Abstract: This paper characterises key weaknesses in the ability of current digital libraries to support scholarly inquiry, and as a way to address these, proposes computational services grounded in semiformal models of the naturalistic argumentation commonly found in research literatures. It is argued that a design priority is to balance formal expressiveness with usability, making it critical to co-evolve the modelling scheme with appropriate user interfaces for argument construction and analysis. We specify the requirements for an argument modelling scheme for use by untrained researchers, describe the resulting ontology, contrasting it with other domain modelling and semantic web approaches, before discussing passive and intelligent user interfaces designed to support analysts in the construction, navigation and analysis of scholarly argument structures in a Web-based environment. Keywords: scholarly argumentation; argument modelling user interfaces; argument visualization; semantic annotation; cognitive support; conceptual graphs; electronic publishing; contested knowledge 1 This journal special issue brings together work which investigates the implications of modelling, with computational support, naturally occurring arguments as formulated in the course of everyday work. This paper contributes with respect to several issues raised by this challenge: • A domain application of natural argumentation modelling, namely, to scholarly electronic publishing and discourse; Buckingham Shum, et al. • This is accomplished through Web-mediated computer supported collaborative argumentation, for modelling the specific types of argumentation found in research literatures; • Tools are provided for interacting with structures of argument, include visualisation tools and interfaces supporting structured dialogue. We start by characterising some weaknesses in current scholarly/scientific publishing infrastructures, and as a way to address these, propose computational services grounded in semiformal models of the naturalistic argumentation found in research literatures. Let us begin with a question to focus the imagination: In 2010, will scientific knowledge still be published solely in prose, or can we imagine a complementary infrastructure that is 'native' to the emerging semantic, collaborative web, It is important to say that we are seeking neither to replace textual narrative as an expressive medium, nor its products such as books and peer reviewed publications. We seek instead to augment them by exploiting globally networked information in ways that -precisely because of its historical pedigreethe prose publication cannot support. Conventional scholarly publications are the result of a long coevolution of notational form with print publishing technology, but are not designed to take advantage of today's information infrastructure. While information retrieval and human language engineering research seek to extract structure of different sorts from these texts, the strategy pursued here is to question why this structure is lost in the first place? Instead, we are investigating the interdependent representational and usability challenges in capturing and publishing the conceptual structure of a research article as a human and machine readable, semiformal structure. In the following sections, we set out the rationale for this work (Section 2), and then focus on associated challenges, with an approach derived from the research into Hypertext, Human-Computer Interaction, Computer-Supported Collaborative Work and Computational Linguistics. Section 3 specifies the particular requirements for an argument modelling scheme which will be usable by researchers untrained in conceptual modelling or argumentation theory, Section 4 describes the modelling scheme, before Sections 5 and 6 describe a series of user interfaces designed to support the variety of user tasks in Buckingham Shum, et al. the modelling environment. This paper extends our previous work by contextualising our approach to the specific field of naturalistic argument modelling, consolidating previously presented but unpublished material Uren, et al., 2003a) , updating the description of the user interfaces from earlier papers Uren, et al., 2003b) , expanding the theoretical rationale behind the representational scheme (Uren, et al., 2004) , and illustrating new literature modelling case studies and computational services from those already reported Uren, et al. 2003a ). Researchers are benefiting from more rapid access to research documents as resources such as new digital libraries and e-print archives go online almost by the week, but researchers (like almost all other professions) are also drowning in this ocean, with less time to track growing numbers of conferences, journals and reports. But beyond tracking new results, there is the whole dimension of analysing a literature. Researchers are concerned with the significance of a contribution to the literature, but no digital library can answer the obvious -but complex -questions which are fundamental to critical inquiry, and which we seek to instill in our students: • Which publications support and challenge this document? • What is the intellectual lineage of this idea? • What data is there to support this specific claim or prediction? • Who else is working on this problem? • Has this approach been used in other fields? • What logical or analogical connections have been made between these ideas? Such questions self-evidently require complex interpretative work, and moreover, there may be disagreements of different sorts. The above questions require semantic annotation at a different level from that addressed by conventional metadata or ontologically-based markup in semantic web research, which seek to iron out inconsistency, ambiguity and incompleteness in the way resources are characterised (clearly these