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Knowledge Integration with Conceptual Blending

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Knowledge Integration with Conceptual Blending Knowledge Integration with Conceptual Blending Keywords: Cognitive Modeling Conceptual Blending Metaphor Abstract In a subsequent paper, [Veale and O’Donogue, 1999] present the resulting computational model in more detail, which re- In this paper, we present our most recent work on lies mainly on the metaphor mapping engine, Sapper, to es- the integration of different domains of knowledge tablish a dynamic blend between two domains. This blend, into a single domain. The construction of this new rather than being an independent new domain, becomes a domain, the Blend, is inspired on the theory of Con- unifying set of correspondences of concepts from both do- [ ceptual Blending, of Fauconnier and Turner, Fau- mains, built according to a constructor space. As we can see, ] connier and Turner, 1998 , and is formalized in de- in this work, the blend is embeded in the structure mapping tail. As we will discuss later, a blend of two do- itself, instead of becoming an independent new domain. An- mains won’t consist of their sum or juxtaposition. other interesting work is that of [Leite et al.,2000], which ap- Instead, this new domain will have its own structure plies Dynamic Logic Programming [Alferes et al., 1998] to and semantics, which, on one side, brings problems metaphor based knowledge integration. In this work, the au- of interpretation and validation of emergent con- thors use a metaphor mapping function to obtain correspon- cepts, but on the other side represents a promising dences between the two input domains (the tenor and the ve- space for generation of new ideas and solutions. hicle) and generate a third one, the update, that contains the tenor and the projection of the vehicle such that inconsisten- 1Introduction cies between newly created facts are removed. Therefore, it yields a composition of the tenor and the vehicle, which, al- One particularly interesting feature of human cognition is the ¿ ability to manage with a considerably large amount of knowl- though only super cially, the authors call a blend. Each do- edge, coming from a wide variety of sources, semantically main consists of a logic program and the result will also be distant from each other. Yet, we seem to easily combine ap- a logic program. As the reader will notice, our approach is parently unrelated information and, according to [Guilford, quite compatible to this one, since the general architecture is 1967], this is a fundamental process in Human Creative Cog- similar: given two input domains, generate a third one recur- nition. Computationally, we argue that it would be useful to ring to a mapping function. The main difference lies in the use a multi-domain knowledge base in such a way that infor- blend itself, which in our case allows for co-reference of con- mation from one domain could be transferred and applied in cepts from both input domains at the same time, avoiding the other, different, domain. To be able to do so, it is essential necessity of giving priority of one domain over the other. ¿ to have some sort of unifying process, as suggested by the As far as we know, we present the rst formalization of Conceptual Blending Theory[Fauconnier and Turner, 1998]. this theory from a computational perspective. In section 3, Conceptual Blending (CB) is rapidly emerging as a ma- we describe our Blender in detail and apply it to a well known jor force in Cognitive Science, and is increasingly gathering structure mapping example: the heat-water analogy of Dedre several researchers from a diversity of areas. This theory, Gentner’s paper[Gentner, 1983]. which combines a set of processes and principles applied to We feel our work as part of the continuum of evolution of entities named mental spaces, has brought some new light CB theory and its implementations. In our model, we expect to analogy, metaphor, counterfactual reasoning, natural lan- two input domains, each one de¿ned according to two levels: guage processing and creative cognition[Oakley and Coulson, the Domain Theory and the Domain Instances. A structure 1999]. In this paper, we will focus on some practical aspects mapping function is applied to the Domain Theory, which that are important to our contribution, which is essentially will then be the linking basis that allows the construction of centred on creative cognition and modeling. We can ¿nd pre- the blend. This will lead to the creation of a new domain, the vious valuable work on this topic. [Veale, 1997], for example, Blend, which will have its own Domain Theory and Domain presents his model, Pastiche, of CB and applies it to cinematic Instances, as we will discuss in section 4. borrowing. He exploits the domain of Hollywood ¿lm indus- In the next section, we will give an overview