Papers by Rouzbeh Meymandpour
International Conference on Information and Knowledge Management, Proceedings, 2012
ABSTRACT Linked Data offers new opportunities for Semantic Web-based application development by c... more ABSTRACT Linked Data offers new opportunities for Semantic Web-based application development by connecting structured information from various domains. These technologies allow machines and software agents to automatically interpret and consume Linked Data and provide users with intelligent query answering services. In order to enable advanced and innovative semantic applications of Linked Data such as recommendation, social network analysis, and information clustering, a fundamental requirement is systematic metrics that allow comparison between resources. In this research, we develop a hybrid similarity metric based on the characteristics of Linked Data. In particular, we develop and demonstrate metrics for providing recommendations of closely related resources. The results of our preliminary experiments and future directions are also presented.
Ranking of universities represents a complex endeavor which involves gathering, weighting, and an... more Ranking of universities represents a complex endeavor which involves gathering, weighting, and analyzing diverse data. Emerging semantic technologies enable the Web of Data, a giant graph of interconnected information resources, also known as Linked Data. A recent community effort, Linking Open Data project, offers the possibility of accessing a large number of semantically described and linked concepts in various domains. In this paper, we propose a novel approach to take advantage of this structured data in the domain of universities to develop proxy measures of their relative standing for ranking purposes. Derived from information theory, our approach of computing the Information Content for universities and ranking them based on these scores achieved results comparable to the international ranking systems such as Shanghai Jiao Tong University, Times Higher Education, and QS. The metric we developed can also be used for innovative semantic applications in a range of domains for e...
We propose LODify, a hybrid recommendation method which measures the semantic similarity of items... more We propose LODify, a hybrid recommendation method which measures the semantic similarity of items or resources of interest and combines this with user ratings to make recommendations across diverse domains. The semantic similarity metric draws on information theory and computes the similarity of items based on the information content of their shared characteristics. Detailed semantic analysis of items, considering the special characteristics of Linked Data represented using various kinds of relations, incorporating the relative importance of each relation into the similarity measurement, and successful handling of the item cold-start problem are among the key benefits of the presented approach. We demonstrate how this approach can be successfully applied to provide recommendations and to predict user ratings. 1 LODify and its Innovation Linking Open Data (LOD) project is one of the successful initiatives of the Web of Data which aims to publish and link public datasets in a wide var...
The Linked Open Data (LOD) project is a community effort that aims to publish structured data usi... more The Linked Open Data (LOD) project is a community effort that aims to publish structured data using open and liberal licences. The LOD cloud provides free access to datasets in diverse areas such as media, geography, publications and life sciences. These datasets are publicly available for machine and human consumption using Semantic Web standards and SPARQL endpoints. In addition to facilitating interoperability and integrity across diverse platforms, this movement not only opens up unique opportunities for developing novel and innovative applications but also makes the application development more efficient and cost-effective. This paper demonstrates how LOD can be a reliable and rich source of content information that supports recommender systems to overcome problems such as the item cold-start problem and limited content analysis that restrict many of the existing systems. By building on a robust measurement of the similarities between items using LOD, we present a hybrid recomm...
Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), 2019
For network analysts, understanding how traffic flows through a network is crucial to network man... more For network analysts, understanding how traffic flows through a network is crucial to network management and forensics such as network monitoring, vulnerability assessment and defence. In order to understand how traffic flows through a network, network analysts typically access multiple, disparate data sources and mentally fuse this information. Providing some sort of automated support is crucial for network management. However, information about the quality of the network data sources is essential in order to build analyst's trust in automated tools. This paper presents SydNet, a novel Linked Data quality assessment framework which allows analysts to define quality dimensions and metrics which provide an accurate reflection of the quality of the data sources. The SydNet architecture also provides a number of novel fusion heuristics which can be used to fuse data from various network data sources. We demonstrate the utility of the SydNet architecture using CAIDA longitudinal top...
