Manual Character Evidence: An Abductive Theory (Argumentation Library)

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Another factor is that in artificial intelligence, something called a justification explanation been recognized [ 7 ], suggesting that argument and explanation are often combined and work together. Suffice it to say that abductive reasoning, also commonly called inference to the best explanation, is just such a species of argument. There is also a tendency among students who are learning to use argumentation techniques in introductory logic courses, once they have learned some tools to analyze and evaluate arguments, to see any text of discourse they are given as expressing an argument.

This can be a problem. The student who treats an explanation as an erroneous argument committing a fallacy, for example the fallacy of arguing in a circle, when the argument is really an explanation, has committed an error by misapplying logic.

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Logic textbooks attempt to solve this problem by offering a pragmatic test to determine, in a given case, whether a passage expresses an argument or an explanation, namely by looking at how the discourse is being used in the given case. If it is being used to prove something that is in doubt, it is an argument. If it is being used to convey understanding of something that does not make sense or is incomprehensible, it is an explanation. The focus of this way of drawing the distinction is on the proposition or event that is to be explained or proved.

If it is not subject to doubt e. If it is subject to doubt, that is, if it is unsettled whether it is true or not, then the bit of text in question should be taken as an argument. Here is the first one: the Challenger spacecraft exploded after liftoff because an O-ring failed in one of the booster rockets.

Classifying this assertion as an argument or an explanation depends on whether the statement that the Challenger spacecraft exploded after liftoff should be taken as a statement that is accepted as factual or whether it should be taken to be a statement that is subject to doubt and that requires proof, or at least some supporting evidence, before it is accepted. The statement that the O-ring failed is not being used to prove the statement that the spacecraft exploded. That the spacecraft exploded is not in doubt. Most of us graphically remember seeing the exploding spacecraft on TV.

The passage quoted above is not trying to prove that statement by providing evidence or reasons that support or imply it. The passage assumes that it is an accepted matter of fact that the spacecraft exploded, and is trying to show why it exploded. So the passage contains an explanation, as opposed to an argument. Because it is generally taken as common knowledge that the Challenger spacecraft exploded after liftoff, the whole causal statement is taken as an explanation. The same principle applies to the second example: cows can digest grass, while humans cannot, because their digestive systems contain enzymes not found in humans.

Should we take it as an accepted fact that cows can digest grass while humans cannot, or should we take this statement as subject to doubt and something that needs to be proved before it can be accepted? Again, it seems fairly plausible that the statement that cows can digest grass while humans cannot is generally accepted as part of common knowledge. We need to be aware, however, that this distinction based on common knowledge is not the only criterion required to distinguish arguments from explanations in a natural language text of discourse.

The problem is that the same indicator words are often used with respect to both arguments and explanations. Hence in any individual case one has to look carefully at the details of the actual text of discourse in the given case. In the context of argumentation, premises are offered as proof of a conclusion or a claim, often in order to persuade someone or settle an issue that is subject to doubt or disputation. A number of computational models of argumentation have emerged and matured in the past twenty-or-so years [ 20 ] and the computational aspects of the dialectics of argument and of the structure of argument are well understood cf.

In the context of explanation, the explananda facts to be explained are explained by a coherent set of explanans facts that explain. The usual purpose of explanation is not necessarily to convince someone but rather to help someone understand why the explananda are the case. Computational models for explanation are mainly based on the technique of abductive model-based reasoning, which has been studied in the context of medical and system diagnosis [ 9 ]; other examples of computational explanation are [ 8 ], which models explanatory dialogues, and [ 24 ], which uses explanations for natural language understanding.

Despite the interest in dialogue treatments of explanation, the formal dialectical systems deriving from the early work of Hamblin treat only arguments. As each party moves, statements are either inserted into or retracted from its commitment set of the party who made the move. Despite the important role explanations can play in argumentative dialogue, there have not been many attempts to combine argumentation and explanation into one formal model.

Perhaps the most thorough work thus far is [ 2 , 6 ], in which arguments in the framework of [ 19 ] are combined with abductive-causal reasoning based on standard models of explanation [ 9 ] in one hybrid theory. The basic idea of this hybrid approach is as follows.

