On the Existence of Primitive Meaning Units

Knowledge representation schemes are either based on a set of primitives or not. The decision of whether or not to have a primitive-based scheme is crucial since it affects the knowledge that is stored and how that knowledge may be processed. We suggest that a knowledge representation scheme may not initially have primitives, but may evolve into a prlmltive-based scheme by inferring a set of primitive meaning units based on previous experience. We describe a program that infers its own primitive set and discuss how the inferred primitives may affect the organization of existing information and the subsequent incorporation of new information. i. DECIDING HOW TO REPRESENT KNOWLEDGE A crucial decision in the design of a knowledge representation is whether to base it on primitives. A primitive-based scheme postulates a pre-defined set of meaning structures, combination rules and procedures. The primitives may combine according to the rules into more complex representational structures, the procedures interpret what those structures mean. A primltive-free scheme, on the other hand, does not build complex structures from standard building blocks; instead, information is gathered from any available source, such as input and information in previously built meaning structures. A hybrid approach postulates a small set of pro-defined meaning units that may be used if applicable and convenient, but is not limited to those units. Such a representation scheme is not truly prlmitive-based since the word "primitive" implies a complete set of pre-deflned meaning units that are the onl 7 ones available for construction. However, we will call this hybrid approach a primitive-based scheme, since it does postulate some pro-defined meaning units that are used in the same manner as primitives. 2. WHAT IS A PRIMITIVE? All representation systems must have primitives of some sort, and we can see different types of primitives at different levels. Some primitives are purely structural and have little inherent associated semantics. That is, the primitives are at such a low level that there are no semantics pre-deflned for the primitives other than how they may combine. We call these primitives structural primitives. On the other hand, semantic primitives have both structural and semantic components. The structures are defined on a higher level and come with pre-attached procedures (their semantics) that indicate what they "mean," that is, how they are to be meaningfully processed. What makes primitives semantic is this association of procedures with structures, since the procedures operating on the structures give them meaning. In a primitive-based scheme, we design both a set of structures and their semantics to describe a specific environment. There are two problems with pre-defining primitives. First, the choice of primitives may be structurally inadequate. That is, they may limit what can be represented. For example, if we have a set of rectilinear primitives, it is difficult to represent objects in a sphere world. The second problem may arise even if we have a structurally adequate set of primitives. I_n this case the primitives may be defined on too low a level to be useful. For example, we may define atoms as our primitives and specify how atoms interact as their semantics. Now we may adequately describe a rubber ball structurally, hut we will have great difficulty describing the action of a rolling ball. We would like a set of semantic primitives at a level both structurally and semantically appropriate to the world we are describing. 3. INFERRING AN APPROPRIATE PRIMITIVE SET Schank [1972] has proposed a powerful primitive-based knowledge representation scheme called conceptual dependency. Several natural language understanding programs have been written that use conceptual dependency as their underlying method of knowledge representation. These programs are among the most successful at natural language understanding. Although Schank does not claim that his primitives constitute the only possible set, he does claim that some set of primitives is necessary in a general knowledge representation scheme. Our claim is that any advanced, sophisticated or rich memory is likely to be decomposable into primitives, since they seem to be a reasonable and efficient method for storing knowledge. However, this set of after-thefact primitives need not be pre-defined or innate to a representation scheme; the primitives may be learned and therefore vary depending on early experiences. We really have two problems: inferring from early experiences a set of structural primitives at an appropriate descriptive level and learning the semantics to associate with these structural primitives. In this paper we shall only address the first problem. Even though we will not address the semantics attachment task, we will describe a method that yields the minimal structural units with which we will want to associate semantics. We feel that since the inferred structural primitives will be appropriate for describing a partitular environment, they will have appropriate semantics and that unlike pro-defined primitives, these learned primitives are guaranteed to be at the appropriate level for a given descriptive task. Identifying the structural primitives is the first step (probably a parallel step) in identifylng semantic primitives, which are composed of structural units and associated procedures that 81ve the structures meaning. This thesis developed while investigating learning strategies. Moran [Salveter 1979] is a program that learns frame-like structures that represent verb meanings. We chose a simple representative frame-like knowledge representation for Moran to learn. We chose a primitive-free scheme in order not to determine the level of detail at which the world must be described. As Moran learned, its knowledge base, the verb world, evolved from nothing to a rich interconnection of frame structures that represent various senses of different root verbs. When the verb world was "rich enough" (a heuristic decision), Moran detected substructures, which we call building blocks, that were frequently used in the representations of many verb senses across root verb boundaries. These building blocks can be used as after-the-fact primitives. The knowledge representation scheme thus evolves from a primitivefree state to a hybrid state. Importantly, the building blocks are at the level of description appropriate