If I extract certain association rules from a sample itemset consisting of let's say:
a, b -> c
c, d -> e
a, c -> d
b, c -> c
Is there a way to combine the found rules into one formula depending on a fixed item count number were all rules are aggregated to get the most likely combination of all association rules combined?
Let's say the fixed item number is four and the above association rules have to be mixed to get the most likely combination. How would I do that? Are there algorithms or programmes for this?
Each association rule has a confidence and support.
For example A --> BC support : 50 % confidence : 50%.
If you combine several association rules, then how you would calculate the support and confidence of the resulting rule ? That would be a problem.
Actually, you could look at CBA: Classification by Associations. This project use association rules to perform classification. Instead of trying to combine association rules, it uses some heuristics to select the rule that is the most appropriate for classifying a new instance. To choose the best rule, it considers the support, the confidence and the size of the left par of the rule. There is other similar works.
But I have not seen any work trying to combine association rules... maybe if you search "association rule clustering" in Google you could find something related to your idea.
By the way, besides confidence and support, some people use other interestingness measures like the lift, J-Measure, etc.
Related
We are using GraphDB 8.4.0 as a triple store for a large data integration project. We want to make use of the reasoning capabilities, and are trying to decide between using HORST (pD*) and OWL2-RL.
However, the literature describing these is quite dense. I think I understand that OWL2-RL is more "powerful", but I am unsure how so. Can anyone provide some examples of the differences between the two reasoners? What kinds of statements are inferred by OWL2-RL, but not HORST (and vice versa)?
Brill, inside there GraphDB there is a single rule engine, which supports different reasoning profiles, depending on the rule-set which was selected. The predefined rule-sets are part of the distribution - look at the PIE files in folder configs/rules. One can also take one of the existing profiles and tailor it to her needs (e.g. remove a rule, which is not necessary).
The fundamental difference between OWL2 RL and what we call OWL-Horst (pD*) is that OWL2RL pushes the limits of which OWL constructs can be supported using this type of entailment rules. OWL Horst is limited to RDFS (subClassOf, subSpropertyOf, domain and range) plus what was popular in the past as OWL Lite: sameAs, equivalentClass, equivalentProperty, SymmetricProperty, TransitiveProperty, inverseOf, FunctionalProperty, InverseFunctionalProperty. There is also partial support for: intersectionOf, someValuesFrom, hasValue, allValuesFrom.
What OWL 2 RL adds on top is support for AsymmetricProperty, IrreflexiveProperty, propertyChainAxiom, AllDisjointProperties, hasKey, unionOf, complementOf, oneOf, differentFrom, AllDisjointClasses and all the property cardinality primitives. It also adds more complete support for intersectionOf, someValuesFrom, hasValue, allValuesFrom. Be aware that there are limitations to the inference supported by OWL 2 RL for some of these properties, e.g. what type of inferences should or should not be done for specific class expressions (OWL restrictions). If you chose OWL 2 RL, check Tables 5-8 in the spec, https://www.w3.org/TR/owl2-profiles/#OWL_2_RL. GraphDB's owl-2-rl data set is fully compliant with it. GraphDB is the only major triplestore with full OWL 2 RL compliance - see the this table (https://www.w3.org/2001/sw/wiki/OWL/Implementations) it appears with its former name OWLIM.
My suggestion would be to go with OWL Horst for a large dataset, as reasoning with OWL 2 RL could be much slower. It depends on your ontology and data patterns, but as a rule of thumb you can expect loading/updates to be 2 times slower with OWL 2 RL, even if you don't use extensively its "expensive" primitives (e.g. property chains). See the difference between loading speeds with RDFS+ and OWL 2 RL benchmarked here: http://graphdb.ontotext.com/documentation/standard/benchmark.html
Finally, I would recommend you to use the "optimized" versions of the pre-defined rule-sets. These versions exclude some RDFS reasoning rules, which are not useful for most of the applications, but add substantial reasoning overheads, e.g. the one that infers that the subject, the predicate and the object of a statement are instances of rdfs:Resource
Id: rdf1_rdfs4a_4b
x a y
-------------------------------
x <rdf:type> <rdfs:Resource>
a <rdf:type> <rdfs:Resource>
y <rdf:type> <rdfs:Resource>
If you want to stay 100% compliant with the W3C spec, you should stay with the non-optimized versions.
