Frame-based System

A Frame-based System is a knowledge representation scheme that uses frames as modeling primitives.

Example(s):
Counter-Example(s)
- Production System.
See: Knowledge-based System, Semantic Network, Ontology, Class, Frame, Slot Value, Frame Language, Description Logic.

References

2017

(Wikipedia, 2017) ⇒ http://en.wikipedia.org/wiki//Frame_(artificial_intelligence)
- Frames were proposed by Marvin Minsky in his 1974 article "A Framework for Representing Knowledge." A frame is an artificial intelligence data structure used to divide knowledge into substructures by representing “stereotyped situations." Frames are the primary data structure used in artificial intelligence frame languages.

Frames are also an extensive part of knowledge representation and reasoning schemes. Frames were originally derived from semantic networks and are therefore part of structure based knowledge representations. According to Russell and Norvig's "Artificial Intelligence, A Modern Approach," structural representations assemble "...facts about particular object and even types and arrange the types into a large taxonomic hierarchy analogous to a biological taxonomy."

2007

(Obitko, 2007) ⇒ Marek Obitko. (2007). “Translations between Ontologies in Multi-Agent Systems", Ph.D. dissertation, Faculty of Electrical Engineering, Czech Technical University in Prague. http://www.obitko.com/tutorials/ontologies-semantic-web/frame-based-models.html
- Frame based systems use entities like frames and their properties as a modeling primitive. The central modeling primitive is a frame together with slots. These slots are applicable only to the frames they are defined for. Value restriction (facets) can be defined for each attribute. A frame provides a context for modeling one aspect of a domain. An important part of frame-based languages is the possibility of inheritance between frames. The inheritance allows inheriting attributes together with restrictions on them. Knowledge base then consists from instances (objects) of these frames.

An example of the usage of the frame-based model is Open Knowledge Base Connectivity (OKBC) that defines API for accessing knowledge representation systems (...)

A frame is a primitive object that represents an entity in the domain of discourse. A frame is called class frame when it represents a class, and is called individual frame when it represents an individual. A frame has associated with it a set of slots that have associated a set of slot values. A slot has associated a set of facets that put some restrictions on slot values. Slots and slot values can be again any entities in the domain of discourse, including frames. A class is a set of entities, that are instances of that class (one entity can be instance of multiple classes). A class is a type for those entities. Entities that are not classes are referred to as individuals. Class frames may have associated a template slots and template facets that are considered to be used in instances of subclasses of that class. Default values can be also defined. Each slot or facet may contain multiple values. There are three collection types: set, bag (unordered, multiple occurrences permitted), and list (ordered bag). A knowledge base (KB) is a collection of classes, individuals, frames, slots, slot values, facets, facet values, frame-slot associations, and frame-slot-facet associations. KBs are considered to be entities of the universe of discourse and are represented by frames. There are defined standard classes, facets, and slots with specified names and semantics expressing frequently used entities.

1999

(Nebel, 1999) ⇒ Bernhard Nebel, (1999). https://www.csee.umbc.edu/courses/771/papers/nebel.html
- Frame-based systems are knowledge representation systems that use frames, a notion originally introduced by Marvin Minsky, as their primary means to represent domain knowledge. A frame is a structure for representing a CONCEPT or situation such as "living room" or "being in a living room." Attached to a frame are several kinds of information, for instance, definitional and descriptive information and how to use the frame. Based on the original proposal, several knowledge representation systems have been built and the theory of frames has evolved. Important descendants of frame-based representation formalisms are description logics that capture the declarative part of frames using a logic-based semantics. Most of these logics are decidable fragments of first order logic and are very closely related to other formalisms such as modal logics and feature logics.

In the seminal paper "A framework for representing knowledge," Minsky (1975) proposed a KNOWLEDGE REPRESENTATION scheme that was completely different from formalisms used in those days, namely, rule-based and logic-based formalisms. Minsky proposed organizing knowledge into chunks called frames. These frames are supposed to capture the essence of concepts or stereotypical situations, for example being in a living room or going out for dinner, by clustering all relevant information for these situations together. This includes information about how to use the frame, information about expectations (which may turn out to be wrong), information about what to do if expectations are not confirmed, and so on. This means, in particular, that a great deal of procedurally expressed knowledge should be part of the frames. Collections of such frames are to be organized in frame systems in which the frames are interconnected. The processes working on such frame systems are supposed to match a frame to a specific situation, to use default values to fill unspecified aspects, and so on. If this brief summary sounds vague, it correctly reproduces the paper's general tone. Despite the fact that this paper was a first approach to the idea of what frames could be, Minsky explicitly argued in favor of staying flexible and nonformal.

1975

(Minksky, 1975) ⇒ Minsky, Marvin (1975). A framework for representing knowledge. http://papers.cumincad.org/cgi-bin/works/_id=ecaade2013/Show?7a2a
- Briefly describes frame systems as a formalism for representing knowledge and then concentrates on the issue of what the content of knowledge should be in specific domains. Argues that vision should be viewed symbolically with an emphasis on forming expectations and then using details to fill in slots in those expectations. Discusses the enormous problem of the volume of background common sense knowledge required to understand even very simple natural language texts and suggests that networks of frames are a reasonable approach to represent such knowledge. Discusses the concept of expectation further including ways to adapt to and understand expectation failures. Argues that numerical approaches to knowledge representation are inherently limited.