## 4 The Experts' Tool
They can also tag triples as _\"worth considering\"_ (thumbs up in Figure 9) or _\"irrelevant\"_ (thumbs down) as indications to the other experts who collaborate in the development of the same experience. Since the suggested triples may come in large numbers, they appear in the list ordered according to estimates of their relatedness to (i) the keywords that define the reflective topic and (ii) the keywords that define the expert's areas of competence (also free text or borrowed from EuroVoc). Relatedness is measured by using the word embeddings of _word2 vec_[24], which are huge collections of numbers reflecting which words appear commonly in close proximity to others in a Google News dataset. Specifically, we rate every sequence of semantic triples that takes from one venue to another in the CCKB according to Equation (1):  
\[ \begin{split} relatedness(\mathit{Sequence},\mathit{Topic},\mathit{ Expert})=&\frac{1}{\#\mathcal{E}(\mathit{Sequence})\cdot\#\mathcal{K}(\mathit{Topic})}\cdot\bigg{(}\sum_{e\in \mathcal{E}(\mathit{Sequence})}\sum_{k\in\mathcal{K}(\mathit{Topic})}WCD(e,k) \bigg{)}\cdot\\ &\frac{1}{\#\mathcal{E}(\mathit{Sequence})\cdot\#\mathcal{K}( \mathit{Expert})}\cdot\bigg{(}\sum_{e\in\mathcal{E}(\mathit{Sequence})}\sum_{k \in\mathcal{K}(\mathit{Expert})}WCD(e,k)\bigg{)}\end{split}  \tag{1}\]  
\(\bullet\)\(WCD\): word cosine distance. \(\bullet\)\(\#\): the cardinality of a set. \(\bullet\)\(\mathcal{E}(\mathit{Sequence})\): the set of entities in a sequence of triples. \(\bullet\)\(\mathcal{K}(\mathit{Topic})\): the set of keywords linked to a reflective topic. \(\bullet\)\(\mathcal{K}(\mathit{Expert})\): the set of keywords in the expert's profile. Figure 10 shows the dialog that appears when an expert clicks on a node of the graph. Using that interface, she can link the corresponding entity to snippets of information that will make up the narrative of the reflective topic, including text and/or multimedia contents. Besides, the nodes can be turned into multiple-choice questions for the quiz game. The choices may be presented in the shape of text, image galleries, time lines or maps. The alternatives to the correct entity may be entered manually by the experts or suggested by a recommender system running on the CrossCult platform, which simply picks from any relevant _wikicategory_ that contains the entity. (Wikicategories are groupings of Wikipedia pages on similar subjects. A global index of such groupings in English can be found at [[https://en.wikipedia.org/wiki/Wikipedia:Quick_cat_index](https://en.wikipedia.org/wiki/Wikipedia:Quick_cat_index)]([https://en.wikipedia.org/wiki/Wikipedia:Quick_cat_index](https://en.wikipedia.org/wiki/Wikipedia:Quick_cat_index)).) First of all, the relevance of the wikicategories in relation to the current reflective topic and the expert's areas of competence is computed as indicated by Equation (2), where \(\mathcal{C}(\mathit{Wikicat})\) denotes the set of entities included in a given category:  
\[ \begin{split} relevance(\mathit{Wikicat},\mathit{Topic},\mathit{ Expert})=&\frac{1}{\#\mathcal{E}(\mathit{Wikicat})\cdot\#\mathcal{K}( \mathit{Topic})}\cdot\bigg{(}\sum_{e\in\mathcal{E}(\mathit{Wikicat})}\sum_{k \in\mathcal{K}(\mathit{Topic})}WCD(e,k)\bigg{)}\cdot\\ &\frac{1}{\#\mathcal{E}(\mathit{Wikicat})\cdot\#\mathcal{K}( \mathit{Expert})}\cdot\bigg{(}\sum_{e\in\mathcal{E}(\mathit{Wikicat})}\sum_{k \in\mathcal{K}(\mathit{Expert})}WCD(e,k)\bigg{)}\end{split}  \tag{2}\]  
Figure 9: Edition of topics and connections. By this equation, the recommender system identifies the four most relevant wikicategories, and then it puts all the entities they contain in a unique set, from which it can pick alternatives whenever the expert clicks on the \"_Get recommendation_\" button. The selection can take into account the levels of knowledge indicated in the definition of user types (if any): compared to higher levels of knowledge, lower levels yield alternatives which are further from the correct entity in terms of \(WCD\).