Creating new vocabulary, so long as it is properly defined and axiomatized, is an inexpensive way to make inferencing in Cyc easier. Following the “Rule of 10″, if 10 interesting things can be said about a concept, then it is probably a constant worth reifying in the KB.
Imagine that of the object denoted by the constant #$JoesDog, the following statements are true:
(hasAttributes JoesDog BlackColor) (hasAttributes JoesDog Viciousness) (hasAttributes JoesDog Hungry) (hasAttributes JoesDog Healthy)
If hasAttributes is the only predicate relating objects to attributes they might have, that predicate could quickly become overloaded. Searching the KB for all the things that are black could take a seriously long time. Inferencing is made simpler, then, by making spec-predicates of hasAttributes.
(mainColorOfObject JoesDog BlackColor) (personalityAttributes JoesDog Viciousness) (hungerLevelOf Hungry JoesDog) (stateOfHealth JoesDog Healthy)
Instead of running the query
(hasAttributes ?X Viciousness)
in the inference engine, which would require Cyc considering the entire predicate extent of hasAttributes in order to find all vicious things, this query can be simplified:
(personalityAttributes ?X Viciousness)
The predicate extent of personalityAttributes is considerably smaller than the predicate extent of hasAttributes. This query would limit the inference space, and, as a result, the time it takes to run a query.
Creating new vocabulary, such as quite precise spec-predicates, drastically increases Cyc’s reasoning power. Furthermore, since these predicates are spec-preds of hasAttributes, it is still possible to collect together all the characteristics of Joe’s dog. Assuming that all the predicates used earlier are spec-preds of hasAttributes, the query
(hasAttributes JoesDog ?ATT)
would return the following bindings for ?ATT:
BlackColor Viciousness Hungry Healthy
Likewise, it is difficult for Cyc to reason with long and complicated rules.
(implies (and (isa ?DOG Dog) (carriesInfectionType ?DOG Rabies) (thereExists ?BITING (and (isa ?BITE Biting) (doneBy ?BITING ?DOG)))) (thereExists ?TRANS (thereExists ?HOST (and (isa ?TRANS InfectionTransmissionEvent) (isa ?HOST Animal) (transmissionFromHost ?TRANS ?DOG) (transmissionToHost ?TRANS ?HOST)))))
This rule is almost certainly functionally useless. The only way this rule would be called, first of all, is in the case that we are querying the consequent, which would be highly unlikely. This rule is not reusable: it is called only in very few cases.
Second, even if it is true that every bite done by a dog with rabies causes a transmission of the rabies infection, isn’t this also true for any animal with rabies? Since the only performers of biting events are animals or insects, the (isa ?DOG Dog) is unnecessarily and inappropriately limiting.
Smaller, simpler, more modular rules would be more effective.
Rules should be short: in order to use a rule in inference, Cyc must check all the conditions in the antecedent to see if there are bindings for the variables that cause the antecedent to be true. Thus, if there are ten variables in the antecedent, the inference engine must check to see if there are bindings for all ten variables. This could take quite a long time to determine. Chances are, a large and awkward rule can be broken down into many smaller rules, and, ideally, GAFs, which do not require backchaining in inference.
(implies (and (carriesInfectionType ?ANIMAL Rabies) (thereExists ?BITE (and (isa ?BITE Biting) (doneBy ?BITE ?ANIMAL)))) (thereExists ?TRANS (isa ?TRANS TransmissionEvent)))
This rule is still rather unwieldy with several variables to bind in the antecedent, and it does have an existential quantification in the consequent, but it is much simpler and more reusable than the first. Furthermore, the transmission event from the consequent in this rule can be added to the antecedent of another rule.
A better way to express this might be to create a predicate that relates an infection type to actions that cause its transmission. This would then create small, managable rules as opposed to frightening, non-modular, and inferentially inert rules.
One of the paramount concerns of ontologists must be the reusability of their work. Since logical correctness underdetermines the logical form used to express any particular set of propositions, other considerations must also be taken into account when making choices about the representation of a domain. These choices can lead to knowledge which is represented in an idiosyncratic way that is not useful for purposes which are even slightly more general than those which the author had in mind when the domain was originally designed.
This is one of the major areas where the engineering aspect of ontological engineering comes into play. In many ways, this is like software engineering more generally.
The first thing to realize is that, in designing software, we cannot expect to anticipate all of our future needs. This is true as well for knowledge represented in the KB. If we cannot do a perfect job of anticipating all of our needs then the next best thing is to design systems which can be easily extended to handle later requirements. One of the main tools which is used to ensure this in software engineering is the use of hierarchically ordered structures and inheritance. Since all of the concepts in the knowledge base are ordered in this way we receive much of the benefit these approaches provide for extensibility already. This is one reason, however, why it is generally worth a little extra care in making sure that the hierarchy itself is solid (see Finding the Right Level of Generality).
