In this paper, we propose a general agri-ontology-based knowledge fusion architecture as shown in Figure 2. The architecture consists of three main aspects: 1) agricultural ontology and fusion rules are the cornerstones of the convergence of agricultural knowledge; 2) agricultural ontology-based knowledge representation and matching, as well as mining and automatically selecting fusion rules based on the property of concept, are the key components in knowledge fusion; 3) in order to find more accurate knowledge to satisfy users’ queries, assessment of the fusion results is necessary to enhance knowledge fusion. All these parts form a complete system of knowledge fusion.
4.2 Fusion Rules
Each fusion rule, such as Min, Max, and Avg, can be looked as an aggregation function in the database (Xie et al., 2012). We divide fusion rules into two types: the single data fusion rule and the multi data fusion rule.
Definition 4: The single data fusion rule (SFR) is a type of aggregation function such that:
f:D1×D2× , . . . , ×Dn→D
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\[
f:D1 \times D2 \times {\rm{ }},{\rm{ }}.{\rm{ }}.{\rm{ }}.{\rm{ }},{\rm{ }} \times Dn \to D
\]
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where Di is the value domain that has been unified as a domain so D1 = D2 = , …, = Dn. Given vi∈D (i = 1,2, …, n), ƒ(v1, v2, …, vn) = v, v∈D. In this paper, the SFR includes Majr(Majority rule), Max, Min, Avg, Minr (Min-Priority rule), etc.
Definition 5: The multi data fusion rule (MFR) is a type of aggregation function such that:
f:
D
1
×
D
2
× . . . ×
D
n
→
2
D
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\[
f:{D_1} \times {D_2} \times {\rm{ }}.{\rm{ }}.{\rm{ }}.{\rm{ }} \times {D_n} \to {2^D}
\]
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given vi∈D(i = 1, 2, …, n), ƒ(v1, v2, …, vn) = D′, vi∈D, D′⊆ D. The MFR includes CInt (Interval Rule), Or, and And.
In general, the single data fusion rule and the multi data fusion rule cannot be applied to an information set. Instead, we must analyze the query and answer type and then define a combination of fusion rules. However, usually the user participates in the rules selection to finish the knowledge fusion process. We have defined 13 fusion operator rules based on global ontology. For example, a closed interval operator is a fusion operator whose definition is as follows:
Definition 6: Given a domain D and possible values on it D′ = {v1′, v2′, … vn′}, the closed interval operator(CInt) satisfies:
CInt(
D
′
)=[
v
i
,
v
j
], if ∀
v
′
i
∈
D
′
, then
v
′
i
∈[
v
i
,
v
j
]
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\[
CInt(D') = [{v_i},{\rm{ }}{v_j}],{\rm{ if }}\forall {\rm{ }}{v'_i} \in D',{\rm{ then }}{v'_i} \in [{v_i},{\rm{ }}{v_j}]
\]
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Example 1: If there exist three possible tuples: v1= (Wang da hong; age; 12), v2 = (Wang da hong; age; 13), and v3 = (Wang da hong; age; 15), then we will get CInt ({v2, v2, v3}) = (Wang da hong; age; [12–15]).
In our Fusion rule selection, each rule will be limited to some condition that can be deduced by a rule character and a query that can be defined:
Definition 7: Given query ontology Ω, a knowledge fusion query can be formally defined:
o⋅{(
s
1
, f
r
1
)=?,…,(
s
n
, f
r
n
)=?}|cnt, o⋅{(
s
1
, f
r
1
)=?,…,
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\[
o \cdot \{ ({s_1}, f{r_1}) = ?,...,({s_n}, f{r_n}) = ?\} |cnt, o \cdot \{ ({s_1}, f{r_1}) = ?,...,
\]
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where (sn, frn) = ?} represents query objects, and cnt is a set of constraint conditions. O is a concept or instance in Ω, s1 is a slot (attribute) of o, and fr1 is a fusion rule. If fr1 is omitted, the query will be changed into a general query in traditional information integration.