If you need further assistance, please, write to support#ontotext.com
In addition to what Atanas (our CEO) wrote and your excellent example, http://graphdb.ontotext.com/documentation/enterprise/rules-optimisations.html provides some ideas how to optimize your rulesets to make them faster.
Two of the ideas are:
ptop:transitiveOver is faster than owl:TransitiveProperty: quadratic vs cubic complexity over the length of transitive chains
ptop:PropChain (a 2-place chain) is faster than general owl:propertyChainAxiom (n-place chain) because it doesn't need to unroll the rdf:List underlying the representation of owl:propertyChainAxiom.
Under some conditions you can translate the standard OWL constructs to these custom constructs, to have both standards compliance and faster speed:
use rule TransitiveUsingStep; if every TransitiveProperty p (eg skos:broaderTransitive) is defined over a step property s (eg skos:broader) and you don't insert p directly
if you use only 2-step owl:propertyChainAxiom then translate them to custom using the following rule, and infer using rule ptop_PropChain:
Id: ptop_PropChain_from_propertyChainAxiom
q <owl:propertyChainAxiom> l1
l1 <rdf:first> p1
l1 <rdf:rest> l2
l2 <rdf:first> p2
l2 <rdf:rest> <rdf:nil>
----------------------
t <ptop:premise1> p1
t <ptop:premise2> p2
t <ptop:conclusion> q
t <rdf:type> <ptop:PropChain>
http://rawgit2.com/VladimirAlexiev/my/master/pubs/extending-owl2/index.html describes further ideas for extended property constructs, and has illustrations.
Let us know if we can help further.
After thinking this for bit, I came up with a concrete example. The Oral Health and Disease Ontology (http://purl.obolibrary.org/obo/ohd.owl) contains three interrelated entities:
a restored tooth
a restored tooth surface that is part of the restored tooth
a tooth restoration procedure that creates the restored tooth surface (e.g., when you have a filling placed in your tooth)
The axioms that define these entities are (using pseudo Manchester syntax):
restored tooth equivalent to Tooth and has part some dental restoration material
restored tooth surface subclass of part of restored tooth
filling procedure has specified output some restored tooth surface
The has specified output relation is a subproperty of the has participant relation, and the has participant relation contains the property chain:
has_specified_input o 'is part of'
The reason for this property chain is for reasoner to infer that if a tooth surface participates in a procedure, then the whole tooth that the surface is part of participates in the procedure, and, furthermore, the patient that the tooth is part of also participates in the procedure (due to the transitivity of part of).
As a concrete example, let define some individuals (using pseudo rdf):
:patient#1 a :patient .
:tooth#1 a :tooth; :part-of :patient#1
:restored-occlusal#1 a :restored-occlusal-surface; :part-of tooth#1 .
:procedure#1 :has-specified-output :restored-occlusal#1 .
Suppose you want to query for all restored teeth:
select ?tooth where {?tooth a :restored-tooth}
RDFS-Plus reasoning will not find any restored teeth b/c it doesn't reason over equivalent classes. But, OWL-Horst and OWL2-RL will find such teeth.
Now, suppose you want to find all patients that underwent (i.e. participated in) a tooth restoration procedure:
select ?patient where {
?patient a patient: .
?proc a tooth_restoration_procedure:; has_participant: ?patient .
}
Again, RDFS-Plus will not find any patients, and neither will OWL-Horst b/c this inference requires reasoning over the has participant property chain. OWL2-RL is needed in order to make this inference.
I hope this example is helpful for the interested reader :).
I welcome comments to improve the example. Please keep any comments within the scope of trying to make the example clearer. Its purpose is to give insight into what these different levels of reasoning do and not to give advice about which reasoner is most appropriate.