of the CB try, which is full of blending examples, to elucidate the re- theory to give the reader some necessary background to un- quirements of a computational model of metaphoric blending. derstand the paper. As we see from our experience, this new computer-virus domain has a life of its own, although we still perceive eas- ily the connections and dependencies to the input domains. Concepts like ”cracking the virus” are so deeply entrenched in this new domain that sentences such as ”the Ebola virus hasn’t been cracked yet” can only be interpreted from in its context. The Blend has emergent structure not provided by the in- puts. This happens in three (unrelated) ways[Fauconnier, 1997]: 1. Composition - Taken together, the projections from the Figure 1: Conceptual Blending of Input 1 with Input 2 inputs make new relations become available that did not exist in the separate inputs 2. Completion - Knowledge of background frames, cogni- 2 Conceptual Blending Theory tive and cultural models, allows the composite structure pro- jected into the blend from the inputs to be viewed as part of a Conceptual Blending was initially proposed by [Fauconnier larger self-contained structure in the blend. The pattern in the and Turner, 1998], and its value has been increasingly ac- blend triggered by the inherited structure is ”completed” into knowledged as a wider range of researchers is becoming in- the larger, emergent structure. terested in studying it. The works of [?][Sweetser and Dan- 3. Elaboration - The structure in the blend can then be cygier, 1999] [Coulson, 1997] and [Veale and O’Donogue, elaborated. This is ”running the blend”. It consists of cog- 1999] are examples of how this theory is an important con- nitive work performed within the blend, according to its own tribution to Linguistics, Creative Cognition, Analogy and emergent logic. Metaphor. To explain it in some detail, we must introduce It is also common to ¿nd blends in artistic creativity. Muss- the concept of Mental Space. According to [Fauconnier and gorsky’s ”pictures of an exhibition”(inMusic)orKandinsky’s Turner, 1998], Mental Spaces are partial structures that pro- ”Improvisations” (in Visual Arts) are two examples of a com- liferate when we think and talk, allowing a ¿ne-grained parti- mon tendency of artists to seek for ideas in different domains. tioning of our discourse and knowledge structures. As we talk Blending is also common in scienti¿c discovery, an interest- or think, our reasoning focus Àows from space to space, trans- ing example of which is the relatively recent AI paradigm of porting and mapping concepts according to points of view, Evolutionary Computation, that results from blending new- presuppositions, beliefs, changes of mood or tense, analogi- Darwinist theories with the problem of function optimization. cal counterfactuals and so on, each giving birth to a different mental space. Blending is generally described as involving two input 3TheBlender mental spaces that, according to a given structure mapping, will generate a third one, called Blend. This new domain We will now present our model for the application of CB the- will maintain partial structure from the input domains and add ory to multi-domain integration. This model has the funda- emergent structure of its own. mental goal of blending two different domains into a new As can be seen in ¿gure 1, a generic space is also consid- third domain. Therefore, we consider a domain as a sub- ered. This can be seen as having a uni¿cation role, such that type of Fauconnier’s mental space and we generate a blend concepts mapped onto each other are considered as belonging through the application of a mapping between two input do- to the same, generic, concept. mains, recurring to an intermediate, mediating domain, the Take, as an example, the notion of computer virus. The generic domain. concept of virus comes from biology and its entry in dictio- Each domain consists of a combination of two kinds of naries generally corresponds to ”Any of various simple sub- knowledge: the Domain Theory and the Domain Instances. microscopic parasites of plants, animals (...) and that consist A Domain Theory is de¿ned through relational and pro- of a core of DNA or RNA surrounded by a protein coat. Un- cedural knowledge. Relational knowledge is represented in able to replicate without a host cell, viruses are typically not the form of a Concept Map that discriminates relationships considered living organisms.” between concepts of the domain. Computer science, a much more recent subject than biol- ogy, brought to life concepts like program, program execu- De¿nition 1 Let O be a Language and OF 5 O be a set of tion, user, terminal, etc. Quite surprisingly (at least initially), symbols (the conceptv). Let OU 5 O be another set of sym- these two domains were mapped onto each other in such a bols (the relations).
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