Archive for the ‘ontology’ Category

Legal Case Ontology OWL file and Case Graphic

Wednesday, May 5th, 2010

In conjunction with the paper by Rinke Hoekstra and I (as previously noted on this blog), we are making the ontology and a graphic of Popov v. Hayashi available:

Legal Case Ontology v9

This is the OWL file. It was developed using Protege version 4, a knowledge acquisition and editing tool.

As we have not previously made this a publicly available ontology, consider it a beta release. Comments very welcome.

The graphic is the ontological representation of Popov v. Hayashi; it is a pdf file.

Ontological Graphic for Popov v. Hayashi

By Adam Wyner
Distributed under the Creative Commons
Attribution-Non-Commercial-Share Alike 2.0

New Article on Legal Case Ontologies in Knowledge Engineering Review

Wednesday, April 28th, 2010

Rinke Hoekstra and I have a paper which will appear in Knowledge Engineering Review.

A Legal Case OWL Ontology with an Instantiation of Popov v. Hayashi
Adam Wyner and Rinke Hoekstra
To appear in Knowledge Engineering Review

Abstract
The paper provides an OWL ontology for legal cases with an instantiation of the legal case Popov v. Hayashi. The ontology makes explicit the conceptual knowledge of the legal case domain, supports reasoning about the domain, and can be used to annotate the text of cases, which in turn can be used to populate the ontology. A populated ontology is a case base which can be used for information retrieval, information extraction, and case based reasoning. The ontology contains not only elements of indexing the case (e.g. the parties, jurisdiction, and date), but as well elements used to reason to a decision such as argument schemes and the components input to the schemes. We use the Protege ontology editor and knowledge acquisition system, current guidelines for ontology development, and tools for visual and linguistic presentation of the ontology.

By Adam Wyner
Distributed under the Creative Commons
Attribution-Non-Commercial-Share Alike 2.0

Forthcoming Article: On Controlled Natural Languages: Properties and Prospects

Friday, January 22nd, 2010

I am a co-author of the forthcoming article On Controlled Natural Languages: Properties and Prospects. From the abstract:

This collaborative report highlights the properties and prospects of Controlled Natural Languages (CNLs). The report poses a range of questions concerning the goals of the CNL, the design, the linguistic aspects, the relationships and evaluation of CNLs, and the application tools. In posing the questions, the report attempts to structure the field of CNLs and to encourage further systematic discussion by researchers and developers.

The reference and link to the article:

A. Wyner, K. Angelov, G. Barzdins, D. Damljanovic, N. Fuchs, S. Hoefler, K. Jones, K. Kaljurand, T. Kuhn, M. Luts, J. Pool, M. Rosner, R. Schwitter, and J. Sowa. On Controlled Natural Languages: Properties and Prospects, to appear in: N.E. Fuchs (ed.), Workshop on Controlled Natural Languages, CNL 2009, LNCS/LNAI 5972, Springer, 2010.

Instructions for GATE’s Onto Root Gazetteer

Tuesday, November 24th, 2009

In this post, I present User Manual notes for GATE’s Onto Root Gazetteer (ORG) and references to ORG. In Discussion of GATE’s Onto Root Gazetteer, I discuss aspects of Onto Root Gazetteer which I found interesting or problematic. These notes and discussion may be of use to those researchers in legal informatics who are interested in text mining and annotation for the semantic web.

Thanks to Diana Maynard, Danica Damljanovic, Phil Gooch, and the GATE User Manual for comments and materials which I have liberally used. Errors rest with me (and please tell me where they are so I can fix them!).

Purpose

Onto Root Gazetteer links text to an ontology by creating Lookup annotations which come from the ontology rather than a default gazetteer. The ontology is preprocessed to produce a flexible, dynamic gazetteer; that is, it is a gazetteer which takes into account alternative morphological forms and can be added to. An important advantage is that text can be annotated as an individual of the ontology, thus facilitating the population of the ontology.

Besides being flexible and dynamic, some advantages of ORG over other gazetteers:

  • It is more richly structured (see it as a gazetteer containing other gazetteers)
  • It allows one to relate textual and ontological information by adding instances.
  • It gives one richer annotations that can be used for further processes.

In the following, we present the step by step instructions for ‘rolling your own’, then show the results of the ‘prepackaged’ example that comes with the plugin.

