Abstract
Knowledge space theory (KST) is a mathematical framework for the assessment of knowledge and learning in education. An important task of KST is to achieve all of the atoms. With the development of KST, considering its relationship with formal concept analysis (FCA) has become a hot issue. The atoms of the knowledge space with application in knowledge reduction based on FCA is examined in this paper. The knowledge space and its properties based on FCA are first discussed. Knowledge reduction and its relationship with molecules in the knowledge context are then investigated. A Boolean matrix is employed to determine molecules and meet-irreducible elements in the knowledge context. The method of the knowledge-reduction-based Boolean matrix in the knowledge space is also explored. Furthermore, an algorithm for finding the atoms of the knowledge space in the knowledge context is developed using a Boolean matrix.
1. Introduction
Formal concept analysis (FCA), as a supplement of a rough set, is a mathematical method of data analysis with applications in various areas [,,,,,,,,,,,,,]. In particular, it provides a theoretical framework for the discovery and design of concept hierarchies from relational information systems. Concept hierarchies are built on a binary relation between the sets of objects and attributes in a given formal context. FCA derives from the fact that it provides an easy-to-understand diagram rooted in data, the so-called concept lattice. Moreover, the concept lattice is the collection of all formal concepts, which consists of extents and intents that are determined by each other. In general, the research of formal contexts has mainly focused on two aspects: one is the construction of the concept lattice; the other is knowledge reduction. For the first aspect, Berry [] presented an approach to generate concepts by discussing the relationship between concept lattices and the underlying graphs. After that, Kuznetsov [] thoroughly compared and summarized several well-known algorithms. On the other hand, the second aspect is knowledge reduction, which includes two parts: concept reduction, which reduces the size of the concept lattices [,,,,], and attribute reduction, as well as object reduction, which preserve the hierarchical structure of concept lattices [,,,,,,,]. Several important investigations arose at these points. Kumar [] proposed a method based on fuzzy K-means clustering for reducing the size of the concept lattices. Reference [] derived the mean value of cardinality of the reduced hierarchical-structure-based graph-theoretical point of view on FCA together with simple probabilistic arguments. To reduce redundant information, Wu et al. [] illustrated a method of granular reduction based on a discernibility matrix in a formal context. Kumar [] put forward a non-negative matrix factorization to address the knowledge reduction. Li et al. [,,] formulated heuristic knowledge reduction approaches for finding a minimum granular reduct in decision formal contexts.
Knowledge space theory (KST) [,,,], proposed by Doignon and Falmagne provides a valuable mathematical framework for computerized web-based systems for the assessment of knowledge and learning in education. The knowledge state, the key notion of KST, is represented by the subset K of items (or problems) in the finite domain of knowledge Q that an individual is capable of solving correctly, barring careless errors and lucky guesses. A pair represents a knowledge structure by convention, where is the collection of all the knowledge states called the knowledge structure always containing at least the following special constituent parts: the empty state, i.e., corresponding to a student knowing nothing about the subject; Q, which is the state of a student knowing everything about the subject. Subsequently, a knowledge space is closed under union when any two states K and L are given in the space, then union is in . In essence, at some point, the knowledge of students arising from the union of their initial knowledge states is plausible if they have different knowledge states that are involved in an extensive interaction. Therefore, it is unnecessary to reserve any union of states in a description of the knowledge space.
With the growth of KST, the research about its relationship with other approaches has become a research topic. KST has the same mathematical background as FCA. Both of them aim to order two sets of elements simultaneously. There is an intimate relationship between FCA and KST []. The relationship between the attribute implications and the entailed relations was considered by Falmagne et al. []. References [,,] built a knowledge space by querying an expert to interpret an entailed relation. They showed that the implication systems are in essence closure systems. This is not just in FCA, but also applies in KST via taking set-theoretic complements.
At present, our study focuses on the framework of FCA. In this paper, we use the FCA for the knowledge reduction in the knowledge space. However, FCA could lead to potentially high combinatorial complexity, and the structure obtained, even from small dataset, may become prohibitively large []. To overcome this limitation, we applied a Boolean matrix to construct the intents (extents) of molecules (meet-irreducible elements) in knowledge contexts. This model avoids making the concept lattice from the knowledge context and aims to maintain both the object relation and attribute relation simultaneously. We first introduce the relationships between concepts in the knowledge context from the viewpoint of molecule lattice. A novel method based on a Boolean matrix is further proposed for finding the knowledge reduction of a knowledge space.