are undesirable if the domain is uncontentious). In contrast, principled disagreement about the significance of a contribution, conflicting perspectives, new evidence that changes the world to be Buckingham Shum, et al. modelled, and the resulting ambiguities and inconsistencies are precisely what define a field as research; they are the objects of explicit inquiry. In sum, there remains a gap in the researcher's digital toolkit: tools to track (claimed) contributions in a field, and to express, analyse and contest their significance. It in this context that structured argumentation has a contribution to make to support individuals and research teams construct a picture of the key arguments in the literature from their particular standpoint. Let us now consider the detailed requirements for such a research tool. Requirements for an argumentation scheme to model naturalistic scholarly discourse "Ontologies" are the term used in knowledge modelling and agent research, and increasingly within the semantic web community, to describe a specification of concepts, attributes and relationships (Gruber, 1993). Typical ontology-based applications develop an ontology to control interpretation or semantic annotation in a specific domain of inquiry (such as an ontology of problem-solving methods) or to model a particular aspect of the world (such as organisational functions), enabling machine-to-machine interoperability and interpretation. In contrast, we propose a semiformal ontology for scholarly discourse, primarily for humans to communicate through as a medium for publishing and discourse (although we envisage agents as protagonists and claim-makers at some point), with the express goal of supporting multiple (often contradictory) perspectives. In this sense it is as much an ontology for principled disagreement. It still requires consensus in the sense that participants subscribe to the ontology as a reasonable language for "making and taking perspectives" (Boland and Tenkasi, 1995) , but in contrast to most existing ontology applications, stakeholders need not agree at all on the structure of the field being modelled. All modelling is interpretation, but when there is meant to be consensus, the end-user community is not given the option of disputing the ontology or the way in which it has been applied. In contrast, our modelling scheme makes it explicit that every contribution can be contested. This emphasis is carried through into the language of the user interface and help information, which talks about "claims", and makes clear that the system's function is to serve as a medium for supporting and contesting ideas in various ways. Buckingham Shum, et al. A representation scheme for the arguments in papers needs to achieve a fine balance between expressiveness and usability. Whilst our ontology could be designed to support automated reasoning and verification of argumentation structures of the sort offered in other computational argument modelling research systems, if the database is to be populated by domain experts from fields outside knowledge engineering it seems implausible that a critical mass of readers of research papers would feel inclined to learn such a scheme or have the confidence to publish the argument maps they built using it. Conversely, too weak a scheme will not deliver sufficient services to make it worth the readers' while to use it. We do not yet claim to have delivered a system with a large user base, such is the difficulty of negotiating this trade-off, but it is a primary constraint in the design iterations we present here. Our work derives from the tradition of hypertextual argumentation (see Buckingham Shum, 2003 for an historical account of the field's emergence). This places an emphasis on interactive semiformal representations, often with graphical renderings, which are processable by both human and software agents. We describe later how specific hypertext functionality supports argument modelling in a concept mapping tool. Our modelling scheme comprises nodes and links. Nodes may be atomic or composite at the end user's discretion. Atomic nodes 1 are expressed as short pieces of free text succinctly summarising a 'contribution' (at whatever granularity the researcher wishes to express this). For instance, an (optionally untyped) atomic node might simply be the name of a new algorithm that the researcher wishes to add to the network as a contribution, e.g.: PageRank. A different, typed atomic node might summarise an empirical result: <Data> Undergraduate chemistry exam performance is doubled after training on the ChemVR system. These are now objects (loosely analogous to published websites with URLs) which others can link to in their own work (but unlike the web) using a semantically typed link. We refer later to nodes as "Concepts" but in explaining the data model, have found that it is more helpful to refer to them in semantic hypertext language as nodes. Buckingham Shum, et al. Figure 1. Structure of a Claim in the discourse ontology. As shown in Figure 1 , an object may optionally be assigned a type (e.g. In addition to atomic nodes, two kinds of composite object can be used as the nodes in Claims. A Set is a group of objects (atomic nodes, Sets or Claims) declared by the user to share a common theme and enabling them to be referenced by a single named node (e.g. Constructivist Theories of Learning). Claim triples themselves can also be linked from or to other atomic nodes, Sets or Claims. This nesting allows users to build complex conceptual and argument structures. To illustrate claim triples, consider the following: This uses one of the General relations uses/applies/is enabled by to assert that the Decision Forest classifier studied in the paper uses, applies or is enabled by a well known method, Decision tree learning. The latter node was introduced in a different