Setup

Step 1. Add (if not already used) the Onto Root Gazetteer plugin to GATE following the usual plugin instructions.

Step 2. Add (if not already used) the Ontology Tools (OWLIM Ontology LR, OntoGazetteer, GATE Ontology Editor, OAT) plugin. ORG uses ontologies, so one must have these tools to load them as language resources.

Step 3. Create (or load) an ontology with OWLIM (see the instructions on the ontologies). This is the ontology that is the language resource that is then used by Onto Root Gazetteer. Suppose this ontology is called myOntology. It is important to note that OWLIM can only use OWL-Lite ontologies (see the documentation about this). Also, I succeeded in loading an ontology only from the resources folder of the Ontology_Tools plugin (rather than from another drive); I don’t know if this is significant.

Step 4. In GATE, create processing resources with default parameters:

  • Document Reset PR
  • RegEx Sentence Splitter (or ANNIE Sentence Splitter, but that one is likely to run slower
  • ANNIE English Tokeniser
  • ANNIE POS Tagger
  • GATE Morphological Analyser

Step 5. When all these PRs are loaded, create a Onto Root Gazetteer PR and set the initial parameters as follows. Mandatory ones are as follows (though some are set as defaults):

  • Ontology: select previously created myOntology
  • Tokeniser: select previously created Tokeniser
  • POSTagger: select previously created POS Tagger
  • Morpher: select previously created Morpher.

Step 6. Create another PR which is a Flexible Gazetteer. At the initial parameters, it is mandatory to select previously created OntoRootGazetteer for gazetteerInst. For another parameter, inputFeatureNames, click on the button on the right and when prompt with a window, add ‘Token.root’ in the provided text box, then click Add button. Click OK, give name to the new PR (optional) and then click OK.

Step 7. To create an application, right click on Application, New –> Pipeline (or Corpus Pipeline). Add the following PRS to the application in this order:

  • Document Reset PR
  • RegEx Sentence Splitter
  • ANNIE English Tokeniser
  • ANNIE POS Tagger
  • GATE Morphological Analyser
  • Flexible Gazetteer

Step 8. Run the application over the selected corpus.

Step 9. Inspect the results. Look at the Annotation Set with Lookup and also the Annotation List to see how the annotations appear.

Small Example

The ORG plugin comes with a demo application which not only sets up all the PRs and LRs (the text, corpus, and ontology), but also the application ready to run. This is the file exampleApp.xgapp, which is in resource folder of the plugin (Ontology_Based_Gazetteer). To start this, start GATE with a clean slate (no other PRs, LRs, or applications), then Applications, then right click to Restore application from file, then load the file from the folder just given.

The ontology which is used for an illustration is for GATE itself, giving the classes, subclasses, and instances of the system. While the ontology is loaded along with the application, one can find it here. The text is simple (and comes with the application): language resources and parameters.

FIGURE 1 (missing at the moment)

FIGURE 2 (missing at the moment)

One can see that the token “language resources” is annotated with respect to the class LanguageResource, “resources” is annotated with GATEResource, and “parameters” is annotated with ResourceParameter. We discuss this further below.

One further aspect is important and useful. Since the ontology tools have been loaded and a particular ontology has been used, one can not only see the ontology (open the OAT tab in the window with the text), but one can annotate the text with respect to the ontology — highlight some text and a popup menu allows one to select how to annotate the text. With this, one can add instances (or classes) to the ontology.

Documentation

One can consult the following for further information about how the gazetteer is made, among other topics:

Discussion

See the related post Discussion of GATE’s Onto Root Gazetteer.

By Adam Wyner
Distributed under the Creative Commons
Attribution-Non-Commercial-Share Alike 2.0

Discussion of GATE’s Onto Root Gazetteer

Tuesday, November 24th, 2009

In Instructions for GATE’s Onto Root Gazetteer, I have information to set up Onto Root Gazetteer. In this post, I discusses aspects of the Onto Root Gazetteer that I found interesting or problematic.

For me, the documentation was not helpful as too much technical information was provided (e.g. preprocessing the ontology) rather than the steps just to get it to run. Also, no walk through example was clearly illustrated. I would still like (and will provide in the near future) a richer text (a nice paragraph) and a simpler ontology (couple of classes, subclasses, object and data properties, and individuals) to illustrate just what is done fully.