Arguments can be used to support and attack stories, and these arguments can themselves be attacked and defeated. Thus, it is possible to reason about, for example, the extent to which an explanation conforms to the evidence. This is important when comparing explanations: the explanation that is best supported and least falsified by arguments is, ceteris paribus, the best explanation. Dialogues consist of a series of locutions or utterances made by the participants.

As a simple example of a dialogue, take the following exchange between Allen and Beth. The link between a dialogue and this underlying structure can be explained by combining speech act theory [ 25 ] with Hamblin-style dialogue theory. For example, one may include p in different kinds of moves like asserting p , asking p , challenging p , promising p and so on. There are different types of dialogue [ 28 ], each with a different goal. Another example of a dialogue type is inquiry dialogue , the aim of which is to increase knowledge.

The participants in such a dialogue collectively gather, organize and assess hypothetical explanations and evidence for and against these explanations. Hence, Walton [ 30 ] identifies both explanation and argumentation as functions of an inquiry dialogue. Aleven [ 1 ] has defined an inquiry dialogue based on the hybrid theory in which the participants build explanations and then support and critically analyze these explanations using arguments.

In this type of dialogue, the participants collectively build a hybrid theory of explanations and arguments. The very first problem in attempting to analyze the concept of an explanation is to attempt to provide criteria to determine when some piece of discourse that looks like it could be either an explanation or an argument should be taken to fit into one category or the other. One possible way of distinguishing between argumentation and explanation might be to look at the product of our reasoning, that is, the underlying reasoning structure. At first sight, it often seems an explanation is abductive and causal whilst an argument is modus-ponens style, non-causal reasoning.

However, as it turns out it is also possible to give abductive or causal arguments cf. As was previously argued in [ 4 ], argument and explanation can only be properly distinguished by looking at the dialogical context of reasoning. In order to determine this context, we need not just look at the original intention of the speaker i. Consider the example in Fig. Allen makes his first move by asserting that the old warehouses should be preserved, and then Beth asks for a justification for this claim.

Allen then provides this, but then Beth asks him the why-question: why are they so valuable? The speech act could be interpreted as requesting either an argument challenging or an explanation Fig. Circular arguments and explanations. Circular reasoning has long been a concern in logic. The fallacy of arguing in a circle has been included under the heading of informal fallacies in logic textbooks since the time of Aristotle [ 12 ]. But circularity is not been concerned exclusively with respect to arguments.

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Circular explanations are often condemned by the logic textbooks as unhelpful and confusing. But the reasons for condemning circular explanations are different from those for condemning circular argumentation [ 26 ]. The fallacy of arguing in a circle, or begging the question, is committed by an instance of circular reasoning that fails to work as an argument supposed to prove the conclusion that is in doubt. A standard textbook example is provided by the following short dialogue between a man, Smith, and his bank manager.

Here we can detect a sequence of circular reasoning. The trustworthiness of Smith is supposed to depend on the testimony of his friend Jones, but the trustworthiness of Jones depends on the testimony of his friend Smith. This obviously will not work because of the circularity in the procedure of providing evidence to support a claim in an argument. In this kind of case, we cannot prove claim q by relying on premise p and then try prove p by backing it up by using q as a premise.

It does not follow, however, that all circular arguments are fallacious as we now indicate. To extend the example a bit further, suppose that a third-party could vouch for Jones, and that the trustworthiness of this third party is not dependent on the trustworthiness of either Smith or Jones. Then there would still be a circle in the argumentation structure, as shown in Fig. This new argument gives us a way of breaking out of the circle that we were locked into in the previous argument represented by the dialogue above.

The argumentation as a whole shown in Fig. The problem with real cases where the fallacy of begging the question is a serious danger is that the circle is embedded in a text where it may be mixed in with much other discourse. This danger becomes even more serious when the discourse combines argumentation with explanation. But if you can find such a circle in an argument, it represents quite a serious criticism of that argument. A rational argument used to persuade a respondent to accept its conclusion must not be based on premises that can only be accepted if part of the evidence for one of these premises depends on the prior acceptance of the conclusion itself.

If, so the argument is useless to prove the conclusion. The argument lacks what has been called a probative function [ 26 ]. The situation is different for explanations. They need to be evaluated in a different way. When a circular explanation is fallacious it is because it is uninformative or useless in transferring understanding. As with arguments, however, an explanation can be circular, but still be useful as an explanation.

