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And if this is the case, philosophical accounts of understanding that assume that the basic representational units are word-sized concepts and sentence-sized beliefs or judgments are missing something crucial about our cognitive architecture: that in some sense the basic unit of understanding is actually a mental model of a domain of some size, which is larger and semantically richer than a belief, sentence or proposition, but considerably smaller than the holist's comprehensive web of concepts, beliefs, and inferential dispositions.

I would say, in addition, that models generate a space of possible propositional representations using the concepts involved in the models, and in this sense models are prior to language-like propositional representations.

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A model-based account of cognition probably has to posit a great many models of different content domains. And in this sense, model-based approaches would seem to lead to a type of dis unity claim in the form of a model-pluralism, which I have dubbed "Cognitive Pluralism" Horst , And if you assume, as I do, that models are optimized individually to be good-enough for particular epistemic and practical purposes, there is every reason to expect that different models will sometimes license different conclusions, thus generating inconsistencies that one might count as a form of epistemic disunity.

But this is not the kind of "unification" that Danks is after. His claim, rather, is that cognition is "unified" in the sense that understanding is all or mostly, or at least to a significant extent encoded in a particular type of model, the graphical model. Graphical models are a single type of data structure that is flexible enough to be able to be used for many types of representational and computational problems, a claim which Chapters are devoted to making plausible through treatment of a significant though non-exhaustive list of cases.

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There are presumably many individual models, and we are continually updating them, but they are all of the same formal type, and a single model may be operated upon by many different reasoning processes. And this is very different from a modular architecture in which each module has its own proprietary types of representations which are operated upon only by a distinctive set of processes, even if modules also produce outputs into a "central cognition" system with its own set of domain-general processes.

There are two questions that I found myself left with upon completing the book. The first is just how general the claim that cognition is based in graphical models is supposed to be. The language of "unifying the mind" might suggest that Danks is claiming that all cognition is based in graphical models. But often he makes more guarded versions of the unifying claim, such as that "large swaths of human cognitive activity" p. Indeed, in at least one case, he says that graphical models are not suitable.

In this light, we should probably assume that his view is that it is ultimately an empirical question whether any particular type of cognitive activity can be explained as an operation upon a graphical model, in which case his unification claim should be regarded as a kind of working hypothesis for which he has already provided partial justification. The second question is about just what we are committed to if we say that cognitive processes operate over representational structures that "are" graphical models. Danks acknowledges the worry that this might be interpreted as little more than a claim for the utility of a particular mathematical description, though he seems to favor a more realist interpretation of the status of graphical models as representations.

Indeed, some passing remarks on representations suggest that he may favor a somewhat more robust "representation realism" than I would.

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I propose a particular cognitive architecture in which many of our cognitive representations are well modeled by graphical models. This account is committed to the realism of these representations but is largely agnostic with regard to realism about the processes though there must be suitable processes that can use the information encoded in the representations.

More specifically, the account proposes that there are persistent objects in the mind that subserve a wide range of cognitive processes, but where the precise processing method might, but need not, be identical in all domains or all contexts. The account thus places both upward constraints on accounts of human behavior e. This passage starts out sounding like an endorsement of a realism about representations as "persistent objects", which conjures images of the model-based equivalent of symbolic representations of data or stored programs in computer memory.

But the last sentence softens this interpretation by including persistent dispositions among the possible realizers of mental models. This ambiguity is repeated in a later chapter.

Unifying Class-Based Representation Formalisms

Danks seems to criticize connectionist models for lacking persistent representations because their equivalent of concept-activations appear only evanescently in the hidden layers:. Connectionist networks contain no persistent mental objects that could play the role of representations; cognition instead involves the distributed transformation of distributed information without any explicit or symbolic representation of entities and properties in the mind and the world. Of course, there is a sense in which the hidden unit activation levels at some particular moment do "represent" a particular dog in that moment, but this form of "representation" is radically different from that assumed by cognitive architectures based on discrete, persistent symbols.

At the same time, it certainly seems as though we do have persistent cognitive representations, and so something also seems to be wrong with the connectionist picture. However, two pages later, he seems to want to leave open the possibility that models are, as we might say, functionally emergent from the behavior of the brain:. I deliberately did not add any commitment that we have the ability to point toward particular objects in our brains, since we know too little about how the brain both represents and processes information about the world.

As a result, my representation realism requires only that people behave in systematic and systematically interpretable ways that are best explained as operations on graphical models. That is, I commit myself to a realism about representations that is entirely consistent with them being neutrally distributed or emergent, as long as the distributed representations are appropriately stable across tasks and environments.

I tend to favor the broader interpretation, not only because I agree with Danks that a theory cast at the level of cognitive architecture should leave questions of implementation open though constrained , but also because I think that what a model-based theory commits us to is something on the order of " having a model" being able to think in ways corresponding to a model-based description , without any necessary commitment to there being "entities" except perhaps in the broadest and most abstract sense that are models.

Reasoning and Unification over Conceptual Graphs by Dan Corbett, Paperback | Barnes & Noble®

This is an interesting and engaging book. There are sections that will be hard going for readers not already familiar with such things as Bayesian nets and Markov assumptions. But the exposition of graphical models and the unification claim can be understood without this background, and Danks has provided one of the few book-length philosophical examinations of a model-based approach to cognition, and this fact in itself is enough to make it an important contribution.

Collins, Allan M. Retrieval Time From Semantic Memory. Journal of Verbal Learning and Verbal Behavior 8 2 : To complete the notion of a semantic field must be known i. If the referent field is null, the colon is connect a conceptual relation and its adjacent concept s. There exist eleven types of concepts that There are at least two ways of displaying a graph.