Though I have it running, there are several questions (and partial answers or musings):

  • What is the annotation relative to the ontology good for?
  • What is the difference between gazetteers derived from ontologies and default gazetteers?
  • What is the selection criteria for annotating the tokens?
  • What is the relationship between the annotated text and the ontology?

Concerning the first point, presumably more annotations allow more processing capabilities. A (simple) example would be very helpful.

Concerning the second point, matters are more complex (to my mind). First, default gazetteers (or flexible gazetteers for that matter) are flat lists (a list containing no sublists as parts) where the items in the list are annotated as per the properties of the list; for example, if we have a gazetteer for Organisation (call this the header of the list) which lists IBM, BBC, Hackney Council (call these the items of the list), then every token of IBM, BBC, and Hackney Council found in the corpus will be annotated Organisation. If there is a token organisation in the corpus, it will not be annotated with Organisation; similarly, no token of IBM in the corpus is annotated IBM. The list categorises, in effect, IBM, BBC, and Hackney Council as of the type Organisation.

ORG works differently (I believe, but may be wrong), but these points are not made in the documentation. First, a gazetteer which is derived from an ontology preserves the subsumption hierarchy of the ontology, giving us a list of lists. Such a gazetteer is a taxonomy of terminology, which is not the same as an ontology (though frequently mistaken to be identical). Second, if a token in the text is found to (flexibly) match an item in the gazetteer, then the token is annotated with that item, meaning that if the string IBM is a token in our text and an item in the gazetteer, then token is annotated IBM. In these respect, ORGs work differently from other gazetteers.

The third question might be addressed in the richer documentation concerning ORG. It relates to observations concerning the results of the example application. Consider the following. The token “language resources” has the annotation:

URI=http://gate.ac.uk/ns/gate-ontology#LanguageResource, heuristic_level=0, majorType=, propertyURI=http://www.w3.org/2000/01/rdf-scheme#label, type=class

The token “resources” has the annotation:

URI=http://gate.ac.uk/ns/gate-ontology#GATEResource, heuristic_level=0, majorType=, propertyURI=http://www.w3.org/2000/01/rdf-scheme#label, type=class

And the token “parameters” has annotation:

URI=http://gate.ac.uk/ns/gate-ontology#ResourceParameter, heuristic_level=0, majorType=, propertyURI=http://www.w3.org/2000/01/rdf-scheme#label, type=class

We see that the tokens in the text are annotated in relation to the ontology. Yet it is not clear why the token “resources” is not annotated with LanguageResource or ResourceParameter since these are components of the ORG as well. Likely there is some prioritising among the annotations that we need to learn.

Finally, concerning the last question, matters are somewhat unclear (to me) largely because the line between annotations, gazetteers, and ontologies are blurred, where for me the key unclarity focuses around annotations in the text that match items in the gazetteer. Consider the issue from a different point of view. ORG was developed in the context of a project to support ontology development from text — find terms and relations which are candidates for the ontology, then (if one wants) use the terms and relations to build the ontology. For example, if one sees lots of occurrences of “organisation” in the text, then perhaps it would be introduced as a concept in the ontology. We have a many-one relation from the tokens to the ontology. This makes sense. See it another way, where we have a default gazetteer where every given token (e.g. IBM) in a text has the same annotation, giving the impression of a one-many relation. This also makes sense. Neither of these seem problematic to me largely because I don’t really know much or presume much about the meaning of the annotation on the token: from the text, I abstract the concept, from the gazetteer, I label tokens as belonging to the same annotation class. In no case is a token “organisation” annotated with Organisation; even if it were, I couldn’t really object unless I said more about what I think the annotation means.