Knowledge and Information Systems
Communications in Computer and Information Science
Ranking of universities represents a complex endeavor which involves gathering, weighting, and an... more Ranking of universities represents a complex endeavor which involves gathering, weighting, and analyzing diverse data. Emerging semantic technologies enable the Web of Data, a giant graph of interconnected information resources, also known as Linked Data. A recent community effort, Linking Open Data project, offers the possibility of accessing a large number of semantically described and linked concepts in various domains. In this paper, we propose a novel approach to take advantage of this structured data in the domain of universities to develop proxy measures of their relative standing for ranking purposes. Derived from information theory, our approach of computing the Information Content for universities and ranking them based on these scores achieved results comparable to the international ranking systems such as Shanghai Jiao Tong University, Times Higher Education, and QS. The metric we developed can also be used for innovative semantic applications in a range of domains for entity ranking, information filtering, and multi-faceted browsing.
Linked Data allows structured data to be published in a standard manner so that datasets from div... more Linked Data allows structured data to be published in a standard manner so that datasets from diverse domains can be interlinked. By leveraging Semantic Web standards and technologies, a growing amount of semantic content has been published on the Web as Linked Open Data (LOD). The LOD cloud has made available a large volume of structured data in a range of domains via liberal licenses. The semantic content of LOD in conjunction with the advanced searching and querying mechanisms provided by SPARQL has opened up unprecedented opportunities not only for enhancing existing applications, but also for developing new and innovative semantic applications. However, SPARQL is inadequate to deal with functionalities such as comparing, prioritizing, and ranking search results which are fundamental to applications such as recommendation provision, matchmaking, social network analysis, visualization, and data clustering. This paper addresses this problem by developing a systematic measurement model of semantic similarity between resources in Linked Data. By drawing extensively on a feature-based definition of Linked Data, it proposes a generalized information content-based approach that improves on previous methods which are typically restricted to specific knowledge representation models and less relevant in the context of Linked Data. It is validated and evaluated for measuring item similarity in recommender systems. The experimental evaluation of the proposed measure shows that our approach can outperform comparable recommender systems that use conventional similarity measures.
Ranking of universities represents a complex endeavor which involves gathering, weighting, and an... more Ranking of universities represents a complex endeavor which involves gathering, weighting, and analyzing diverse data. Emerging semantic technologies enable the Web of Data, a giant graph of interconnected information resources, also known as Linked Data. A recent community effort, Linking Open Data project, offers the possibility of accessing a large number of semantically described and linked concepts in various domains. In this paper, we propose a novel approach to take advantage of this structured data in the domain of universities to develop proxy measures of their relative standing for ranking purposes. Derived from information theory, our approach of computing the Information Content for universities and ranking them based on these scores achieved results comparable to the international ranking systems such as Shanghai Jiao Tong University, Times Higher Education, and QS. The metric we developed can also be used for innovative semantic applications in a range of domains for entity ranking, information filtering, and multi-faceted browsing.
Lecture Notes in Computer Science, 2013
International Conference on Information and Knowledge Management, Proceedings, 2012
Ranking of universities represents a complex endeavor which involves gathering, weighting, and an... more Ranking of universities represents a complex endeavor which involves gathering, weighting, and analyzing diverse data. Emerging semantic technologies enable the Web of Data, a giant graph of interconnected information resources, also known as Linked Data. A recent community effort, Linking Open Data project, offers the possibility of accessing a large number of semantically described and linked concepts in various domains. In this paper, we propose a novel approach to take advantage of this structured data in the domain of universities to develop proxy measures of their relative standing for ranking purposes. Derived from information theory, our approach of computing the Information Content for universities and ranking them based on these scores achieved results comparable to the international ranking systems such as Shanghai Jiao Tong University, Times Higher Education, and QS. The metric we developed can also be used for innovative semantic applications in a range of domains for entity ranking, information filtering, and multi-faceted browsing.
2009 2nd IEEE International Conference on Computer Science and Information Technology, 2009
Abstract Electronic Marketplaces (e-marketplaces, EMs) are one of the most important components o... more Abstract Electronic Marketplaces (e-marketplaces, EMs) are one of the most important components of electronic commerce (EC). They allow the exchange of goods or services between buyers and sellers for money or other products and services. EMs also play a ...
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Papers by Rouzbeh Meymandpour