One reason is that there are feedback processes in nature, and to explain what is happening, the account given needs to go in a circle. For example, the more overweight a diabetic gets, the more insulin is produced in his blood, but the more insulin there is in his blood, the more he eats, and the more he becomes overweight. In this vicious circle, the problem becomes worse and worse by a continual process of feedback that escalates it.

To understand that the process is circular helps to explain the whole picture of what is going on. Now extend the dialogue as follows:. But why do the older buildings lend the town its distinctive character? When examining this dialogue we might be suspicious about the possibility that it contains the fallacy of begging the question. After all, when Allen is asked by Beth about the justification for preserving the old warehouses 4 , Allen replies that the warehouses are valuable architecturally 5. But then later, at his last move in the dialogue 7 , he reverts back to making the same statement again.

It definitely appears that the dialogue is circular. The question then is whether the circularity is benign or vicious. Now the reasoning in the dialogue is no longer just a sequence of argumentation, but a mixture of argumentation and explanation Fig. In order to prove his claim that the warehouses are valuable architecturally, Allen has used the premise that the older buildings lend the town its distinctive character.

But then he has used the former as an explanation to help Beth understand the latter. This volume collects twenty essays that examine theoretical issues in the study of argumentation. It provides a multidisciplinary and even interdisciplinary outlook on the current state of affairs in argumentation theory. It illuminates an area of rhetoric and logic which has remained obscure for more than two thousand years. Starting from a clearly defined theoretical basis, they report about a continued series of experimental tests. In this book, the authors report on their Systematic Empirical Research of the Conventional Validity Infinite regress arguments are part of a philosopher's tool kit.

But how sharp or strong is this tool? The author has collected and evaluated a host of infinite regress arguments, comparing and contrasting many of the formal and non-formal properties. But how sharp or strong is this Toggle navigation. New to eBooks. Argumentation Library Series. Filter Results. Last 30 days. A metadata representation to support this value chain would need to:. This paper introduces the micropublications semantic metadata model. This model responds to the nine use cases we present, in which digital summarization of scientific argumentation with its evidence and methodological support is required.

These use cases, for the most part, deal directly with the scientific literature, rather than its processed reflection in curated topical databases. Statement-based models have been proposed as mechanisms for publishing key facts asserted in the scientific literature or in curated databases in a machine processable form. Some offer statement backing in the form of other statements in the scientific literature, but none actually has a complete representation of scientific argument including empirical evidence and methods.

Of the three examples we mention,. BEL and SWAN model backing statements from other publications in the literature by citing whole publications, leaving the reader to determine precisely where in the cited document a backing statement actually resides;. None of these models provide a means to transitively close claim lineages to underlying empirical evidence — because they do not represent it.

Nanopublications distill content as a graph of assertions associated with a provenance of the article or dataset from whence they came; and b a set of terms for indexing and filtering in order to identify auxiliary information in large data sets. Note that formalization of the np:Assertion is somewhat awkward in this example, and requires multiple level of reification. Yet the np:Assertion is not modelling a markedly complex scientific claim. Representation of statements and evidence in a nanopublication format. The intent of statement-based models is to be relatively simple and useful for specific tasks.

In the case of nanopublications, this particular model is currently presented on a technical level mainly for data integration across chemical and biological databases. There is no suggestion in the current specification that nanopublications may be applied directly to ordinary scientific articles, nor that they are designed to present primary scientific evidence — although more expansive claims have been made elsewhere in the literature[ 22 , 23 ]. Furthermore, the fact that formalization of assertions is required, is likely an impediment to such direct use. Consequently, a more comprehensive model is needed to be applied successfully across the entire ecosystem of biomedical communications.

The micropublication approach goes beyond statements and their provenance, proposing a richer model in order to account for a more complete and broadly useful view of scientific argument and evidence, beyond that of simple assertions, or assertions supported only by literature references. It is also designed to be readily compatible with assertions coded in BEL or as nanopublications, as these models are considered useful in certain applications and will need to be integrated. Scientific assertions do not become matters of fact until the facts have been established, through judgements made over time in a complex social process.

Once a matter of fact is established, the scientific literature persists as an open documentary record, which from time to time may be challenged and reassessed. Thus, to usefully model facts in the process of formation , empirical evidence, as well as formal statements and their provenance, must be a part of our model.