Contrast these points with what goes on with ORG (admittedly, this gets pretty philosophical, and in terms of day to day practice, it may not be relevant). First, it seems that one instance in the ontology is associated with multiple tokens in the text. Second, an instance or class in the ontology can be associated with a token that is intended to have some similar meaning — e.g. the individual IBM in the ontology is associated by annotation with every token of IBM in the text, and similarly for the classes. Neither of these make sense to me in terms of what ontologies are intended to represent, which is a state of knowledge (the fixed concepts, object and data properties, and individuals) about a domain. On the first point, how can I be assured that the intended meaning of tokens is the same throughout the corpus? In one document, we might find IBM as the name of a non-existent company, in an other for an existing company, and in another for a company that has gone bankrupt. Simply put, the string might remain the same, but the knowledge we have about it may vary. Ontologies (as they are currently represented) do not allow such dynamic interpretation. To ignore this point risks having annotations (and whatever might flow from the annotations) slip; for example, it would be wrong to find a relationship between IBM and owners where the company doesn’t exist. On the second point, conceptually it makes no sense to say that a token “organisation” is itself associated with the concept or instance or ‘organisation’ in the ontology. Or course, in developing the ontology, going from the text to the ontology makes good sense since one is abstracting from the text to the ontology. Yet, in that move, one makes something different — a concept over all the “ideas” drawn from the tokens. So, I disagree emphatically with Peters and Maynard (from the NeON article): “Texts are annotated with ontology classes, and the textual elements function as instances of these classes.” The textual element “organisation” or “IBM” is an instance of the concept organisation or the individual IBM? I think this is a category mistake.

In general, I find the relationship between the text, intermediate representations (gazettees), and ontologies (higher level representations of knowledge) rather interesting, but somewhat murky. As I said earlier, perhaps this is just philosophy. Depending on the domain of discussion, the corpus, and the way the annotations and ontologies are used, perhaps my intuition of lurking trouble will not be realised…. Equally, there is likely something simple that I’m missing. If so, please enlighten me.

By Adam Wyner
Distributed under the Creative Commons
Attribution-Non-Commercial-Share Alike 2.0

Meeting with John Sheridan on the Semantic Web and Public Administration

Tuesday, August 11th, 2009

I met today with John Sheridan, Head of e-Services, Office of Public Sector Information, The National Archives, located at the Ministry of Justice, London, UK. Also at the meeting was John’s colleague Clare Allison. John and I had met at the ICAIL conference in Barcelona, where we briefly discussed our interests in applications of Semantic Web technologies to legal informatics in the public sector. Recently, John got back in contact to talk further about how we might develop projects in this area.

Perhaps most striking to me is that John made it clear that the government (at least his sector) is proactive, looking for research and development projects that make government data available and usable in a variety of ways. In addition, he wanted to develop a range of collaborations to better understand the opportunities the Semantic Web may offer.

As part of catching up with what is going on, I took a look around the web for relatively recent documents on related activities.

In our discussion, John gave me an overview of the current state of affairs in public access to legislation, in particular, the legislative markup and API. The markup is intended to support publication, revision, and maintenance of legislation, among other possibilities. We also had some discussion about developing an ontology of goverment which would be linked to legislation.

Another interesting dimension is that John’s office is one of a few that I know of which are actively engaged to develop a knowledge economy partly encouraged by public administrative requirements and goals. Others in this area are the Dutch and the US (with xml.gov). All very promising and discussions well worth following up on.

Copyright © 2009 Adam Wyner

Participating in One-Lex — Managing Legal Resources on the Semantic Web

Wednesday, July 22nd, 2009

Later this summer, I’ll be participating in the summer school Managing Legal Resources in the Semantic Web, September 7 to 12 in San Domenico di Fiesole (Florence, Italy). This program will focus on several aspects of legal document management:

  • Drafting methods, to improve the language and the structure of legislative texts
  • Legal XML standards, to improve the accessibility and interoperability of legal resources
  • Legal ontologies, to capture legal metadata and legal semantics
  • Formal representation of legal contents, to support legal reasoning and argumentation
  • Workflow models, to cope with the lifecycle of legal documentation

While I’m familiar with several of these areas, I’m using this opportunity to fill in my knowledge in these key areas.

General Architecture for Text Engineering Summer School

Wednesday, July 22nd, 2009

Next week I’m attending a week long summer school on General Architecture for Text Engineering (GATE). GATE is an open-source and extensible toolkit for text mining, which has been used in a variety of areas. After having worked with people who had their “hands on” the tools, I decided it would better suit me to be able to work the material myself. I’ve been looking forward to this summer school for some time and am excited at the prospect of applying GATE tools to a DB of legal cases as well as developing an ontology.

Legal Taxonomy

Saturday, May 16th, 2009

Introduction
In this post, I comment on Sherwin’s recent article Legal Taxonomy in the journal Legal Theory. It is a very lucid, thorough, and well-referenced discussion of the state-of-the-art in taxonomies of legal rules. By considering how legal taxonomies organise legal rules, we better understand current conceptions of legal rules by legal professionals. My take away message from the article is that the analysis of legal rules could benefit from some of the thinking in Linguistics and Computer Science, particularly in terms of how data is gathered and analysed.