I have a background in frame-based ontologies, in which classes represent concepts and there's no restriction against assertion of class:class relationships.
I am now working with an OWL2 ontology and trying to determine the best/recommended way to represent "canonical part-of" relationships - conceptually, these are relationships that are true, by definition, of the things represented by each class (i.e., all instances). The part-of relationship is transitive, and I want to take advantage of that so that I'd be able to query the ontology for "all parts of (a canonical) X".
For example, I might want to represent:
"engine" is a part of "car", and
"piston" is a part of "engine"
and then query transitively, using SPARQL, for parts of cars, getting back both engine and piston. Note that I want to be able to represent individual cars later (and be able to deduce their parts by reference to their rdf:type), and of course I want to be able to represent sub-classes of cars as well, so I can't model the above-described classes as individuals - they must be classes.
It seems that I have 3 options using OWL, none ideal. Is one of these recommended (or are any discouraged), and am I missing any?
OWL restriction axioms:
rdfs:subClassOf(engine, someValuesFrom(partOf, car))
rdfs:subClassOf(piston, someValuesFrom(partOf, engine))
The major drawback of the above is that there's no way in SPARQL to query transitively over the partOf relationship, since it's embedded in an OWL restriction. I would need some kind of generalized recursion feature in SPARQL - or I would need the following rule, which is not part of any standard OWL profile as far as I can tell:
antecedent (body):
subClassOf(B, (P some A) ^
subClassOf(C, (P some B) ^
transitiveProperty(P)
consequent (head):
subClassOf(C, (P some A))
OWL2 punning: I could effectively represent the partOf relationships on canonical instances of the classes, by asserting the object-property directly on the classes. I'm not sure that that'd work transparently with SPARQL though, since the partOf relationships would be asserted on instances (via punning) and any subClassOf relationships would be asserted on classes. So if I had, for example, a subclass six_cylinder_engine, the following SPARQL snippet would not bind six_cylinder_engine:
?part (rdfs:subClassOf*/partOf*)+ car
Annotation property: I could assert partOf as an annotation property on the classes, with values that are also classes. I think that would work (minus transitivity, but I could recover that easily enough with SPARQL paths as in the query above), but it seems like an abuse of the intended use of annotation properties.
I think you have performed a good analysis of the problem and the advantages/disadvantages of different approaches. I don't know if any one is discouraged or encouraged. IMHO this problem has not received sufficient attention, and is a bigger problem in some domains than others (I work in bio-ontologies which frequently use partonomies, and hence this is very important).
For 1, your rule is valid and justified by OWL semantics. There are other ways to implement this using OWL reasoners, as well as RDF-level reasoners. For example, using the ROBOT command line wrapper to the OWLAPI, you can run the reason command using an Expression Materializing Reasoner. E.g
robot reason --exclude-tautologies true --include-indirect true -r emr -i engine.owl -o engine-reasoned.owl
This will give you an axiom piston subClassOf partOf some car that can be queried using a non-transitive SPARQL query.
The --exclude-tautologies removes inferences to owl:Thing, and --include-indirect will include transitive inferences.
For your option 2, you have to be careful in that you may introduce incorrect inferences. For example, assume there are some engines without pistons, i.e. engine SubClassOf inverse(part_of) some piston does not hold. However, in your punned shadow world, this would be entailed. This may or may not be a problem depending on your use case.
A variant of your 2 is to introduce different mapping rules for layering OWL T-Tboxes onto RDF, such as described in my OWLStar proposal. With this proposal, existentials would be mapped to direct triples, but there is another mechanism (e.g. reification) to indicate the intended quantification. This allows writing rules that are both safe (no undesired inferences) and complete (for anything expressible in OWL-RL). Here there is no punning (under the alternative RDF to OWL interpretation). You can also use the exact same transitive SPARQL query you wrote to get the desired results.
I am going to implement a personal recommendation system using Apriori algorithm.