Example 2: Given a query = Potato · (price, Avg), the knowledge fusion system should provide an average price of a price set of potatoes returned by information integration. If Avg is NULL, then the knowledge fusion system will return the potato price in a way similar to traditional information integration. Often a user can select a rule according his preference.
In query ontology Ω, we define a default rule for each slot of a concept, involving two slot types: meta-slot and composite-slot. A meta-slot is a slot that cannot be divided semantically while a composite-slot can be divided into many meta-slots. For example, slot IdentityNo of a concept person is a meta-slot, but Name, usually, is a composite-slot including a meta-slot first-name and a meta-slot last name. A fusion rule for meta-slot is always pre-defined according to the meta-slot definition, but a composite-slot usually needs a concatenate rule. In order to acquire a high quality answer, we need to extend the slots of a concept to filter out useless information. The slots also are called data quality slots including:
Authority (DQa) The data quality authority is used to measure the probability of information correctness in information sources.
Timeliness (DQt) Timeliness presents a means to estimate the goodness (or badness) of information in information sources in terms of time.
Completeness(DQc) The degree to which all data relevant to an application domain have been recorded in an information source.
Therefore, given a concept and its slot set {a1, a2, …, an}, the extensional slot set will be {a1, a2, …, an, DQa, DQt, DQc}.
4.3 Knowledge Inconsistency Problem Analysis
In general, knowledge consistency means a judgment is in accord with both historical judgments and current facts. On the other hand, inconsistency means a contradiction between the historical judgments and current facts. From the aspect of ontology, consistency means that the logic relationships of the terminology are consistent while inconsistency means conflicts exist between some parts of the ontologies. For example, we define grain crops and cash crops as disjoint classes that do not have the same instances. If the class wheat belongs to both grain crops and cash crops, an inconsistency will occur.
In this paper, agricultural ontology consistency includes consistency between the ontology definition and the knowledge based on the ontology. This means that we cannot obtain conflicting knowledge from the knowledge base. Generally, when a knowledge base exists, conflict knowledge depends on the following conditions:
The consistency of concept defining. That is to say, the formal definition contains the same meaning as the informal one. Take the concept “dogs” as an example. If the formal definition of dogs is the same as that of the concept cats, inconsistency exists.
The consistency of concept extension. In terms of formal or non-formal concept definitions, conflict knowledge can exist through concept explanation (including reasoning). For example, cats can catch mice, but we cannot say that mice can catch cats.
The consistency of axioms. The axiom system will not produce conflict knowledge.
From the viewpoint of knowledge application, the knowledge base can guide users to make correct decisions and ensure that no confusing conclusions arise. In brief, consistency is an important criterion with which to evaluate an ontology-based knowledge base. Knowledge inconsistency will lead to unreliable service, which threatens knowledge correctness. This paper proposes a method with which to check ontology consistency.
Definition 8: Given knowledge base K, the knowledge inconsistency problem is a 3 triple KI = (K, Y, Q), which satisfies :
Y = {y1, y2, …, yn} is a knowledge operation set.
Q is a given knowledge query.
Definition 9: Given knowledge inconsistency problem KI = (K, Y, Q). If a knowledge conflict exists in K, it satisfies the following conditions:
∃ k, k11, k22, …, k1j ∈ K, y11, y12, …, y1j ∈ Y,
∑
1
j
y
1l
(
k
1l
)
|=k∧k→Q
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\[
\sum\limits_1^j {{y_{1l}}({k_{1l}})} | = k \wedge {\rm{k}} \to {\rm{Q}}
\]
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. The symbol |= indicates “reason out” and → represents “can satisfy”.
∃ k, k11, k22, …, k1m ∈ K, y21, y22, …, y2j ∈ Y,
∑
1
j
y
2l
(
k
2l
)
|=¬k∧¬k→Q
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\[
\sum\limits_1^j {{y_{2l}}({k_{2l}})} | = \neg k \wedge \neg {\rm{k}} \to {\rm{Q}}
\]
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.
From the above definitions, we see that the knowledge base has inconsistency if there are two pieces of contradictory knowledge. It is very import to find a mechanism or method to check this knowledge base inconsistency (Xie, 2012).