## 4 The Proposed PIKC-CI Method
### Multi-Source Innovation Knowledge Fusion Based on Semantic Elements Reconfiguration  
For the convenience of detailed elaboration, this sub-section first presents the relevant definitions for process innovation knowledge and its fusion process. Figure 1: A novel process innovation knowledge capture paradigm based on collective intelligence. **Definition 1**. : _Manufacturing process innovation-oriented knowledge network is a set of spatial knowledge structure, formally represented as_  
\[PIK_{\Omega}=\{KN,CTR,U\} \tag{1}\]  
_where KN is a set of multi-type process innovation knowledge units, \(CTR\) is a set of knowledge contextual relevance for specific process innovation scenarios, and \(U\) is a set of social-wiki users involved in knowledge capture._  
The hierarchical structure of the process innovation-oriented knowledge network is shown in Figure 2 and formally defined as follows. **Definition 2**. : _Process innovation knowledge unit is a local capability unit that has the ability to solve certain types of process problems and deliver information. It is defined as_  
\[KN=<P,I_{I},I_{O},E,U> \tag{2}\]  
_where \(P\) is a set of knowledge properties, \(I_{I}\) and \(I_{O}\) represent the sets of knowledge input interface and knowledge output interface, respectively. \(E\) stands for an encapsulation space for complete knowledge units. Several types of innovation knowledge, \(\Pi_{KN}=\{PHS,PDT,PCM,MSE,ISI,IEP,MCD\}\), are basically used in the innovation design process. Among them, PHS is the Problem Heuristic Scene, PDT is the Problem Description Template, PCM is the Process Contradiction Matrix, MSE is the Manufacturing Scientific Effect, 1S1 is the Innovative Scheme Instance, 1 EP is the Innovative Evaluation Parameter, and MCD is the Manufacturing Capability Description._  
**Definition 3**. : _Knowledge contextual relevance of manufacturing process innovation is further denoted by_  
\[CTR=\left\{\langle kn,k,r,k^{\prime},u\rangle\big{|}kn\in KN,k,r,k^{\prime}\in \mathbb{O},u\in U\right\} \tag{3}\]  
Figure 2: Schematic diagram of process innovation-oriented knowledge network. _where \(k,\ r,\ k^{\prime}\) are ontological entities defined in process innovation domain ontology \(\mathbb{O}\), and \(r\) is a contextual relationship between \(k\) and \(k^{\prime}\)._  
**Definition 4**. : _Domain ontology \(\mathbb{O}\) consists of a series of concepts and relationships that represent domain knowledge models. It is defined as_  
\[\mathbb{O}:=\left(C,R,\mathcal{E}_{R},I_{C}\right) \tag{4}\]  
_where \(C\) and \(R\) are a set of classes and a set of relations, respectively; \(\mathcal{E}_{R}\subseteq C\times C\) represents a set of relationships between classes, which can be denoted as a set of triples \(\left\{\langle c,r,c^{\prime}\rangle|c,c^{\prime}\in C,r\in R\right.\}\); and \(I_{C}\) is a power set of instance sets of a class \(c\in C\)._  
The knowledge elements of process innovation knowledge are generally expressed in terms of domain terms or natural language descriptions. For example, process conflict parameters can be expressed as process parameters and their deformation, while process innovation principles can be expressed in natural language form. A knowledge element of natural language descriptions is composed of one or more propositions; a proposition is a complete semantic unit that contains terminology and predicate terms. **Definition 5**. : _Process innovation knowledge element represents a complete and indivisible knowledge unit in knowledge space \(PIK_{\Omega}\). It is defined as_  
\[Ke=\left\{\Sigma,\Lambda,\Theta\right\} \tag{5}\]  
_where \(\Sigma=\left\{t_{1},t_{2},\ldots t_{i},\ldots t_{n}\right\}\), \(t_{i}(i=1,2,\ldots,n)\) is terminology, and \(\Lambda=\left\{p_{1},p_{2},\ldots p_{j},\ldots p_{m}\right\}\), \(p_{j}(j=1,2,\ldots m)\) is the predicate term.