However, previously, in the late nineteenth century, the existence and nature of malarial vectors was an open research question[ 30 ]. Open research questions require presentation of evidence to establish a warrant for belief [ 31 ]. Thus, for scientists working on malaria over a century ago, a statement about supposed malarial vectors without supporting empirical evidence, would not have been robust enough to enable evaluation, and thus could not have motivated reasoned belief. Recent results spotlight concerns with the communication of evidence and its citation.

Fang et al. Retractions themselves are an increasingly common event[ 6 ]. Simkin and Roychowdhury showed that, in the sample of publications they studied, a majority of scientific citations were merely copied from the reference lists in other publications[ 32 , 33 ]. The increasing interest in direct data citation of datasets, deposited in robust repositories, is another result of this growing concern with the evidence behind assertions in the literature[ 34 ]. We have incorporated a number of features in our model to enable presentation of empirical scientific evidence; therefore including data, not just assertions, as information supporting a statement; as well as other required features for scientific discourse.

As useful as formal language representations may be, any requirement that statements must only be expressed in formal language such as we find in BEL, nanopublications, and some other approaches, is a potential barrier to adoption in the publication ecosystem. We can expect to encounter scientific claims in their native environment, the biomedical literature, as relatively nuanced arguments for qualified claims supported by evidence. This evidence consists of citations to the literature, and novel data with supporting methods. Moreover, ordinary scientific workers present their conclusions in natural language and will continue to do so.

Consequently the micropublication model must capture the natural language of claims as they appear in the literature. We treat formalization separately as an optional curatorial step. What claims are considered true, may evolve over time, based on re-examination of evidence and development of new evidence. Assertions may be criticised and refuted.

Thus scientific reasoning is defeasible [ 43 ]. It is a mainstream model of argument in the Artificial Intelligence AI community. The micropublication model is grounded in Toulmin-Verheij; and is consistent with recent work in AI on defeasible argumentation[ 43 , 45 , 48 , 50 , 51 , 54 ]. Our model provides a framework to support extensively qualified claims in natural language, as generally presented by researchers in their primary publications.

Support relations, structured as graphs, back up assertions with the data, context and methodological evidence which validates them. Micropublications permit scientific claims to be formulated minimally as any statement with an attribution basic provenance , and maximally as entire knowledgebases with extensive evidence graphs.

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Thus micropublications in their minimal form subsume or encompass statement-based models, while allowing presentation of evidential support for statements and natural language assertions as backing for formalisms. This has significant applicability across the lifecycle of biomedical communications. The goal of the micropublication model is to better adapt scientific publications to production and use on the Web, in the context of the new forms either made available or required.

The model supports nine main activities e. These are activities in the lifecycle of biomedical communications — part of its knowledge or information value chain[ 56 — 58 ] — to which the model is meant to respond, and which provide context for the model-specific use cases. Within these we select a set of important but non-exhaustive applications of the model, targeted at responding to specific deficits already identified. For users of scientific publications, these are mostly centered on failures of evidence and reproduciblity[ 8 , 55 , 59 , 60 ]; and on intractable volumes of information presented to the scientist[ 61 ].

Applications of the model begin with its feature of citable claims use case 1 , supported by evidence use case 2 , from which a robust claims network may be automatically or semi-automatcally constructed and analyzed use case 3. These first three use cases respond to the identified problems of mishandled, degraded or fictitious citations[ 3 , 4 , 32 , 62 ]; and to scientific claims not properly grounded in evidence[ 3 , 4 ]. These ultimately all address the issue of scientific reproducibility[ 8 , 55 , 59 , 60 ].

We then provide a use case centered on abstracting single articles use case 4. This responds to the publication-volume overload issue noted by Cohen and Hunter, and others[ 1 , 63 ], by facilitating useful operations of various computational browsing tools such as[ 64 ]; construction of structured claim-evidence representations within reference managers; and as a side effect, enabling the construction of claim networks already mentioned see above.

Topic-centric claim networks may be equivalent to domain-centric knowledgebasees, if all claims having common meaning, but different wording, can be made functionally equivalent use case 5. They are more readily computable, if formalized use case 6 in languages such as Biological Expression Language BEL [ 18 ]; or nanopublications[ 24 , 26 , 65 ]. They may be developed under formal curation, for a department or other research enterprise; or as extensions to bibliographic data management by individual scientists.