The following is the linear form display of the graph that represents 'A person possesses Generic red ball s and plays cricket in the playground. However, for convenience purposes, the the following graph expresses that 'There exists a set asterisk may be dropped, resulting in a null referent of persons who possesses ball s '. Rather, the notion of a generic concept requires that there exists such a concept in the world being Note that the notion of a generic set specifies the modelled, but gives no further information e. Generic concept is the most commonly used type in Canonical Graph Models. It specifies that there exists a finite set of identified elements as the referent of the concept. For example, the following graph expresses that 'John, example, the graph shown above can be taken to mean Billy and Joe possess ball s '. Persons possess a ball. Partially specified set Persons possess balls. This is denoted by placing the result of the union of If one wants the previous graph to have a more specific an individual set and a generic set as the referent. It meaning, then more relations and concepts have to be specifies that there exists a set of identified elements added to the graph.

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  6. For example, the following graph plus some unidentified elements for the concept. For specifies that 'A person possesses a ball'. It specifies that the interpretation of the concept has to be repeated for each element in the set and only one of these interpretations Individual is valid.

    For example, the following graph expresses that An individual concept is indicated by having an unique 'John possesses a ball or Joe possesses a ball'. An individual concept corresponds to a particular and unique instance of a modelled entity. Sowa's notations of an individual [P. In Canonical Graph Models, numerals are Respective set not allowed as referents, and all referents of concepts This is denoted by adding a 'resp' immediately before must be an alphanumeric string beginning with a non- the referent for an individual set plus substituting the numeric character.

    However, each node i. It relation, graph in the knowledge base has an unique specifies that the interpretation of the concept has to identification number recorded in the system, but these be repeated for each pair of corresponding referents that numbers are transparent to the user in most circum- occurs in concepts of such type, and all such interpre- stances. For example, the following graph expresses that tations are valid. For example, the following graph 'The person John possesses ball s '. This is denoted by placing an entire graph into the refer- It specifies that there does not exist any instance of ent field of a concept.

    It specifies that the semantic infor- a concept. For example, the following graph expresses mation associated with the concept in the graph is that 'There exists no person who possesses ball s '. For example, the graph in Figure 3 represents 'John believes that Mary is hungry'.

    It specifies that the semantic informa- It specifies that there exists a set of unidentified tion associated with the concept in the graph is inter- elements as the referent of the concept. Descriptions of OBJ these knowledge types are omitted as they are not particularly relevant to the rest of this paper.

    Finally, a 'canonical graph' is one that has valid mean- HUNGRY ing in the domain that is being modelled, where a 'con- ceptual graph' may not necessarily have a valid meaning. Propositions as nested graphs Figure 3. A concept may have more than one nested graph as its referent. The theory and examples in this paper refer to universally quantified graphs and vanabies. An universal quantifier V can be Figure 4.

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    As a iurther example, the following graph contained in the concept. For example, the graph in represents 'There exists a person who is hungry' whereas Figure 4 represents 'John believes that Mary is hungry and he also believes that Jill is poor'. In this paper, nested the following represents the fact that 'It is not true that graph s is are also referred to as a 'complex referent' there exists a person x and x is not hungry', which for a concept.

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    It specifies that the referent is a variable In contrast to Sowa's assumption I about OBJ the existential properties of conceptual graphs, we """ By assuming a graph to be univer- IBAL":Vl sally quantified, the algorithms and examples presented In the Extendible Graph Processor5, concept labels are in this paper can be better understood, and the pictorial related in a hierarchical order through the use of a 'type display of graphs can be greatly simplified without limit- hierarchy'. Because of their structure, knowledge graphs capture facts related to people, processes, applications, data and things, and the relationships among them.

    They also capture evidence that can be used to attribute the strengths of these relationships. This is where the context is derived from. An important question: what separates knowledge graphs from data lakes or data warehouses? The answer is operational convenience. When knowledge graphs are thought about this way, it becomes clear why a knowledge graph is so important for AI. A named-entity recognition component, trained on eBay queries, is used to identify brown as the color, leather as the material, and Coach as the brand.

    Once the intent, object and characteristics of the object are known, the data is mapped to eBay inventory using a Knowledge Graph KG. The KG encapsulates shopping behavior patterns on eBay to bridge the gap between the structured query and behavior data.

    Powering Graph Data with Reasoning and Visual Analytics: Examples from Industry. Thorsten Liebig

    In other words, the KG helps figure out the best follow-up questions to ask in order to find the best results in the least amount of time. This is context for user-centered AI and why I believe knowledge graphs are going to be so fundamental to modern AI systems. Meanwhile, the graph accumulates contextual knowledge with each conversation.

    Context requires connections, and graphs — as complex systems — offer the highest level of context. Customers like eBay have shown us that bigger, more connected graphs, driven by smarter contextualization algorithms are the foundation of valuable AI systems. Knowledge graphs have actually existed in the enterprise for a while, with the two classic cases being for knowledge workers or traditional enterprise applications. As organizations accumulate historically high volumes of data, the need to synthesize that data to make strategic business decisions is more critical than ever before.

    There is a name for businesses who glean insights from connected data a system of data points working together as a single fabric — a connected enterprise. Those enterprises are ripe for utilizing knowledge graphs to accelerate delivery of AI applications for their organization. That is the difference that makes a company a connected enterprise, and what will ultimately drive the next wave of competitive advantage through AI.

    About the author: Jim Webber is Chief Scientist at Neo4j working on next-generation solutions for massively scaling graph data. Prior to joining Neo Technology, Jim was a Professional Services Director with ThoughtWorks where he worked on large-scale computing systems in finance and telecoms.