The above ongoing additions are much of what is done under the heading of software maintenance. Once it is realized that most software will require maintenance of this kind the problem reduces to one of trying to do a good job of making it easy to identify components of a system and for components to work with each other. It is to handle this problem that software engineering emphasizes the value of modularity and interfaces. By creating a number of stand-alone components which provide stated functionality we can create an environment in which developers can rely on the functions contained in the published interfaces of modules while leaving the maintainer of that module free to modify the inner workings of that module at will (since no one else has access to it).
This kind of modularity should be seen as a desirable goal of ontological as well as software engineering, but currently there are not clear mechanisms for implementing modular additions to the knowledge base. There are, however, several things that can be done to help with this problem. We currently use microtheory space to divide up our workspace (among many other things). It is hoped that, over time, we will add new ways of identifying public and private work in the knowledge base as well as ways to achieve the benefits of carefully designed modules and interfaces between them. Here are a few of the mechanisms currently used for this purpose that take advantage of microtheory inheritance in order to achieve some of these benefits.
By placing knowledge in microtheories which correspond to natural divisions in how we expect that knowledge to be used we can segregate rules about a particular domain allowing them to be modified without altering other areas of the knowledge base. In order for this to work it is important that we ensure that microtheories do not “over-inherit” from others in order to minimize the impact of later changes. This segregation is usually done by domain area though there are other effective ways of doing this as well. In particular, data can often be usefully collected in microtheories dependent on either the content or the source of the data. This allows the data to be used together in contexts where it is appropriate and to be excluded from contexts in which it is not.
While working on new domains it is a good practice to make sure that the newly added knowledge is located in a part of the microtheory hierarchy where it is unlikely to interfere negatively with others’ work. This can be accomplished by putting the information into a temporary microtheory which is not used by anyone else. This information can be moved to the microtheory in which it belongs when it has been completed and tested (remember it is easier to merge microtheories than to split them).
Additionally, it is important to consider the right level of generality when constructing rules. By making rules as general as possible (without permitting untrue cases), unanticipated applications can be accommodated. In general, short, satisfiable rules are to be preferred over longer, more specific ones. For more information on this topic, please see the Cyc101 lecutre on “Modularity and Reusability”.
It is easy to believe that English translates neatly into CycL because CycL terms are simple to create, and Cyclists strive for the names of CycL terms to be reasonably recognizable based on the English meaning of words. In truth, however, very few common English words delineate exclusively one concept. In extreme cases, an English word can be represented by dozens of CycL terms which may or may not be related. The English term may fit in more than one part of speech, and the meaning of the term can depend on the context. Since our goal is to define completely any constant with axioms, it is most appropriate to have a single Cyc constant represent a single concept. Thus, it would be inappropriate to use the same CycL constant #$Bank for the same English word in the following two sentences:
“The banks of Mississippi have high interest rates.”
“The banks of the Mississippi are composed of very rich soil.”
Fortunately, the two distinctly different concepts are represented by different Cyc constants. The most appropriate CycL translation of “bank” in the first sentence would be #$BankCompany, while #$RiverBank would be the best translation of “bank” in the second.
This case may be obvious because there is really little commonality between a river bank and an investment company, but all the definitions of vague English words, for example “game”, have a little more in common. Axiomatizing the word without regard to the distinctly different concepts it represents will result in an ill-defined constant that is relatively useless. Therefore, it is important to tease out precisely which concepts are contained in a single English word in order to axiomatize the concept, not the word.
Consider the case of the English word “has”. We might be tempted to believe that this is best represented by some sort of possessive relation. When we say that “John has the flu”, “John has blue eyes”, “John has an idea”, and “John has $50″, there seems to be some sort of possession concept in common between the four uses. But once we try and tease out, through rules, what that possession concept may be, we find ourselves unable to say anything useful about the strange predicate in the various cases. We cannot say that if John “has” something, he owns it: how would he own the flu? Nor can we assert that having something implies that it is on someone’s person, as John could have his $50 in a safe at home. In short, relating a predicate for “has” to anything else in the KB with rules could be a nearly impossible task.
Hence, “has” has various senses:
|John has a stomach ache.||(ailmentTypeAffects Stomachache John)|
|John has a shallow personality.||(personalityAttributes John ShallowPersonality Positive)|
|John has pride.||(feelsEmotion John Pride)|
|John has a son.||(children John JohnsSon)|
|John has blue eyes.||(eyeColor John BlueEyeColor)|
|John has $50.||(ownsWealth John (Dollar-US 50))|
These predicates, all representing some sense of “has”, permit us to make rules, draw inferences, and, we hope, make interesting conclusions about John.