Publications must be discussed in the biomedical community. This is a part of their validation. Use case 7 also responds to the increasing interest in algorithmic annotation using semantic models[ 2 , 11 , 17 , 68 — 84 ] by providing a way for computational annotation to be combined with argument models. Use case 8 responds to the nature of scientific publications and discussions as arguments, which may agree of disagree with one another, and allows findings from the argumentation theory community[ 49 , 54 ] to be deployed on constructed claim networks.

Lastly, if this model is to be deployed, it must be backward-compatible with the existing communications ecosystem. For this we rely on emerging stand-off web annotation models use case 9 [ 85 — 88 ]. This lifecycle is part of a value chain. We will show here how the micropublications model can effectively support information creators and consumers tool users in this ecosystem, for important unsatisfied use cases.

For each one we show a motivation, use, point of implementation, and comments if any. Activity lifecycle of biomedical communications linked to use cases, activity inputs and activity outputs. The main information content generated, enhanced and re-used in the system is shown. Bolded inputs and outputs represent micropublication-specific content. In his analysis, it is straightforward to see how citation distortions may contribute to non-reproducible results in a pharmaceutical context, as reported in[ 8 ].

Use: Citable claims are a specific remedy for citation distortion by allowing ready comparison of what is cited, to what the citation is claimed to assert. Implementation: Citable claims may be constructed economically at the point where researchers read and take notes upon, or search for backing for their own assertions in, the domain literature of their field. Comments: Any scientific statement with an attribution may be formalized as a citable claim using the micropublication model.

Motivation: Evidence is the basis for assessment and validation of claims in biomedical and scientific argument. Greenberg[ 3 , 4 ] specifically showed how citation lineages may not actually resolve to empirical evidence. Claims ultimately must be based on data, and data must be based on reproducible methods. Use: Micropublications may be used to represent experimental evidence supporting claims, as they can represent non-statement artifacts such as reagents, images and other data. This function of the model has multiple roles. It adds additional value to citable claims by indicating what claims are actually backed by direct evidence, and what this evidence is.

It also provides the ability to trace the association of claims in the literature to specific methods and data, and vice versa. Implementation: As in use case 1, evidence support for claims may be modeled as part of the process of recording bibliographic references. It may also be modeled directly by publishers as supplemental metadata, or by biomedical Web communities as part of a discussion. Motivation: Digital abstracts would be extremely useful supplemental metadata. They would be particularly useful to enable text mining as argued by Gerstein et al.

Implementation: They could be provided by publishers or by value-add third parties, or created as part of personal or institutional knowledge bases. Mashing-up digital abstracts can be done by third-party applications, and would be one way to deal with intractable publication volumes, by properly summarizing them in a reliable, computable way. Comments: To enable complete digital abstracting, we define a system of classes for representing biological objects such as reagents, software, datasets and method descriptions, which are not statements in natural language or triples, but are important in documenting the foundational evidence for biomedical claims and arguments, and in making biomedical methods reusable.

Motivation: As previously noted, it has been shown that the biomedical literature contains a significant proportion of non-reproducible results. These can be made even more problematic as they are repeatedly cited and transformed in claim networks. Use: Claim network analysis can be used to determine the origin of, and compare evidence for, individual and contrasting claims in the literature.

Particularly when experiments are being designed based on putative findings of a body of prior research; it seems critical to be able to fully assess the entire background of a set of assertions. Implementation: Micropublications once instantiated, embody individual arguments, which in turn may be composed by resolution of references. This allows us to create extended graphs showing the basis in evidence for claims in the literature, even when they are deeply buried in chains of citations.

Lineage visualization is proposed as a tool for readers and reviewers. Motivation: Resolution of references often entails finding a claim in a cited document, which is similar to the claim formulated in the citing document. Further, parallel claims, of equivalent or near-equivalent meaning, may arise from different lines of research, without resolution to a common progenitor study. Use: Using Similarity groups , a set of claims may be defined as having "sufficient" closeness in meaning to a representative exemplar, or Holotype claim. Their purpose is to allow normalization of diverse sets of statements with essentially the same meaning in the literature, without combinatorial explosion.