Below, I briefly outline ideas concerning taxonomies and legal rules. Then, I present and comment on the points Sherwin brings to the fore.

Taxonomies
Taxonomy is the practice and science of classification of items in a hierarchical IS-A relationship, where the items can be most anything. The IS-A relationship is also understood as subtypes or supertypes. For example, a car is a subtype of vehicle, and a Toyota is a subtype of car; we can infer that a Toyota is a subtype of vehicle. Each subtype has more specific properties than the supertype. In some taxonomies, one item may be a subtype of several supertypes; for example, a car is both a subtype of vehicle and a subtype of objects made of metal, however, not all vehicles are made of metal, nor are all things made from metal vehicles, which indicates that these types are distinct. Taxonomies are more specific than the related term ontologies, for which a range of relationships beyond the IS-A relationship may hold among the items such as is owned by or similar. In addition, ontologies generally introduce properties of elements in the class, e.g. colour, engine type, etc. Classifications in scientific domains such as Biology or Linguistics is intensely debated and revised. It would be expected that this would be even more so true in the legal domain which is comprised of intellectual evidence rather than empirical evidence as in the physical sciences and where the scientific method is not applied.

Legal Rules
First, let us be clear about what a legal rule is with a clear example following Professor David E. Sorkin’s example . A legal rule is a rule which determines whether some proposition holds (say of an individual) contingent on other propositions (the premises). For example, the state of Illinios assault statute specifies: “A person commits an assault when, without lawful authority, he engages in conduct which places another in reasonable apprehension of receiving a battery.” (720 ILCS 5/12-1(a)). We can analyse this into the legal rule:

    A person commits assault if

      1. the person engages in conduct;
      2. the person lacks lawful authority for the conduct;
      3. the conduct places another in apprehension of receiving a battery; and
      4. the other person’s apprehension is reasonable.

Optimally, each of the premises in a rule should be simple and be answerable as true or false. In this example, where all four premises are true, the conclusion, that the person committed assault, is true.

There are significant issues even with such simple examples since each of the premises of a legal rule may itself be subject to further dispute and consideration; the premises may be subjective (e.g. was the conduct intentional), admit degrees of truth (e.g. degree of emotional harm), or application of the rule may be subject to mitigating or aggravating circumstances. The determination of the final claim follows the resolution of these subsidiary disputes and considerations. In addition, some legal rules need not require all of the premises to be true, but allow a degree of counterbalancing evaluation of the terms.

The Sources of Legal Rules
Sherwin outlines the sources of the rules:

      Posited rules, which are legal rules as explicitly given by a legal authority such as a judge giving a legal decision.
      Attributed rules, which are legal rules that are drawn from a legal decision by a legal researcher rather than by a legal authority in a decision. The rule is implicit in the other aspects of the report of the case.
      Ideal rules, which are rules that are ‘ideal’ relative to some criteria of ideality, say morally or economically superior rules.

Purposes of Classification
In addition, we have the purposes or uses of making a classification of legal rules.

      Facilitating the discussion and use of law.
      Supporting the critical evaluation of law
      Influencing legal decision-making

In the first purpose, the rules are sorted into classes, which helps to understand and manage legal information. In Sherwin’s view, this is the most basic, formal, and least ambitious goal, yet it relies on having some taxonomic logic in the first place. The second purpose, the rules are evaluated to determine if they are serving the intended purpose as well as to identify gaps or inconsistencies. As Sherwin points out, the criteria of evaluation must then also be determined; however, this then relates to the criteria which guides the taxonomy in the first place, a topic we touch on below. The final purpose is a normative one, where the classification identifies the normal circumstances under which a rule applies, thereby also clarifying those circumstances in which the rule does not apply. Sherwin points out that legal scholars vary in which purpose they find attractive and worth pursuing.