The remainder of this paper is organized as follows. In Section 1, we briefly review some basic notations of FCA and KST and their relationships. In Section 2, we investigate the relationships between the molecule lattice and the concept lattice from the knowledge context. Then, the judgment theorems of the molecule, as well as meet-irreducible elements of concept lattices are proposed. In Section 3, we conclude that each member in the concept lattice from the knowledge context is the union (intersection) of some molecules (meet-irreducible elements), present a simple way to compute the molecules and meet-irreducible elements in a knowledge context using a Boolean matrix, and discuss an algorithm for the knowledge reduction of a knowledge space in detail. The final summary and further research are drawn in Section 4.
2. Preliminaries
2.1. Formal Concept Analysis Theory
Partial order ≤ is a relation on a set X with reflexivity, anti-symmetry, and transitivity. Then, the set X satisfying the partial order is called the partially ordered set (for short, a poset). For a subset Y of a poset X, we define the lower set and the upper set as follows, respectively:
when Y is a singleton set ; we denote the lower and upper sets for short. Furthermore, a subset Y of X is a lower set if . Analogously, Y is an upper set if . One can see [] for the details.
A subset Z of a partially ordered set is called a chain if any two members of Z are comparable. Generally, alternative names for a chain are a linearly ordered set and a totally ordered set. Therefore, if Z is a chain and , then either or (see []).
An element is named maximal if there exists such that , that is there is no element in X following a except a. Similarly, an element is called minimal if whenever then , that is there is no element in X preceding a except a itself.
Lemma 1
(Kuratowski). Let X be a poset. Then, each chain in X is contained by a maximal chain.
A formal context is a triple , where and are two nonempty finite sets of objects and attributes, respectively. R is a binary relation between U and A, where means that the object g possesses attribute a.
In fact, the representation of the binary relation contains values 1 and 0, where 1 means the row object possesses the column attribute. In this paper, we suppose that the binary relation R is , which holds the following forms: for any :
- There exist with and ;
- There are satisfying and .
For and , one defines the following two operators [,]:
- ;
- .
is the maximal set of attributes that possesses all objects in G, and is the maximal set of objects shared by B. If a pair satisfies and , we say that the pair is a formal concept, in which G is called the extent and B is called the intent. In addition, for any , is denoted as for short. Similarly, for any , denote for convenience instead of .
It is easy to observe that and . With the hypothesis of , we have and ; and .
The collection of all formal concepts of F is denoted by , in which the corresponding partial order relation ≤ is given as follows: for any ,
then is a sub-concept of and the relation ≤ is called the hierarchical order of concepts. Since is closed under union and intersection [], that is, for any ,
then forms a complete lattice denoted as the concept lattice.
A closure system is a collection of subsets that is closed under intersection and contains ∅, U. Caspard and Davey [,] introduced a closure system induced by the concept lattice. Denote:
Then, and in fact form the closure systems on U and A with respect to , respectively.
2.2. Knowledge Space Theory
In this subsection, we recall several notions of KST; for more details, refer [,].
For a partial knowledge structure , Q is a nonempty finite set of items or problems, which is called the domain of the knowledge structure, and is family of subsets of Q containing at least Q. The subsets of are knowledge states. If , then the partial knowledge structure is denoted as a knowledge structure. When is closed under union, is called a knowledge space; equivalently, is a knowledge space on Q. The dual of on Q is the knowledge structure containing all the complements of the states of , i.e., . The minimal subfamily of a knowledge space spanning the original knowledge space is called a base. It should be pointed out that each finite knowledge space has a base.
Furthermore, the states of the base have an important property. Assume that is a nonempty family of sets. Each atom at is a minimal set in containing q. A set is an atom if it is an atom at q for some . We can conclude that a base of a knowledge space is formed by the collection of all atoms.