Comments: The similog-holotype model is an empirically based model that allows similar claims to be normalized to a common natural language representation, without dropping necessary qualifiers and hedging. Translations of claims to formal or other natural languages may also be considered similogs to the translated original, based on sufficient equivalence of meaning. Motivation: Various applications in computing require translation of natural-language claims in the biomedical literature to statements in a formal vocabulary.

Use: Ideally one would like to be able to trace formalized claims back to their foundational evidence in the literature just as one does with natural language claims. The micropublications model supports formalization of claims. Implementation: At the point a formalized claim is created modeled from a base statement in the literature, the creating application may capture its supporting statement using the micropublications model.

For example, in the current BEL software, instead of capturing only the Pubmed ID of the publication from which a BEL statement is derived, one might readily capture the backing statement as well, as a micropublication.

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Comment: Remember that the minimal form of a micropublication is a simple statement, with its attribution, and the attribution of its encapsulating micropublication. Motivation: Annotation and discussion of scientific literature is increasingly conducted on the Web. Use: Scientific claims and evidence may be annotated in personal or institutional knowledge bases, and may be discussed online in specialized Web portals or communities. Modeling these texts as micropublications, with their backing statements and evidence from the literature, allows them to be exchanged freely between applications in a standard format.

Implementation: This may be done by Web or other applications at the time the publications are annotated or discussion is captured. Motivation: Scientific discourse often involves disagreement on the correct interpretation or theoretical model for existing evidence. It is important to know where gaps or disagreements exist because these naturally suggest areas for further research. Use: We provide an abstract logic representation compatible with much of the current AI literature on argumentation, as well as a description logic presentation modeled in OWL Additional file 1.

Implementation: Where groups or individuals systematically collect statements and evidence on scientific topics, this may be implemented as a useful pattern. Comments: We believe this approach could be of particular value in drug discovery and development activities. Motivation: Micropublications may be applied as annotation to scientific documents, including other micropublications. We use an annotation ontology such as AO[ 85 , 86 ] or OAM[ 92 ] to associate micropublication class instances with specific content segments in Web documents, and to record the annotation attribution.

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Use: Contextualization is important for the creator of annotations, because it shows them in context. This is of equal importance for the consumer of annotations. Comments: We believe micropublications will most commonly be created as semantic annotations on published articles, as this is backward-compatible with the existing publication ecosystem[ 67 ]. The most important thing to note about the activities constituting the use cases is that all of them involve assembling, justifying, critiquing, or representing some form or elements of scientific argumentation, including all the support for the argumentation, i.

Thus, very few of these use cases can be met adequately by purely statement-based models. Constraints on the micropublication model should be imposed not only by the use cases above as they relate to biomedical scientists, but also by other work in the field of argumentation models as reviewed in[ 53 ] , which we would like to be able to reuse where possible.

We would like to model both the internal structure of arguments, to digitially summarize publications in a useful way; and the interargumentation structure, so as also to model relations between arguments considered without regard to their internal structure. Also, we want to enable construction of claim networks similar to that described by Greenberg[ 3 ], which are principally based on support relationships, but may also have a significant challenge or attack component. Using a graph model requires common connective properties to allow transitive closure.

So for example, the relation between data and its interpretation in a textual statement is called support , as is the relationship between a statement and the reference cited to justify it. The micropublications model is a framework which accomodates a spectrum of complexity, from minimal to maximal representations.

It can ingest the simplest forms and give room for stepwise elaboration, consistent with the incremental distributed value chain in which we are trying to embed it. The minimal representation is a single identified statement, where attribution is attached to both the statement and the identification. The maximal representation may be as complex as an entire knowledge base.

It is worth stressing that despite the richness of this model, using it does not by any means require deploying all of its concepts for any particular scenario. To introduce the model, we outline the semantic and mathematical models of argument. To illustrate the model, we use exemplar publications as described below. The base classes, predicates and DL-safe rule defintions of the full model, are given in Section A.

Micropublications represent scientific arguments. The goal of an argument is to induce belief[ 44 ]. These are called challenges in our model, rebuttal by Toulmin[ 44 — 46 ], and attacks in the artifical intelligence literature on argumentation frameworks see e.