While I can appreciate that some legal scholars might not find the ‘formal’ classification of interest, I view it from a different perspective. First, any claim concerning the normative application of one rule instead of another rest entirely on the intuitive presumption that the rules are clearly different. This is a distinction that the first level can help to clarify. Similar points can be made for other relationships among rules. Second, focusing on the latter stage does not help to say specifically why one rule means what it does and has the consequences as intended; yet surely this is in virtue of the specific ‘content’ of the rule, which again is clarified by a thorough going analysis at the first stage. Third, if there is going to be any progress in applied artificial intelligence and law, it will require the analytical elements defined at the first stage. Fourth, as the study of Linguistics has shown, close scrutiny at the first stage can help to reveal very issues and problems that are fundamental to all higher stages. Fifth, providing even a small, clear sample of legal arguments analysed along other lines of the first stage can give the community of legal scholars a common ‘pool’ of legal arguments to fruitfully consider at the later stages; along these lines, it is notable how few concrete, detailed examples Sherwin’s paper discusses. Not surprisingly, some of the issues Sherwin raises about the purposes of different ‘levels’ of analysis also appear in the linguistic literature. In my view, though the first stage may not be interesting to most legal professionals, there are very good reasons why it should be.

Criteria of Taxonomy
Several different criteria which guide the taxonomy of legal rules are discussed.

      Intuitive similarity: whether researchers claim that two rules are subtypes of one another.
      Evolutionary history: the legal rule is traced in the history of the law.
      Formal classification: the logical relations among categories of the law.
      Function based: a function from the problem to a set of solutions.
      Reason based: the higher-level reasons that explain or justify a rule.

Sherwin criticises judgements based on intuitive similarity since the taxonomers may be relying on false generalisations rather than their own intuitions and that intuition can be arbitrary and without reason. This is also the sort of criticism leveled at large segment of linguistic research and which has been shown to be misleading. Of course, one must watch false classifications and try to provide a justification for classifying one element in one class and not another. One way to do this is, as in psycholinguistics, is to provide tests run over subjects. Another way is to refine the sorts of observations that lead to classifications. In general, all that we currently know about language, from dictionaries, to grammars, to inference rules is based on linguistic intuitions. Some, such as the rules of propositional logic, have been so fixed that they now seem to exist independent of any linguistic basis.

The issue here is somewhat related to classification by formal logical relations. It is unclear what Sherwin thinks logical relations are and how they are applied. What we do have more clarity on are some of the criteria for such a formal taxonomy: accounting for all legal materials, a strict hierarchy, consistent interpretation of classes, and no overlap of categories. This is but one way to consider a formal hierarchy; indeed, there is a separate and very interesting question about what formal model of classification best suits a legal taxonomy. Yet, this issue is not explored in the article.

The function based approach seems to have meta categories. For example, the rule above can be seen as a function from circumstances to a classification of a person as having committed an assault. However, this is not what appears to be intended in Sherwin’s discussion. Rather, there are meta-functional categories depending on higher level problems and solutions. The examples given are Law as a Grievance-Remedial Instrument and Law as an Administrative-Regulatory Instrument. For me, this is not quite as clear as Sherwin makes it appear.

The reason approach organises rules according to an even higher-level of the rule — the justification or explanation of the rule. Some of the examples are that a wrongful harm imposes an obligation for redress, deterring breaches of promises facilitate exchange, or promoting public safety. In my view, these are what people (e.g. Professor Bench-Capon) in AI and Law would call values which are promoted by the legal rule. Sherwin discusses several different ways that reason based classification is done: intended, attributed, and ideal rationales. In my view, the claimed differences are not all that clear or crucial to the classification. In some cases, the rationale of a legal rule is given by the adjudicator. However, where this is not so, the rationale is implicit and must be interpreted, which is to give the intended rationale. In other cases, legal researchers examine a body of law and provide rationales, which is the attributed rationale. In this sense, the intended and attributed rationales are related (both interpreted), but achieved by different methods (study of one case versus study of a body of cases and considerations about the overall purpose of the law). Finally, there are ideal rationales, which set out broad, ideal goals of the legal rule, which may or may not be ‘ideally’ achievable. In this, the difference between intended/attributed and ideal is whether the rationale is analysed out of cases (bottom-up) or provided legislatively (top-down). In the end, the result is similar — legal rules are classified with respect to some rationale. The general problem with any such rationale is just how it is systematically given and itself justified so as to be consistent and not to yield conflicting interpretations of the same legal rule. Finally, Sherwin seems to think that there is some intrinsic conflict or tension between formal classification and reason based classification. I don’t agree. Rather, the difference is in the properties and methods being employed to make the classification, which are not inherently in conflict. Likely, a mixed approach will yield the most insights.

Copyright © 2009 Adam Wyner