[Humanist] 28.843 an argument & belief system

Humanist Discussion Group willard.mccarty at mccarty.org.uk
Mon Mar 23 07:12:59 CET 2015

                 Humanist Discussion Group, Vol. 28, No. 843.
            Department of Digital Humanities, King's College London
                Submit to: humanist at lists.digitalhumanities.org

        Date: Sun, 22 Mar 2015 09:59:49 +0000
        From: Martin Mueller <martinmueller at northwestern.edu>
        Subject: Re:  28.842 an arguent & belief system
        In-Reply-To: <20150322072610.046D0C2F at digitalhumanities.org>

Many thanks for this very full explanation. What about an example
demonstrating the practice. Take a much commented text like Sonnet 116
"Let me not to the marriage of true minds," a text that like Shakespeare's
other famous sonnets has a lot of stuff (from planets to cosmic junk)
revolving around its sun. Would it ever help a literary critic make more
sense of it?

Martin Mueller
Professor emeritus of English and Classics
Northwestern University

On 3/22/15, 08:26, "Humanist Discussion Group"
<willard.mccarty at mccarty.org.uk> wrote:

>                 Humanist Discussion Group, Vol. 28, No. 842.
>            Department of Digital Humanities, King's College London
>                       www.digitalhumanities.org/humanist
>                Submit to: humanist at lists.digitalhumanities.org
>        Date: Sat, 21 Mar 2015 09:07:08 +0000
>        From: Dominic Oldman <doint at oldman.me.uk>
>        Subject: Re:  28.819 an argument & belief system
>        In-Reply-To: <20150315072307.9A021C29 at digitalhumanities.org>
>Dear Martin,
>I have put together some general notes together. I hope they are helpful.
>CRMInf is a specialisation of the core CIDOC CRM ontology. The CRM
>provides a set of entities and relationships with precise real world
>scientific definitions that provide generalisations designed to harmonise
>structured data. It does not mandate fields and values and supports any
>structured information retaining its original meaning and perspectives.
>It is event based.
>The Core CRM ontology provides the foundational classes and properties
>and is extremely robust to ensure that the ontology cannot be broken, to
>integrate different levels of knowledge, and support a wide range of
>potential specialisations derived from core classes. Extensions as
>opposed to specializations are increasingly rare.
>CRMInf is a specialisation of the CRM derived from the abstraction of
>different argumentation theories. See
>http://dl.acm.org/citation.cfm?id=1921615. It is also worth saying up
>front that CRMInf is closely aligned with CRMSci which is a scientific
>observation extension of the CRM.
>The argumentation system currently works on the basis of three types of
>1.    Observation,
>2.    Inference and
>3.    Belief adoption.
>The full reference and scope notes of all the labels can be found in the
>CRMInf draft athttp://www.ics.forth.gr/isl/index_main.php?l=e&c=713
>These arguments operate on beliefs which have proposition sets.
>For example, in my domain an artefact has things written about it by the
>museum that physically owns it. These things are, in fact, part of an
>argument based on observation, since the museum owns and has access to an
>object in order to create documentation. However, anyone physically
>observing an object themselves can equally make an observation (an
>argument) about it. All beliefs can be attributed and other information
>is added that would allow a logical assessment of the argument.
>Museum documentation may take the form of a database record and this
>record is a proposition. Within that proposition there may be things that
>people agree or disagree with, in which case they can adopt (belief
>adoption) parts of the proposition and challenge others (using any of the
>argument types). They may use the beliefs, along with beliefs collected
>from other sources in the community (and this is crucial), perhaps a
>citation or a different type of data resource, to construct an opposing
>argument. It is possible of course that the museum may, in time, adopt
>the arguments of researchers who have challenged it ­ in which case they
>would adopt the whole provenance of the arguments behind that belief.
>Another example, might be an interpretation. For example, an
>interpretation of a painting, perhaps from an owning institution, may
>form a belief or set of beliefs that can be challenged by someone else.
>These new beliefs could be used as premises and may include historical
>information that provides context for the argument, e.g. facts about the
>people and things depicted or more general historical facts from the
>period or place. This is information that can be adopted from many
>different sources to develop a conclusion that is also in the form of a
