## 4 The Proposed PIKC-CI Method
### Multi-Source Innovation Knowledge Fusion Based on Semantic Elements Reconfiguration
\(\Theta=\Sigma\oplus\Lambda=\left\{t_{1},t_{2},\ldots t_{i},\ldots t_{n} \right\}\oplus\left\{p_{1},p_{2},\ldots p_{j},\ldots p_{m}\right\}\) denotes the logical plus operation of sets \(\Sigma\) and \(\Lambda\)._  
Thus, several general characteristics of knowledge elements can be introduced from Definition 5: (1) knowledge elements have a certain structure and constitute the smallest controllable unit of process innovation knowledge; (2) knowledge elements are logically complete and capable of expressing facts, principles, methods, and so on; (3) new knowledge can be generated by semantically correlating multi-sourced knowledge elements. **Definition 6**. : _Natural language description \(D\) and its composition proposition \(P_{i}\) of process innovation knowledge element can be further represented as_  
\[ \begin{array}{c}D\triangleq\cup P_{i}(i=1,2,\ldots,n),\\ P_{i}\triangleq\cup(t_{ij})\oplus\cup(p_{ij})\ (i=1,2,\ldots,n;j=1,2,\ldots m)\end{array}  \tag{6}\]  
_where \(t_{ij}(i=1,2,\ldots,n;j=1,2,\ldots m)\) is terminology of proposition, and \(p_{ij}(i=1,2,\ldots,n;j=1,2,\ldots m)\) is the predicate of proposition._  
Knowledge fusion is a process of forming new knowledge, with the help of multi-source knowledge interaction and support. For terminology fusion, the terminology specification and terminology conflict resolution of the fusion process are based on domain ontology and semantic relationships. For knowledge element sets of natural language description, we can deconstruct them as subject-predicate-object (SPO) logical form triples and then reconfigure semantic elements through co-reference relationship identification under the domain ontology constraints. The algorithm flow of knowledge fusion for process innovation knowledge is represented in Figure 3, and the specific process is given as follows:  
**Step 1. ** Determine the knowledge candidate set for fusion target of knowledge unit \(KN_{k}^{cnd}\) and knowledge contextual relevance \(CTR_{t}^{cnd}\). \[ \begin{array}{l}KN_{k}^{cnd}=\left\{<P_{k},I_{lk},I_{0k},E_{k},u_{k}>|k=1,2, \ldots,n\right\},\\ CTR_{t}^{cnd}=\left\{\langle kn_{t},k_{t},r_{t},k^{\prime},u_{t}\rangle|t=1,2, \ldots,m\right\}\end{array} , \tag{7}\]  
where \(n\) and \(m\) are the number of knowledge unit candidates and the number of knowledge contextual relevance candidates in the fusion process, respectively. **Step 2. ** Select the target knowledge elements of knowledge candidate set \(Ke^{obj}\), and judge whether it is a terminology type. If so, then go to Step 3, otherwise turn to Step 4. **Step 3. ** Standardize the candidate terminology set and perform logical plus operation based on domain ontology. If completed, turn to step 7. For two knowledge elements \(Ke_{i}\), \(Ke_{j}\) in fusion process, if there are terminology items \(t_{i}\in Ke_{i}\), \(t_{j}\in Ke_{j}\) and terminology conflict \(t_{i}\times t_{j}\), those conflicts will be resolved according to the following rules:  
1. When the terminologies have similar meanings but different expressions, we can map terminology items \(t_{i}\), \(t_{j}\) into the terminology set logic tree \(T\) of domain ontology, and the result can be denoted by \(R\). If \(T(t_{i})\subset T(t_{j})\), then \(R=t_{j}\); if \(T(t_{i})\supset T(t_{j})\), then \(R=t_{i}\); if \(T(t_{i})=T(t_{j})\), then \(R=t_{i}\) or \(t_{j}\). 2. When the terminology items have contrary logic, conflict resolution will depend on collective intelligence. **Step 4. ** Execute semantic and grammatical analysis for the candidate natural language descriptions, and extract SPO logical form triples by using semantic linguistic tool NLPWin [54], which provides deep syntactic and partial semantic analysis of text, then deconstruct them as a set of semantic elements \(S_{SF}\). Figure 3: Algorithm flow of process innovation knowledge fusion based on semantic elements reconfiguration. **Step 5. ** Identify co-reference relationship of terminology entities for \(S_{SF}\).