I know there are three useful concepts as 'support',confidence' and 'lift. I already know the meaning of them. Also I know how to find the frequent item sets using support concept. But I wonder why confidence and lift concepts are there for if we can find frequent item sets using support rule?
could you explain me why 'confidence' and 'lift' concepts are there when 'support' concept is already applied and how can I proceed with 'confidence' and 'lift' concepts if I have already used support concept for the data set?
I would be highly obliged if you could answer with SQL queries since I am still an undergraduate. Thanks a lot
Support alone yields many redundant rules.
e.g.
A -> B
A, C -> B
A, D -> B
A, E -> B
...
The purpose of lift and similar measures is to remove complex rules that are not much better than the simple rule.
In above case, the simple rule A -> B may have less confidence than the complex rules, but much more support. The other rules may be just coincidence of this strong pattern, with a marginally stronger confidence because of the smaller sample size.
Similarly, if you have:
A -> B confidence: 90%
C -> D confidence: 90%
A, C -> B, D confidence: 80%
then the last rule is even bad, despite the high confidence!
The first two rules yield the same outcome, but with higher confidence. So that last rule shouldn't be 80% correct, but -10% correct if you assume the first two rules to hold!
Thus, support and confidence are not enough to consider.
I was learning database normalization and join dependencies and
5NF. I had a hard time. Can anyone give me some practical examples of the multivalue dependency rule:
MVD3: (transitivity) If X ↠ Y and Y ↠ Z, then X ↠ (Z − Y).
Functional dependency / Normalization theory and the normal forms up to and including BCNF, were developed on the hypothesis of all data attributes (columns/types/...) being "atomic" in a certain sense. That "certain sense" has long been deprecated by now, but essentially it boiled down to the notion that "a single cell value in a table could not itself hold a multiplicity of values". Think, a textual CSV list of ISBN numbers, a table appearing as a value in a cell in a table (truly nested tables), ...
Now imagine an example with courses, professors, and study books used as course material. Imagine all of that modeled in a single 3-column table which says that "Professor (P) teaches course (C) and uses book (B) as course material." If there can be more than one book (B) used for any given course (Cn) and there can be more than one course (C) taught by any given professor (Pn) and there can be more than one professor (P) teaching any given course (Cn), then this table is clearly all-key (key is the full set of attributes {P,C,B} ).
This means that this table satisfies BCNF.
But now imagine that there is a rule to the effect that "the set of books used for any given course (Cn) must be the same, regardless of which professor teaches it.".
In the days when normalization was developed to the form in which it is now commonly known, it was not allowed to have table columns (relation attributes) that were themselves tables (relations). (Because such a design was considered a violation of 1NF, a notion which is now considered suspect.)
Imagine for a moment that we are indeed allowed to model relation attributes to be of type relation. Then we could model our 3-column table (/relation) as follows : "Professor (P) teaches course (C) and uses THE SET OF BOOKS (SB) as course material.". Attribute SB would no longer be an ISBN number, as in the previous and more obvious design, but it would be a (probably unary) RELATION holding the entire set of ISBN numbers. If we draw our design like that, and we then consider our rule that "all professors use the same set of books for the same course", then we see that this rule is now expressible as an FD from (C) to (SB) !!! And this means that we have a violation of a lower NF on our hand !!!
4 and 5 NF have arisen out of this kind of problems (where the appearance of a single attribute value -courseID (C)- causes a requirement for the appearance of A MULTITUDE of rows (multiple (B) ISBn numbers) being recognised quite early on, but without the solution that is currently regarded as the best (RVA's), being recognised as a valid one. So 4 and 5 NF were created "new and further normal forms", where the then-existing definitions of 2, 3 and BC NF were already sufficient for dealing with the situation at hand, provided RVA's had been recognised as a valid design approach.
To support that claim, let's look at what whould be done to eliminate the NF violation in our {P,C,SB} design with the FD C->SB :
We would split the table into two separate tables {P,C} and {C,SB} with keys {P,C} and {C}, repsectively. Both tables satisfy BCNF.