document, so this link has a contextual role: it locates the paper near similar claims. Buckingham Shum, et al. This claim uses the negative, Supports/Challenges relation is inconsistent with to link one of the experimental results of this paper to a result in a third paper. In addition to its contextual role, locating the claim near other comparisons of classifiers, this claim has a rhetorical role: it contrasts pieces of evidence that make contradictory assertions. The priority of the system in supporting multiple perspectives means that it does not add the kinds of constraints that would be expected when one can safely assume a single worldview. One researcher may think that X is an example of Y, but a peer may argue the opposite. This is the substance of research discourse, but limits the scope for automated reasoning. However, we are focusing on the argumentation level primarily, with the domain model emerging as a secondary product; other modelling efforts could focus on fields where there is consensus (or where only consistent views are modelled), and build richer, more constrained representations that can support correspondingly more advanced reasoning. A link between two nodes is typed with a natural language label from a discipline-specific dialect, which in turn is a member of a generic, discipline-independent class (e.g. Problem-related; Taxonomic; Causal). The structure of the current discourse scheme is shown schematically in Figure 2 . Our goal is to provide a given research community with a dialect that will cover the most common claims that they make (there may well be exceptional kinds of contributions that fall outside the expressiveness of the vocabulary, but the generic Other Link is available for those situations). Buckingham Shum, et al. Figure 2: Class structure of the scholarly discourse ontology Defining relations in terms of class and dialect means the same classes can be employed by research communities who speak different "dialects", or even different languages: one can change the dialect labels of the relations, without changing the underlying relational classes. Looking at the Supports/Challenges class, refutes is clearly a stronger term than is inconsistent with. Authors would be careful in their usage, particularly of stronger relations, but clearly they also both express the notion of a negative relationship between two nodes. We therefore add the explicit notions of polarity and weight (Table 1) which are predefined and used by the system, but end-users are not asked to provide these. Combining classes, dialects, polarity and weight means we can reason at a higher level of granularity than individual relations, in delivering services by working with relations which share combinations of these properties (Section 5). Buckingham Shum, et al. Table 1 . The discourse ontology with polarity and weightings. Elsewhere we have described the iteration from the first to the current version of the ontology . The relational classes were originally derived from a data-driven approach of modelling naturalistic argumentation as we found it in a range of research domains, including computer supported collaborative work, text categorization, literary criticism, genetics, philosophy of computing, applied ethics of technology, and film theory. Relations common to several domains were identified which we could classify in the classes shown above: Supports/Challenges, Problem Related, Taxonomic, Causality, Similarity, and General. Interwoven with this bottom-up approach was a theoretical strand of work, which we found enabled us to critique and validate the classes we had derived. Cognitive Coherence Relations theory (described next) provides a grounding for the relational classes, and conceives relations in pairs of opposites, such as proves and refutes, where one has positive and the other negative implications. The discourse ontology evolved through a combination of theoretical and data-driven processes. The theory-driven approach derived from psycholinguistics and computational research on Cognitive Coherence Relations (CCR), combined with a semiotic perspective on representation which emphasises the interpretive act of modelling (Mancini and Buckingham Shum, 2001; Mancini, 2003). According to CCR theory, discourse coherence is a cognitive phenomenon that goes beyond any linguistic expression. It depends on the interpreter's ability to create a coherent cognitive representation of the discourse content, by establishing coherent connections between its parts. The categories of discourse connectivity are expressed in natural language by specific indicators, but these are evidence of the deeper cognitive processes that natural language is optimised to express (Sanders and Noordman, 2000) . Comprehensive sets of parameters have been proposed (Sanders, et al., 1993; Louwerse, 2001), defining a space of relational primitives by which two discourse units can be related. The basic relations are additiveness, temporality (sequentiality) and causality. Each of these is then parameterised: additiveness can be conjunctive or comparative (similarity); causality can be actual or hypothetical (conditionality); both causal and additive relations can be semantic (e.g. cause-effect) or pragmatic (e.g. argument-claim); they can have positive or negative polarity (e.g. similarity or contrast); the order of the related units can be forward (e.g. cause-effect), backward (e.g. effect-cause) or bi-directional (e.g. list). Table 2 summarises this scheme.
REF were the first to describe one of the most comprehensive models for argumentation in scientific publications, using as links between the epistemic items Cognitive Coherence Relations.
Modelling naturalistic argumentation in research literatures: Representation and interaction design issues
International Journal of Intelligent Systems, Special
Computer Science