, with respect to queries and assertions using the vocabulary defined in the ontology.

[^1]: Ontologies are often equated with taxonomic hierarchies of classes, but class definitions, and the subsumption relation, but ontologies need not be limited to these forms. Ontologies are also not limited to conservative definitions, that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world (Enderton, 1972) . To specify a conceptualization one needs to state axioms that do constrain the possible interpretations for the defined terms.