# A Heterogeneous Battlefield Situation Information Sharing Method Based on Content
## 5 Information Sharing Mechanism Based on Content
The _ERrecord_ function is used to record the position of the attribute \(a\) in \(T_{LEC}(V_{EC})\), including the first level entity class \(EC_{a}\) that it describes, as well as the level \(Level_{a}\) and the number \(LNun_{a}\) in \(Level_{a}\) where it is located. \(Doc_{i}\) represents the document stored in UIS that describes \(e_{i}(i\in\mathbf{N})\), and the first level entity class that \(e_{i}\) belongs to is \(EC_{i}\). The _Match_ function is used to match the value of \(a_{i}\) in the particular position of \(T(e_{i})\) with \(v_{a}\), to determine whether the entity \(e_{i}\) is the one for which we are searching. The _CoFind_ function is used to find the position of semantic links directed to \(e_{f}\) in the first row of SRM. The _CoRecord_ function is used to record the column number \(c_{f}\) of the semantic links directed to \(e_{f}\) in SRM. \(Doc_{i}\) represents the document stored in UIS that describes \(e_{i}(i\in\mathbf{N})\), and the first level entity class that \(e_{i}\) belongs to is \(EC_{i}\). Algorithm complexity analysis: Assume that there are \(l\) nodes in \(T_{LEC}(V_{EC})\) and \(n\) documents in the UBSIS. * The time complexity: The average time complexity of finding attribute in \(T_{LEC}(V_{EC})\) is O(log\(l\)), and the average time complexity of attribute matching in the UIS is O(\(n\)), then the average time complexity of this algorithm is O(\(n\)log\(l\)). As for the semantic judging based on semantic links, SRM is used to find whether there are semantic links between entities, and the average time complexity is O(\(n\)). Thus, the average time complexity \(T_{avr}\) of information searching based on specific attributes and semantic relationships between entities can be regarded as O(\(n\)(1+log\(l\))). * The space complexity: The average space complexity of finding attribute in \(T_{LEC}(V_{EC})\) is O(\(l\)), and the average space complexity of attribute matching in the UIS is O(\(n\)), then the average space complexity of this algorithm is O(\(nl\)). As for the semantic judging based on semantic links, the average space complexity is O(\(n^{2}\)). Thus, the average time complexity \(S_{avr}\) of information searching based on specific attributes and semantic relationships between entities can be regarded as O(\(n\)(\(n+l\))). In the traditional ontology based systems, graph matching and the subgraph indexing technique are usually used for information query. The total number of nodes in the graph is approximately \(n(1+l/k)(k\in\mathbf{N}\wedge k\in[1,l])\), where \(k\) is related to the number of entity classes, especially the number of first level entity classes. Both the average time complexity \(T^{\prime}_{avr}\) and the space complexity \(S^{\prime}_{avr}\) of finding required entities can be O(\(nd^{2}(1+l/k)\)) [26], where \(d\) represents the average node-degree. Then, the ratio between \(T_{avr}\) and \(T^{\prime}_{avr}\), which is denoted as \(TRto\), is \((1+\log l)/(d^{2}(1+l/k))\), while the ratio between \(S_{avr}\) and \(S^{\prime}_{avr}\), which is denoted as \(SRto\), is \((n+l)/(d^{2}(1+l/k))\). When \(n\) increases, \(SRto\) will increases accordingly, which indicates that the space complexity of our method in information query is usually larger than that of graph matching. As for the time complexity, Figure 11 shows the relation of \(k\), \(l\), and \(d\) when \(TRto=1\), where we can see that:  
* When \(k\) is fixed, \(TRto\) decreases with the increase of \(d\) or \(l\), that is \(TRto\) will be smaller when the number of nodes in \(T_{LEC}(V_{EC})\), i.e., the number of categories, attributes, or states that describe entities is larger or the relationships among entities is more complex. For example, if \(k=1\), i.e., all the entities belong to the same first entity class, then \(TRto\) is usually small than one as \(d\) is usually larger than one. * When \(l\) is fixed, \(TRto\) decreases with the increase of \(d\), i.e., \(TRto\) will also be smaller when the relationships among entities is more complex.