Concept Learning Supported Semantic Search Using Multi-Agent System Based on Social Networks
| atmire.migration.oldid | 2019 | |
| dc.contributor.advisor | Far, Behrouz | |
| dc.contributor.advisor | Eberlein, Armin | |
| dc.contributor.author | El_sherif, Shimaa Mansour Moailak | |
| dc.date.accessioned | 2014-04-17T17:20:33Z | |
| dc.date.available | 2014-06-16T07:00:30Z | |
| dc.date.issued | 2014-04-17 | |
| dc.date.submitted | 2014 | en |
| dc.description.abstract | In this research, we propose an agent-based semantic search system supported by ontological concept learning and contents annotation. Our system consists of a group of multi-agent systems (MASs), each controlling a repository of structured and unstructured documents. Each repository has its own concept hierarchy, i.e. ontology. . Each MAS consists of a group of agents, each with its own responsibilities and an assigned tasks to perform. All agents in each MAS cooperate with each other to perform more general tasks. Agents of different MASs communicate with each other by developing a common understanding of concepts used during communication. Agents communicate with each other via a social network. Strengths of ties between agents in the social network represent how close/far two agents are to each other. In our system, there are two major modules: a semantic search module and a concept learning module. In the semantic search module, MASs cooperate with each other to perform semantic search and return results back to the user. The second major module in our system is the concept learning module. During the semantic search process, a MAS may discover that it needs to learn new concepts. The Concept Learning module helps that MAS to learn the new concepts from other MASs. A social network is used in communication between agents from different MASs. In this research, we define two case studies to test the system. These case studies evaluate the efficiency of using social networks in representing relationships between agents in different MASs in learning new concepts from several teachers. We also introduce a novel approach of calculating tie strengths in social networks using Hidden Markov Model (HMM). The results obtained show that using social networks in communicating between agents in different MASs has a positive effect in our system. During leaning new concepts, using social networks between the learner and the teachers gives betters accuracies in all concepts learnt and with different machine learning techniques used. On the other hand, using social networks decrease the negative effect of increasing number of teachers in the concept learning process. | en_US |
| dc.identifier.citation | El_sherif, S. M. (2014). Concept Learning Supported Semantic Search Using Multi-Agent System Based on Social Networks (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25406 | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/25406 | |
| dc.identifier.uri | http://hdl.handle.net/11023/1419 | |
| dc.language.iso | eng | |
| dc.publisher.faculty | Graduate Studies | |
| dc.publisher.institution | University of Calgary | en |
| dc.publisher.place | Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Computer Science | |
| dc.subject | Engineering--Electronics and Electrical | |
| dc.subject.classification | Multi-Agent System | en_US |
| dc.subject.classification | Social networks | en_US |
| dc.subject.classification | Concept Learning | en_US |
| dc.title | Concept Learning Supported Semantic Search Using Multi-Agent System Based on Social Networks | |
| dc.type | doctoral thesis | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Doctor of Philosophy (PhD) | |
| ucalgary.item.requestcopy | true |