There is an intimate relation between FCA and KST []. The core connection between FCA and KST is the property that both the collection of extents, as well as the collection of intents of the concepts yield the closure systems. Consider the following formal context:
Definition 1
([]). Let be a formal context where U and Q are individuals and the knowledge domain. For any and , , if individual x is not capable of solving problem q, then is defined as a knowledge context.
Example 1
([]). Let be a set of individuals and be knowledge domains. Then, the solution behavior is characterized by the relation R in the formal context defined in the following table.
The collection of intents derived from the knowledge context of Table 1 is:
Table 1.
Formal context of individuals to .
It is easy to determine that is closed under intersection forming a closure system on Q with respect to a knowledge context. Now, taking the set-theoretic complements of all the intents, we obtain a family of Q, which contains ∅ and Q and is closed under union, then it forms a knowledge space:
It is easily seen that a base of knowledge space is , the collection of the subset of solved items for all individuals.
3. Molecular Lattice
Lemma 2.
Let be a concept lattice of a formal context . For any , then
- 1.
- .
- 2.
- .
Proof.
We only present the course of proof for . For any , we have and , then and . Similarly, and . As we know, and for , then we have .
and . Thus . □
By the above the lemma, the complete lattice satisfying the distribution laws is called a completely distributive lattice. Note that for any , if , there exists with , then we call the refinement of , denoted as . If , there exists such that , then we say is thinner than , denoted by .
Definition 2.
Let be a formal context, and for , . If:
- 1.
- , i.e., ,
- 2.
- and for with such that ,
where “inf” means the infimum. Then, is referred to as a maximal set of .
The minimal sets of can be defined under antithesis.
Definition 3.
Let be a formal context and for , . If:
- 1.
- , i.e., ,
- 2.
- and for with such that ,
where “sup” means the supremum, then is called a minimal set of .
It is enough to show that and are the minimal set and maximal set in of ∅, respectively, since is a complete concept, that is and . Actually, the union of some maximal sets of is also a maximal set of . Then, the biggest maximal set will inevitably exist, and we denote it as . Similarly, the union of some minimal sets of remains a minimal set of since is complete. Thus, there exists the biggest minimal set of denoted by . Next, we aim to conduct an investigation on the relationship between and its biggest maximal set.
By virtue of the complete distributive lattice , is in correspondence with and uniquely exists, which can be seen as a image of with a mapping from to .
Definition 4.
Let be a formal context. For any , suppose corresponds to , then an obtained mapping is defined as the maximal mapping on .
Theorem 1.
Suppose is a formal context. Then:
- 1.
- For any , is an upper set, that is , there is with .
- 2.
- is an intersection–union mapping, that is suppose , then .
Proof.
(1) Suppose is the upper set of , then there exists such that . Let . Evidently, . For with , then by virtue of the maximal set . In addition, meets that . It is easy to see that . Then, is a maximal set of . On account of being the biggest maximal set of , it only has , for which . Then, .
(2) Suppose and . We only have to prove that is the biggest maximal set of . In reality,
.
On the other hand, due to with and for any , we have . This implies there exists such that for any . Then, , which yields that is a maximal set of . Furthermore, for any maximal set of and , by the definition of the maximal set, we conclude that , i.e., for any , there exists satisfying . Assume , then by the condition of , which indicates . Thus, is the biggest maximal set of , that is . □
Definition 5.
Let be a formal context. For any , suppose corresponds to , then an obtained mapping is referred to as the minimal mapping on .
Theorem 2.
Suppose is a formal context. Then:
- 1.
- For any , is a lower set, that is, , there is with .
- 2.
- is a union-preserved mapping, that is, suppose , then .
Proof.
This is similar to the proof of Theorem 1. □
Lemma 3.
Let be a formal context and with . Then, there exists satisfying and .
Proof.
Notice that and is an intersection–union mapping, so then
Thus, there is such that and . □
Corollary 1.
Let be a formal context and with . Then, there are following that:
- 1.
- and , .
- 2.
- , .
Proof.
This follows immediately by applying Lemma 3. □
If , then we can discover such that .
Definition 6.
Let be a formal context. For any , the following properties hold:
- 1.
- and , such that and ;
- 2.
- , that is, , such that .
Then, we call an ideal in .