>belief ­ a new belief. Equally others may use one or more of these
>beliefs (adoption), as well as their own, and formulate their own
>arguments, all of which are connected and eventually lead back to the
>original premise or belief again forming a provenance of different
>Other examples recently used come from archaeology, for example using
>logic about the way different context (stratigraphic) can support an
>hypothesis about a find.
>No honest argument or belief is out of scope and many different types of
>inference logic can be used (philosophical, mathematical or other kinds
>of evidence), although some logic might be more weighty than others. This
>means that arguments are not lost and gaps do not develop over time that
>might lead to wrong conclusions. It also means that these arguments can
>be reasoned over using computers.
>Anyway, taking the very simple initial example using the CRM ontology. A
>database record is a type of Information object (an E31 Document in CRM
>terms) ­ a document is a special type of information object and can be
>harmonised with other documents or indeed other information objects. For
>example, an information object is a special type of symbolic or
>propositional object which in turn are conceptual objects. I add this as
>an aside because CIDOC CRM is a poly-hierarchical system of meaning
>allowing different levels of knowledge to be integrated because, of
>course, people have different levels of knowledge which could not be
>integrated otherwise!
>In CRMInf this particular type of information object is called a
>proposition set (I4 Proposition Set). It is therefore both a document and
>a proposition set. A proposition is also a type of information object
>and, ³comprises identifiable symbols and any aggregation of symbols².
>In other words it can be text, images, musical scores, emblems, signs,
>and so on. This is important because it means that different types of
>information, heterogeneous data, can be connected without conversion to a
>single knowledge representation system. However, the CRM itself is about
>information/knowledge harmonisation and CRMInf becomes extremely powerful
>when the information that it operates on is encoded in the same knowledge
>representation system - but CRMInf is not limited by doing this first.
>This proposition forms the basis of a belief. A belief consists of a
>proposition and a belief value. The value is whatever is appropriate. In
>this case let¹s say, True, False, Unknown. The museums belief was created
>as a result of an observation.
>If someone was working with an image of an object then the proposition
>that they start with is different and based on the production of that
>image which was part of a previous observation.  This is an important
>distinction and may affect the way that an argument is perceived and
>weighted ­ particularly internet research in which people sometimes talk
>about an object as if they have physically examined it.
>An inference making argument is different in that it can use beliefs that
>may have resulted from observations or adoptions. Inference making
>usually uses as a premise an existing belief and can, with other premises
>conclude a new belief. Belief adoption takes an existing belief and makes
>it yours. The adoption concludes in your own belief.
>In CIDOC CRM termsŠ.
>S4 Observation -> J2 concluded that -> I2 Belief -> J4 that -> I4
>Proposition set AND J5 holds to be -> I6 Belief Value. (will stop at
>belief after this)
>e.g. An Observation activity/event  (argument) concluded a belief that is
>made up of a proposition and a value.
>I5 Inference Making ->   J1 used as a premise -> I2 Belief
>e.g. An Inference making activity/event (argument) used a belief as a
>I7 Belief Adoption -> J6 Adopted -> I2 Belief
>e.g. A belief adoption activity/event resulted in the adoption of an
>existing belief 
>I7 Belief Adoption -> J2 concluded that -> I2 Belief
>e.g., The belief adoption activity/event therefore concluded in a belief
>I5 Inference making -> J3 applies -> I3 Inference logic
>e.g. The inference making activity/event applied a logic
>I5 Inference making -> J2 concluded that -> I2 Belief
>e.g. The inference making activity/event concluded in a new belief
>In other words. I can take a belief and use it as a premise. I can also
>use my own beliefs derived from my own observations and adopt other
>beliefs from different sources of information and make these my beliefs.
>I can then through the activity of inference making apply a logic from
>which I conclude, most likely, a set of beliefs that negate some of the
>original premises (beliefs) and propose new beliefs.
>Anyway, we are currently in the middle of incorporating this into a
>software application. I will put the designs up on the Web soon.

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