A fuzzy tree similarity based recommendation approach for telecom products

Due to the huge product assortments and complex descriptions of telecom products, it is a great challenge for customers to select appropriate products. A fuzzy tree similarity based hybrid recommendation approach is proposed to solve this issue. In this study, fuzzy techniques are used to deal with the various uncertainties existing within the product and customer data. A fuzzy tree similarity measure is developed to evaluate the semantic similarity between tree structured products or user profiles. The similarity measures for items and users both integrate the collaborative filtering (CF) and semantic similarities. The final recommendation hybridizes item-based and user-based CF recommendation techniques. A telecom product recommendation case study is given to show the effectiveness of the proposed approach. © 2013 IEEE

A similarity measure on tree structured business data

In many business situations, products or user profile data are so complex that they need to be described by use of tree structures. Evaluating the similarity between tree-structured data is essential in many applications, such as recommender systems. To evaluate the similarity between two trees, concept corresponding nodes should be identified by constructing an edit distance mapping between them. Sometimes, the intension of one concept includes the intensions of several other concepts. In that situation, a one-to-many mapping should be constructed from the point of view of structures. This paper proposes a tree similarity measure model that can construct this kind of mapping. The similarity measure model takes into account all the information on nodes&rsquo; concepts, weights, and values. The conceptual similarity and the value similarity between two trees are evaluated based on the constructed mapping, and the final similarity measure is assessed as a weighted sum of their conceptual and value similarities. The effectiveness of the proposed similarity measure model is shown by an illustrative example and is also demonstrated by applying it into a recommender system.<br /

Generating Tensor Representation from Concept Tree in Meaning Based Search

Meaning based search retrieves objects from search index repository based on user's search Meanings and meaning of objects rather than keyword matching. It requires techniques to capture user's search Meanings and meanings of objects, transform them to a representation that can be stored and compared efficiently on computers. Meaning of objects can be adequately captured in terms of a hierarchical composition structure called concept tree. This thesis describes the design and development of an algorithm that transforms the hierarchical concept tree to a tensor representation using tensor algebra theory. These tensor representations can capture the information need of a user in a better way and can be used for similarity comparisons in meaning based search. A preliminary evaluation showed that the proposed framework outperforms the TF-IDF vector model in 95% of the cases and vector based conceptual search model in 92% of the cases in adequately comparing meaning of objects. The tensor conversion tool also was used to verify the salient properties of the meaning comparison framework. The results show that the salient properties are consistent with the tensor similarity values of the meaning comparison framework

Ontological View-driven Semantic Integration in Open Environments

In an open computing environment, such as the World Wide Web or an enterprise Intranet, various information systems are expected to work together to support information exchange, processing, and integration. However, information systems are usually built by different people, at different times, to fulfil different requirements and goals. Consequently, in the absence of an architectural framework for information integration geared toward semantic integration, there are widely varying viewpoints and assumptions regarding what is essentially the same subject. Therefore, communication among the components supporting various applications is not possible without at least some translation. This problem, however, is much more than a simple agreement on tags or mappings between roughly equivalent sets of tags in related standards. Industry-wide initiatives and academic studies have shown that complex representation issues can arise. To deal with these issues, a deep understanding and appropriate treatment of semantic integration is needed. Ontology is an important and widely accepted approach for semantic integration. However, usually there are no explicit ontologies with information systems. Rather, the associated semantics are implied within the supporting information model. It reflects a specific view of the conceptualization that is implicitly defining an ontological view. This research proposes to adopt ontological views to facilitate semantic integration for information systems in open environments. It proposes a theoretical foundation of ontological views, practical assumptions, and related solutions for research issues. The proposed solutions mainly focus on three aspects: the architecture of a semantic integration enabled environment, ontological view modeling and representation, and semantic equivalence relationship discovery. The solutions are applied to the collaborative intelligence project for the collaborative promotion / advertisement domain. Various quality aspects of the solutions are evaluated and future directions of the research are discussed

A Tree Similarity Measuring Method and its Application to Ontology Comparison

Classical tree similarity measuring approaches focus on the structural and geometrical characteristics of the trees. The degree of similarity between two trees is measured by the minimal cost of editing sequences that convert one tree into the other one from pure structural perspective. Differently, when the trees are created to represent concept structures in a knowledge context (known as concept trees), the tree nodes represent concepts, not merely abstract elements occupying specific positions. Therefore, measuring similarity of such trees requires a more comprehensive method which takes the position, significance of the concepts (represented by the tree nodes), and conceptual similarity among the concepts from different trees into consideration. This paper extends the classical tree similarity measuring method to introduce tree transformation operations which transform one concept tree to another one. We propose definitions for the costs of the operations based on the position, importance of each concept within a concept structure, and similarity between individual concepts from different concept structures in a knowledge context. The method for computing the transformation costs and measuring similarity between different trees is presented. We apply the proposed method to ontology comparison where different ontologies for the same domain are represented as trees and their similarity is required to be measured. We show that the proposed method can facilitate the initiation of ontology integration and ontology trust evaluation