Theorem 3.
Let be a formal context and with . Then, there exists an ideal in satisfying the following properties:
- 1.
- ;
- 2.
- For any , there exists a minimal element in such that .
Proof.
There exist satisfying the properties of the above corollary. Let . Then, is a lower set, obviously. Second, for any , there are with and . Owing to for , it has either or . These yield that is an ideal in and . The following certify .
Suppose , then is a chain. By Kuratowski, there exists a maximal chain containing . Assume . Next, we prove . Actually, if , then there is a natural number such that . , i.e., as a result. However, , and in light of the definition of the maximal set, there exists with , which indicates , in conflict with . □
Definition 7.
Let be a formal context. For :
- 1.
- is called a complement-prime element if implies or .
- 2.
- is defined a join-irreducible element if implies or .
- 3.
- is seen as a prime element if implies or .
- 4.
- is called a meet-irreducible element if implies or .
Lemma 4.
is a prime element if and only if is a complement-prime element, where is called the complement of and is defined as follows:
Theorem 4.
Let be a formal context. Then:
- 1.
- is a complement-prime element if and only if is a join-irreducible element;
- 2.
- is a prime element if and only if is a meet-irreducible element.
Proof.
We only prove the first. Note that is a join-irreducible element and with . By distributive laws, , then or . These yield that or . Conversely, if is a complement-prime element and , then . Thus, or . However, , we obtain and . Then, or , that is is a join-irreducible element. □
Normally, a join-irreducible element is not distinguished from a complement-prime element in this paper. Obviously, is a join-irreducible element. Every join-irreducible element is called a molecule except . Therefore, if is a molecule, then is a join-irreducible element.
Theorem 5.
Let be a formal context. For any , is a molecule if and only if, whenever , then there exists such that and .
Proof.
Suppose , for any , and let
then and . Then, or since is a molecule. Actually, . Otherwise, , and in light of , the minimal set of and , we have such that , a contradiction. Hence, . In addition, according to , there exists meeting , which implies , , and .
Conversely, suppose ; there exists such that and . If is not a molecule, then we have with , , and , that is and . Therefore, there are satisfying and . According to the hypothesis, we have with and . On the other hand, on the basis of and the definition of , we can conclude that and , which is a contradiction. Therefore, is a molecule in . □
Corollary 2.
If is a molecule, then there exists such that .
Proof.
This is trivial based on Theorem 5. □
Corollary 3.
For any , if there exists with neither nor yielding and , then is not a molecule.
Proof.
This follows immediately from Theorem 5. □
Theorem 6.
Let be a formal context. For any , is a meet-irreducible element if and only if, whenever , then there exists such that and .
Proof.
Necessity. Let be a meet-irreducible element and , . Denote and , then and . Hence, or . We next prove that it is impossible for . Inversely, if , by the definition of and , we have such that . This is a contradiction. Thus, there can only be . Moreover, owning to , there exists with . This yields with and .
Sufficiency. Suppose , then there exists such that and . If is not a meet-irreducible element, then there are satisfying and and , i.e., and . Then, we have such that and . According to the known condition, there is meeting and . However, and is the biggest maximal set of ; for the above , there is either or , a contradiction. Therefore, is a meet-irreducible element. □
Corollary 4.
Let be a formal context. For , if there exists with neither nor such that and , then is not a meet-irreducible element.
Corollary 5.
Let be a formal context. For , is a meet-irreducible element iff , where is the cardinality of a set.
Corollary 6.
For , if and , then is not only a molecule, but also a meet-irreducible element.
4. Knowledge Reduction of Knowledge Context Based on FCA
4.1. Classification of Concepts
In the case of knowledge spaces encountered in education, the cardinality of the base of a knowledge space is typically much smaller than the cardinality of . Furthermore, a knowledge space admits at most one base, which is formed by the collection of all the atoms. An atom is a minimal set in containing an element of knowledge domain Q. In fact, an atom at in the knowledge space is in correspondence with a maximal set at q in the collection of the intents of concepts in the knowledge context.
Let be the collection of all molecules in .
Theorem 7.
Let be a formal context, then every formal concept in is the union of some molecules.
Proof.
