1 Introduction An interesting practical problem for argumentation min- ing is the detection of argument in a specific social or cul- tural context. Communicative-rhetorical actions may look like argument with overt or contentious linguistic markers (e.g., ‘well’, ‘but’, ‘that’s stupid’, ‘I disagree’) but may not function as argument. Communicative-rhetorical actions may include or point to reasons but without any obvious argumentative function (e.g., explaining, clarifying). More- over, reasoning to resolve a difference can happen implicitly among participants with only traces of the jointly owned reasoning evident in the language use. The challenge be- comes how to mine in a way that excludes language that only appears to be argumentative, while including the non- obvious uses of language for argument. A response-centered approach for the context sensitive discovery and classification of argument in argumentation mining is outlined here. It is built around a conceptualization of argument as the use of language and reasoning in the context of disagreement for the purpose of managing disagreement or resolving differences of opinion (e.g., Jackson and Jacobs, 1980). The novelty of the approach is its basic upper ontology, which is designed on the idea that the argumentative use of reasons arise in the context of disagreement. The primary relation to be identified in argument mining is when a communicative- rhetorical action targets a prior action and calls out and makes problematic what has (could have) been said, meant, or implied. From here lower levels of ontology can be built in a principled way that can also incorporate insights of various argument formalisms (e.g., schemes, dialogues). 2 Theoretical Grounds The response-centered approach builds from three key in- sights of argumentation theory and practice. First, the argumentative functions of language use are not merely conveyed through a homogenous class of linguistic forms. “Instead of an isolable and homogeneous speech act, one finds a family of act types that vary in function and pragmatic logic depending upon the context of their use and the form of their expression” (Jacobs, 1989, p. 350). The pragmatic context of those addressed, the practical activity and its discourse, and the dialogue activity factor into what constitutes argument. Second, the pragmatics of interaction is consequential for what is argued and how, especially the way in which re- sponses and countermoves take up and develop (or not) the propositional content at issue (Jacobs and Jackson, 1992). What is classically considered the essence of argument -- the fixing of the propositional content -- is often unex- pressed, implicit, inarticulate, or simply taken for granted as understood. Third, argumentative discourse unfolds sequentially while depending upon networks of overarching presump- tions and underlying assumptions (Aakhus, Muresan, and Wacholder, 2013). The dynamic relationship between the explicit sequence of language use in interaction and the tacit network of assumptions and presumptions that are used ar- gumentatively is known as “disagreement space” (van Eemeren, Grootendorst, Jackson, and Jacobs, 1993; Jackson, 1992). Argument mining must contend with the creative uses of language, pragmatics of interaction, and the disagreement space – that is, arguing as a process not just argument as a product. 3 Ontology Design A response-centered approach places a premium on identifying what is targeted and how it is called out as the means to discover actual argumentative uses of language and reason. Such an approach seeks to maximize the power of current Natural Language Processing methods while supporting the development and refinement of those methods for the discovery of argument practices. Ontology here refers to a set of concepts and relations among those concepts, a view that aligns with work on developing argumentation ontologies for the semantic web (e.g., Tempich et al., 2005). An Argument-Ontology for a Response-Centered Approach to Argumentation Mining Mark Aakhus 1 , Smaranda Muresan 2 and Nina Wacholder 1 1 School of Communication and Information, Rutgers Univerity, New Brunswick, NJ, USA 2 Center for Computational Learning Systems, Columbia University, New York, NY, USA {aakhus@rutgers.edu,smara@columbia.edu,ninwac@rutgers.edu} Proceedings of CMNA 2016 - Floris Bex, Floriana Grasso, Nancy Green (eds) 40