largely based of such community structures that are normally hidden within and AKT across organisations. Ontology OntoCoPI is a tool that uses ontology-based network analysis to User interest breadth-first spreading activation algorithm is applied by User OntoCoPI to crawl the ontology network of instances and publications relationships to extract patterns of certain relations between entities relating to a community of practice. The crawl can be Quickstep OntoCoPl limited to a given set of ontology relationships. These relationships can be traced to find specific information, such as who attended the same events, who co-authored papers and who are members of the same project or organisation. Communities of Figure 7. Ontology and recommender system integration practice are based on informal sets of relationships while ntologies are normally made up of formal relationships. The Upon start-up, the ontology provides the recommender hypothesis underlying OntoCoPI is that some informa with an initial set of publications for each of its registere ationships can be inferred from the presence of formal ones Each user's known publications are then correlated For instance, if A and B have no formal relationships, but they recommender systems classified paper database, and a set of have both authored papers with C, then that could indicate a historical interests compiled for that user. These historical interests form the basis of an initial profile, overcoming the new One of the advantages of using an ontology to identify system cold-start problem. Figure 8 details the initial profile communities of practice, rather than other traditional information algorithm. As per the Quickstep profiling algorithm, fractional networks [3 is that relationships can be selected according to interest in a topic super-classes is inferred when a specific topic is their semantics, and can have different weights to reflect relative importance. For example the relations of document authorship and project membership can be selected if it is required to identify communities of practice based on publications and project work. ∑ 1/ publication age(n) OntoCoPI allows manual selection of relationships or automatic selection based on the frequency of relationship use within the knowledge base. Selecting the right relationships and weights is an experimental process that is dependent on the ontolog structure,the type and amount of information in the ontology, and new-system initial profile =(t, topic interest(D))* the type of community of practice required t= <research paper topic> When working with a new community of practice some Figure 8. New-system initial profile algorithm experiments will be needed to see which relationships are relevant to the desired community of practice, and how to set relative weights. In the experiments described in this paper, certain When the recommender system is up and running and a new user relationships were selected manually and weighted based on our is added, the ontology provides the historical publication list of preferences. Further trials are needed to determine the most the new user and the OntoCoPI system provides a ranked list of effective selection similar users. The initial profile of the new user is formed from a correlation between historical publications and any similar user 7. INTEGRATION OF THE TWO profiles. This algorithm is detailed in figure 9, and addresses the TECHNOLOGIES new-user cold-start problem We have investigated the integration of the ontology, Onto CoPI and Quickstep recommender system to provide a solution to both the cold-start problem and interest acquisition problem. Figure 7 shows our experimental systems after integration.largely based on interviews, mainly because of the informal nature of such community structures that are normally hidden within and across organisations. OntoCoPI is a tool that uses ontology-based network analysis to support the task of community of practice identification. A breadth-first spreading activation algorithm is applied by OntoCoPI to crawl the ontology network of instances and relationships to extract patterns of certain relations between entities relating to a community of practice. The crawl can be limited to a given set of ontology relationships. These relationships can be traced to find specific information, such as who attended the same events, who co-authored papers and who are members of the same project or organisation. Communities of practice are based on informal sets of relationships while ontologies are normally made up of formal relationships. The hypothesis underlying OntoCoPI is that some informal relationships can be inferred from the presence of formal ones. For instance, if A and B have no formal relationships, but they have both authored papers with C, then that could indicate a shared interest. One of the advantages of using an ontology to identify communities of practice, rather than other traditional information networks [3] is that relationships can be selected according to their semantics, and can have different weights to reflect relative importance. For example the relations of document authorship and project membership can be selected if it is required to identify communities of practice based on publications and project work. OntoCoPI allows manual selection of relationships or automatic selection based on the frequency of relationship use within the knowledge base. Selecting the right relationships and weights is an experimental process that is dependent on the ontology structure, the type and amount of information in the ontology, and the type of community of practice required. When working with a new community of practice some experiments will be needed to see which relationships are relevant to the desired community of practice, and how to set relative weights. In the experiments described in this paper, certain relationships were selected manually and weighted based on our preferences. Further trials are needed to determine the most effective selection. 7. INTEGRATION OF THE TWO TECHNOLOGIES We have investigated the integration of the ontology, OntoCoPI and Quickstep recommender system to provide a solution to both the cold-start problem and interest acquisition problem. Figure 7 shows our experimental systems after integration. AKT Ontology User interest profiles User publications User and domain knowledge Communities of practice Quickstep OntoCoPI AKT Ontology User interest profiles User publications User and domain knowledge Communities of practice Quickstep OntoCoPI Figure 7. Ontology and recommender system integration Upon start-up, the ontology provides the recommender system with an initial set of publications for each of its registered users. Each user’s known publications are then correlated with the recommender systems classified paper database, and a set of historical interests compiled for that user. These historical interests form the basis of an initial profile, overcoming the new￾system cold-start problem. Figure 8 details the initial profile algorithm. As per the Quickstep profiling algorithm, fractional interest in a topic super-classes is inferred when a specific topic is added. ˇ n 1.. publications belonging to class t topic interest(t) = 1 / publication age(n) t = <research paper topic> new-system initial profile = (t, topic interest(t))* ˇ n 1.. publications belonging to class t topic interest(t) = 1 / publication age(n) t = <research paper topic> new-system initial profile = (t, topic interest(t))* Figure 8. New-system initial profile algorithm When the recommender system is up and running and a new user is added, the ontology provides the historical publication list of the new user and the OntoCoPI system provides a ranked list of similar users. The initial profile of the new user is formed from a correlation between historical publications and any similar user profiles. This algorithm is detailed in figure 9, and addresses the new-user cold-start problem