Suppose . If , then we have . Now, note that . Let
Then, . Next, we only have to prove . In fact, let and assume is not true. By this, there is with . In accordance with Theorem 3, an ideal such that is existent. Since , we have , that is . Then, there exists a minimal element in satisfying . Inevitably, is a molecule. In reality, , while , then . Meanwhile, is a join-irreducible element. If not, there exist such that , but and , which yield and . On the other hand, and is a minimal set. This implies , a contradiction. Hence, is a join-irreducible element. Then, , that is . In accordance with and being a lower set, we have . This contradicts . Therefore, . □
Theorem 8.
Let be a formal context, then every formal concept in is the intersection of some prime elements.
Proof.
This is similar to the above theorem. □
Definition 8.
Let be a formal context; is referred to as a molecular lattice.
Because is a complete distributive lattice, in which molecules, as well as meet-irreducible elements can be regarded as the basic unit generating , it is therefore a molecule is an intersection of some meet-irreducible elements. In order to demonstrate not all the meet-irreducible elements are necessary to show all the molecules, we research the following example.
Example 2.
Figure 1 is a Hasse diagram of a concept lattice generated from a formal context , in which a dot represents a formal concept. Dots 1 and 26 in the diagram correspond to and , respectively. It is easy to see that Concepts are molecules and are meet-irreducible elements. We have
Figure 1.
The Hasse diagram of .
- ,
- ,
- ,
- ,
- ,
- ,
- .
In other words, in this way, all molecules can be represented by some meet-irreducible elements except for Concepts 24 and 25. Namely, not all meet-irreducible elements are necessary absolutely, similar to molecules.
Therefore, we have the following definition.
Definition 9.
Let be a formal context. For any , if is both a meet-irreducible element and a molecule, then is called a necessary concept in ; if is either a meet-irreducible element or a molecule, then is called a relatively necessary concept; otherwise, is called a relatively unnecessary concept.
Example 3
([]). The formal context is given in Table 2, and the Hasse graph of the corresponding concept lattice is shown in Figure 2.
Table 2.
Formal context.
Figure 2.
The Hasse diagram of .
We can see that are all the molecules in and are all the meet-irreducible elements in . Then, both and are necessary concepts, and are relatively unnecessary concepts, and the others are relatively necessary concepts.
4.2. Representing a Concept Lattice Based on a Boolean Matrix
A concept lattice is an ordering of the maximal rectangles defined by a binary relation. In this subsection, we carry out the relationship between the concept lattice and Boolean matrix. Then, a one-to-one correspondence between the set of elements of the concept lattices and the Boolean vector is established. Furthermore, we explain how to use the properties of the Boolean matrix to research the molecules.
Given a formal context , for each , iff object has a value of 1 in attribute and iff object has a value of 0 in attribute , i.e., has no value in attribute . In other words, a formal context can be seen as a Boolean matrix , defined by
We call the relation matrix of F. This point of view enables us to establish a relationship between the concept lattice and Boolean matrix. This may prove important in many applications. It is well known that, for a relation matrix of a formal context, a row vector is the feature vector of , , and a column vector is the eigenvector of the corresponding , , denoted as and , respectively. As a consequence, we can use Boolean matrices to characterize the formal context, a subset of the objects, or a subset of the properties is characterized by eigenvectors.
Definition 10
([]). Let be a formal context. M denotes a Boolean matrix composed of all (), and N denotes a Boolean matrix consisting of all (). Then, we call M and N the object relation matrix and attribute relation matrix in , respectively.
Lemma 5.
Let , and be Boolean matrices. Then:
- 1.
- if and only if for , ;
- 2.
- , ;
- 3.
- , where ;
- 4.
- , .
Theorem 9
([]). Let be a formal context with the relation matrix with respect to R. For any and , then and .
Proof.
We assume that and . Since , we have
Hence, . Similarly, □
Corollary 7.
Let be a formal context with the relation matrix with respect to R. For any and , then and .
The following theorems can help us determine whether or not a concept is a molecule or a meet-irreducible element.
Theorem 10.
Let be a formal context and . Then, is a molecule in iff one of the following two conditions holds:
- 1.
- There does not exist such that , and , i.e., ;
- 2.