Cyc constants with names ending in “-Underspecified” are exclusively used for parsing natural language to CycL. When the natural language parsing tools come across a word, for example “is”, that has many disparate meanings, the parser maps this term to an underspecified predicate. Postprocessing steps make an attempt to discover which CycL term best represents the meaning of the English term given the context, or ask the user (entering English) to attempt to clarify her meaning. Ontologists should never use an underspecified constant when making assertions, as these terms are absolutely useless in any kind of inference, and are often direct specifications of very broad terms, if they are specifications of any term at all.
Generics, identifed in the KB by the “-Generic” suffix, are the most broad terms possible and are also used primarily for parsing. These terms are loosely axiomatizable, and thus serve as the highest generalization for a particular concept. Ideally, assertions should be written not using these generic terms, but instead the most appropriate specialization of the generic term. Because it is possible to axiomatize these terms in a very loose way, Cyclists are encouraged to do so. Furthermore, they are quite useful for argument constraints on particularly broad predicates. In keeping with the ontology goals of finding the right level of generality or specificity (see “Finding the Right Level Of Generality, in the chapter called “From Constants to Assertions”), however, it is best to make assertions using the most specific specialization of the term. For instance, if, while John is hanging portraits on the wall, he uses his hammer, it is far preferable to assert (deviceUsed JohnsPortraitHanging JohnsHammer) over (instrument-Generic JohnsPortraitHanging JohnsHammer), although both are true.
In order for Cyc to reason about rules, Cyc requires backchaining steps. This involves trying to prove the consequent of a rule by checking to see if there are any bindings for the antecedent. Requiring additional rules, thereby adding backchaining steps to a query is quite expensive: it increases the search space and thus dramatically increases the amount of time it takes for the inference engine to return the results of the query. GAFs (Ground Atomic Formulae – formulae that do not contain any free variables) are much easier to reason about. Cyc can reason about them without using backchaining steps, but can instead rely on less backchaining overall, and can instead solve queries using lookup and other HL modules. A supported predicate, such as #$genlPreds, defines a relation in order to reduce the number of rules required to solve a query.
This is also handy because more reasoning can be done at the type level. There are relatively few well-defined individuals in the Knowledge Base: the KB contains by far more rules relating types of objects. This fulfills the goal of creating “common sense” reasoning. When confronting with instances, Cyc knows what to do with them. Of course, inputting instances into Cyc involves adding data, not common sense, the stuff for Cyc to use. But in the absence of instances, it is still good to be able to reason at the type level.
Rule macro predicates take common types of rules and make it possible to write GAFs that function in the same fashion in order to replace them. This is useful both for type level reasoning, and easing the work placed on the inference engine. The most common rule macro predicate is #$genls, which represents the subset relation. But #$genls is a really a rule macro for:
(implies (isa ?INST ?COLL) (isa ?INST ?SUPER-COLL))
But rule macros are used for many different kinds of common rules. For instance, it is quite true that:
(implies (brothers ?AGENT ?BROTHER) (siblings ?AGENT ?BROTHER))
Every brother of a person is also a sibling of that person, since sibling is a generalization of brother. If it were necessary to write rules for every relation of this sort, the strain on the inference engine would be unbearable. Fortunately, however, there is a rule macro predicate that replaces (a) with the GAF:
(genlPreds brothers siblings)
The two formulae are functionally equivalent: using either in an inference, for instance, would produce the same result. The latter assertion, (b) is supported by an HL module and requires no backchaining. Furthermore, it permits type level reasoning that the equivalent rule (a) does not. Imagine that:
(genlPreds siblings familyMembers)
Since genlPreds is a transitive relation, the inference engine can automatically conclude:
(genlPreds brothers familyMembers)
Consider the equivalent rules. This includes the above rule (a) and the additional rule:
(implies (siblings ?SIB ?FAM) (familyMembers ?SIB ?FAM))
To draw the conclusion that
(implies (brothers ?BRO ?FAM) (familyMembers ?BRO ?FAM))
The inference engine would require a backchain step for each rule. This is expensive, while the HL module which supported the rule macro predicate #$genlPreds is relatively cheap.