But we still have this SB attribute that holds a set of ISBN numbers. Dealing with this can be done by applying a technique like "UNGROUPING". Applying this to our {C,SB} table would get us a {C,B} table, where B are the ISBN book numbers (or whatever identifier you like to use in your database), and the key to the table is {C,B}. This is exactly the same design we would get if we eliminated the 4/5 NF violation !!!
You might also want to take a look at Multivalue Dependency violation?
I am reading a lot about logic programming - ASP (Answer Set Programming) is one example or this. They (logic programs) are usually in the form:
[Program 1]
Rule1: a <- a1, a2, ..., not am, am+1;
Rule2: ...
This set of rules is called the logic program and the s.c. model is the result of such computation - some kind of assignment of True/False values to each of a1, a2, ...
There is lot of research going on - e.g. how such kind of programs (rules) can be integrated with the (semantic web) ontologies to build knowledge bases that contain both - rules and ontologies (some kind of constraints/behaviour and data); there is lot of research about ASP itself - like parallel extensions, extensions for probabilistic logic, for temporal logic and so on.
My question is - is there some kind of research and maybe some proof-of-concept projects where this analysis is extended from Boolean variables to variables with integer and maybe even float domains? Currently I have not found any research that could address the following programs:
[Program 2]
Rule1 a1:=5 <- a2=5, a3=7, a4<8, ...
Rule2 ...
...
[the final assignment of values to a1, a2, etc., is the solution of this program]
Currently - as I understand - if one could like to perform some kind of analysis on Program-2 (e.g. to find if this program is correct in some sense - e.g. if it satisfies some properties, if it terminates, what domains are allowed not to violate some kind of properties and so on), then he or she must restate Program-2 in terms of Program-1 and then proceed in way which seems to be completely unexplored - to my knowledge (and I don't believe that is it unexplored, simply - I don't know some sources or trend). There is constraint logic programming that allow the use of statements with inequalities in Program-1, but it is too focused on Boolean variables as well. Actually - Programm-2 is of kind that can be fairly common in business rules systems, that was the cause of my interest in logic programming.
SO - my question has some history - my practical experience has led me to appreciate business rules systems/engines, especially - JBoss project Drools and it was my intention to do some kind of research of theory underlying s.c. production rules systems (I was and I am planning to do my thesis about them - if I would spot what can be done here), but I can say that there is little to do - after going over the literature (e.g. http://www.computer.org/csdl/trans/tk/2010/11/index.html was excellent IEEE TKDE special issues with some articles about them, one of them was writter by Drools leader) one can see that there is some kind of technical improvements of the decades old Rete algorithm but there is no theory of Drools or other production rule systems that could help with to do some formal analysis about them. So - the other question is - is there theory of production rule systems (for rule engines like Drools, Jess, CLIPS and so on) and is there practical need for such theory and what are the practical issues of using Drools and other systems that can be addressed by the theory of production rule systems.
p.s. I know - all these are questions that should be directed to thesis advisor, but my current position is that there is no (up to my knowledge) person in department where I am enrolled with who could fit to answer them, so - I am reading journals and also conference proceedings (there are nice conference series series of Lecture Notes in Computer Science - RuleML and RR)...
Thanks for any hint in advance!
In a sense the boolean systems already do what you suggest.
to ensure A=5 is part of your solution, consider the rules (I forget my ASP syntax so bear with me)
integer 1..100 //integers 1 to 100 exist
1{A(X) : integer(X)}1 //there is one A(X) that is true, where X is an integer
A(5) //A(5) is true
and I think your clause would require:
integer 1..100 //integers 1 to 100 exist
1{A(X) : integer(X)}1 //A1 can take only one value and must take a value
1{B(X) : integer(X)}1 //A2 ``
1{C(X) : integer(X)}1 //A3 ``
1{D(X) : integer(X)}1 //A4 ``
A(5) :- B(5), C(7), D(8) //A2=5, A3=7, A4=8 ==> A1=5
I hope I've understood the question correctly.
Recent versions of Clojure core.logic (since 0.8) include exactly this kind of support, based on cKanren
See an example here: https://gist.github.com/4229449