- and .
Proof.
If there does not exist such that and , then by the definition, it is obvious that is a molecule in .
Now, we assume that . If , we can find an attribute . Since , there exists such that . On the other hand, for any satisfying and , we have for . Consequently, we conclude that , then is a molecule in .
Under the other situation of , we cannot find any attribute such that and for any satisfying and . Alternatively, it is hard to find any attribute such that for an and for all satisfying and . Thus, we can conclude that , then is not a molecule in . □
Theorem 11.
Let be a formal context and . Then, is a meet-irreducible element in iff one of the following two conditions holds:
- 1.
- There does not exist such that , , that is, ;
- 2.
- and .
It should be pointed out that we are interested in receiving the intents of all molecules without generating the concept lattice. As we know, for any and , which imply a row in the relation matrix is an intent of a concept and a column is an extent of a concept in .
It should be noted that there is a concept such that for any attribute proposed by Mao et al. []. Moreover, Mao et al. [] proposed a proposition that any concept in is either equivalent to () satisfying or the intersection of its two father concepts, that is there are with . Then, we write two results as follows.
Lemma 6.
Let be a formal context and . There exists such that .
Proof.
We assume that . There are satisfying , . Note that and, obviously, . Since , then , . □
Theorem 12.
Let be a formal context. For any , it satisfies one of the following conditions:
- 1.
- There exists such that and ;
- 2.
- There exist and with such that , i.e., can be represented by the union of its two sub-concepts.
Proof.
Now, we assume that .
Case 1. If there exists such that , then for any , namely, . Then, by .
Case 2. Suppose , . In such a case, by the definition of a concept, for any and , then since . Consequently, by the property of the complete lattice, there are and with such that . □
Wu et al. [] showed that each pair , , may be a join-irreducible element, i.e., a molecule in . It is asserted above that a concept in is not a molecule if it is represented by the union of its two sub-concepts. Therefore, the object relation matrix can be used to achieve the molecules in formal contexts without generating concept lattices. The algorithm (Algorithm 1) of concept reduction in a formal context is shown as follows, and its time complexity is less than .
Furthermore, in general, an atom in a knowledge space is a non-join-irreducible element that corresponds to a non-meet-irreducible element of the intents of concepts in . Therefore, the object relation matrix can be used to achieve the molecules in formal contexts without generating concept lattices. The time complexity of algorithm-based concept reduction in a formal context is less than .
| Algorithm 1: Concept reduction in formal contexts |
Input: A knowledge context , where and Output://the collection of intents of all concepts 1: Generate the relation matrix 2: Complement of object relation matrix ← 3: for ton 4: if and 5: ←, , ← // delete and in M 6: end if 7: end for 8: for ton 9: if and 10: find corresponding to 11: ←, , ← 12: end if 13: end for 14: while 15: if // empty matrix 16: 17: make uniquely so that each pair of rows in is different 18: ← 19: for tox 20: find the th, ⋯, th rows in M corresponding to 21: L ← 22: end for 23: ←, N← 24: ← 25: else 26: end if 27 end while 28: ← |
5. Conclusions
Knowledge reduction is a necessary issue to delete redundant and worthless knowledge in information representation and processing. In addition, KST has a homologous mathematical background based on FCA. In this correspondence paper, we first specifically discussed the lattice structure relationships, i.e., the molecule lattice and the concept lattice. Inspired by this, the acquisition approach of the molecules and meet-irreducible elements was characterized. Finally, numerical algorithms were designed to find the atoms of the knowledge space in the knowledge context via a Boolean matrix. This paper mainly studied the knowledge reduction of the knowledge context, whereas we did not consider the fuzzy circumstances. In reality, various concepts have been investigated from different viewpoints to meet the requirements such as the fuzzy concept, three-way concept, and object-oriented concept. In further work, we will plan to research a knowledge reduction framework in fuzzy knowledge spaces.
Author Contributions
Methodology, L.Y.; Investigation, J.L.; Analysis, C.Z.; Writing, L.Y.; Review, Y.L. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the National Natural Science Foundation of China (No. 11871259) and the Natural Science Foundation of Fujian Province in China (2019J01748, 2017J01507).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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