The minimal form of an argument in our model is a statement supported by its attribution. If the source of the statement is trusted, that may be enough to induce belief. Aristotle called this aspect of rhetoric ethos , the character and reputation of the speaker[ 95 ]. Minimal form of a micropublication: formalizing a statement and its attribution. Unlike the standard Toulmin-Verheij model, which only deals with statements, scientific argument must ultimately support statements with empirical evidence, consisting of.

Scientific argument must also situate its claim in the context of previous work in the domain, of which it takes account — as additional support, or as error to be challenged and disproven. This context is deployed as paraphrases of other published findings claims , qualified by a citation of the work from which they were paraphrased. As we are interested in constructing claim networks, it should be clear that in a network, warrant and backing are relative terms. Furthermore, to contruct such a network, we will need to have backing which resides in another work, available in the form of a single statement, not the entire work.

While a citation of an entire article may be acceptable as a temporary measure, reflecting pragmatic boundaries, ultimately we wish to have the full claim network at hand.

here This sets us up to be able to transitively close the network. To do so we use a supports relationship between warrant and backing. Defining this relationship consistently across the model — whether we are dealing with supporting statements, data, or methods — also allows us to bridge the gap between internal argument structure, and inter-argument structure.

We call any element of an argument, a Representation a , a class whose subclasses include Sentence , Statement , Claim , Data , and Method. Sentences need not be syntactically complete — they may consist of a phrase, single word, or single meaningful symbol e. Declarative Sentences are Statements. Sentences which qualify a Statement are Qualifiers. The principal Statement in an argument is called a Claim.

Statements may be supported by other Statements, or by Data. Data in turn may be supported by Method, i. A Procedure or a Material is a Method. The Claim is supported here by both a Statement paraphrasing another finding in the literature, and by Data. The paraphrase is supported by a Reference to the work in which we are supposed to be able to find its source. The Data is supported by its Method.

Both the micropublication itself, and the argumentation it formalizes, have Attribution. A Micropublication supported by a Statement referenced to the domain literature; empirical Data; and a reusable Method. Later we will examine various forms of argument formalized as Micropublications, using a closely related set of examples taken from the literature on Alzheimer Disease, for each Use Case. The basic outlines of our model are given here more systematically.

The challenges property is inferred when a Representation either directlyChallenges another, or indirectlyChallenges it by undercutting directlyChallenges a Representation which support s it. A Qualifier is a Sentence, which may modify a Statement. References and SemanticQualifiers tags are two varieties of Qualifier. Data may be supportedByMethod if a Method supports it. A Method is a reusable recipe showing how the Data were obtained; it specifies an Activity, and may refer to some Material as a component of the recipe.

A Material supports any Method of which it is a component. A Representation is defined as an elementOf a Micropublication if that Micropublication either asserts or quotes it. A Representation assertedBy a Micropublication is originally instantiated by that Micropublication. A Representation quotedBy a Micropublication is referred to by that Micropublication, after first being instantiated by another Micropublication. The asserts and quotes notions are simple extensions of concepts from Carroll et al. Representations related to the Claim of a Micropublication by the supports property, and which are elementsOf that Micropublication, constitute its Support Graph, being related to the Micropublication by the property hasSupportGraphElement.

The property hasChallengeGraphElement works similarly. Major classes and relationships in the model. A Claim is the main Statement argued by a Micropublication. A Statement is a truth-bearing Sentence , which may be variously qualifiedBy some Qualifier.

Features & Highlights

A Sentence is a well-formed sequence of symbols, intended to convey meaning; and is not necessarily either complete or truth-bearing. A Micropublication hasElements consisting of those Representations it asserts or quotes. A Representation supports or challenges other Representations. The supporting Representations which are elementOf the Micropublication will be in its SupportGraph ; challenging elements, will be in its ChallengeGraph. Dashed-line boundaries indicate graphs instantiated by query.

The class Artifact has, as previously noted, a series of subclasses allowing us to deal relatively homogeneously with experimental methods, materials, data, and language artifacts such as statements. All Statements, as Artifacts, should have an Attribution. The Attribution of a Statement is therefore a part of its SupportGraph. In scientific argumentation, a publication in Science , Nature , etc.

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  8. This is why Attribution is part of the SupportGraph of an Argument. However, Attribution alone is weak support. The critical element of support in science is empirical scientific evidence.