The Cyc KB is populated with a host of useful rule macro predicates. Among the more useful are those that replace rules of this type:
(implies (isa ?VERT Vertebrate) (thereExists ?HEAD (and (isa ?HEAD Head-Vertebrate) (anatomicalParts ?VERT ?HEAD))))
This rule states that for every vertebrate, there is a vertebrate head that is an anatomical part of that vertebrate. These are just the types of rules that are extraordinarily useful throughout the KB. This can be handled by another sort of rule macro predicate. This rule collapses into this GAF:
(relationAllExists anatomicalParts Vertebrate Head-Vertebrate)
The above rule (f) is equivalent with the GAF (g). The comment on the rule macro predicate #$relationAllExists reads:
"(relationAllExists SLOT COL1 COL2) means that, for every instance of COL1 (INS1), there is some instance of COL2 (INS2) such that (SLOT INS1 INS2) holds. relationAllExists is thus redundant with a huge set of commonly-occurring rules. By having this predicate (along with an axiom defining it, and, eventually, coded support for quick inferencing with it), those rules can be stated more tersely and reasoning at the collection level is possible."
Likewise, a similar GAF, #$relationAllInstance should replace all rules of the form “Every x bears the relation R to a, where a is an individual instance. As the comment reads:
"(relationAllInstance PRED COL1 INS2) means that, for every instance of COL1 (INS1) there is a relation PRED with INS2, i.e. (PRED INS1 INS2) holds. It allows assertions that correspond more directly to common database entries e.g. 'the maximum speed of a golf cart is 15 mph' to be represented by a single GAF (relationAllInstance maxSpeed GolfCart (MilesPerHour 15)) rather than by a rule (implies (isa ?CART GolfCart) (maxSpeed ?CART (MilesPerHour 15)). relationAllInstance is thus redundant with a huge set of commonly-occurring rules. One must be aware of the implicit quantification underlying this predicate. "
(relationAllInstance tasteOfObject Absinthe BitterTaste)
should be read: “All absinthe has a bitter taste”, and is equivalent to the rule:
(implies (isa ?ABS Absinthe) (tasteOfObject ?ABS BitterTaste))
There are a suite of rule macro predicates that behave in this fashion. Be sure to read the comments on relationInstanceAll, relationAllAll, relationExistsExists, and relationExistsAll.
Using rule macro predicates such as these increase Cyc’s capacity for type level reasoning and dramatically cuts the load on the inference engine, resulting in shorter return times for inferences.
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When writing rules, it is important that the rules are clear and easy to read. Cyc is quite dynamic: rules that are asserted today might change, depending on changing vocabulary, a year from now. It is very important that people making assertions are able to read the assertions previously made. Also, no one’s rule writing talent is perfect and no one’s reviews are perfectly thorough. Finally, as rules are added to a constant, a constant is thus defined and gives clues to usage that may not be apparent from the constant’s comment. Thus, rules should be easy to read.
Those coming to Cyc from a logic background will be used to writing formulae of this sort:
y [ (Fx & Gy)
As a result, when they start to write rules in CycL, they often use the same difficult to read variables.
(#$implies (#$and (#$startsAfterEndingOf ?X ?Y) (#$temporallySubsumes ?X ?Z)) (#$startsAfterEndingOf ?Y ?Z))
The assertion above has an error, and it is difficult to spot it because the variable names don’t give us any clues to the intended meaning of the rule. This is the same rule written with clearer variable names:
(#$implies (#$and (#$startsAfterEndingOf ?LATE ?EARLY) (#$temporallySubsumes ?LATE ?LATE-SUB)) (#$startsAfterEndingOf ?EARLY ?LATE-SUB))
Suddenly, the error becomes obvious. Variables are most helpful when they are English names or abbreviations. Instead of?X,?Y,?X1,?XX, etc., use?SUBEVENT,?EVENT,?AGENT,?DOG,?MOON, etc.
A word of caution, however. Just because a speaker of English can read the word that is being used as a variable, this does not mean that Cyc can. Until a variable is constrained, it remains universally quantified. For instance,
(#$implies (#$isa ?HORSE #$Horse) (#$thereExists ?HEAD (#$anatomicalParts ?HORSE ?HEAD)))
While it is clear that the Cyclist was trying to say that “Every horse has a head as an anatomical part”, what the Cyclist accidentally entered is that “For every horse, there is something that is an anatomical part of that horse”. He should have instead asserted:
(#$implies (#$isa ?HORSE #$Horse) (#$thereExists ?HEAD (#$and (#$isa ?HEAD #$Head-AnimalBodyPart) (#$anatomicalParts ?HORSE ?HEAD))))
Of course, this is an undesirable assertion for several reasons. Please see “Finding the Right Level Of Generality”, “Reusibility”, and “Avoiding Existential Quantification”.
This section is not yet available.
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