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This article aims to explore how to incorporate similarity joins into XML database management systems (XDBMSs). The authors aim to provide seamless and efficient integration of similarity joins on tree-structured data into an XDBMS architecture.

The authors exploit XDBMS-specific features to efficiently generate XML tree representations for similarity matching. In particular, the authors push down a large part of the structural similarity evaluation close to the storage layer.

Empirical experiments were conducted to measure and compare accuracy, performance and scalability of the tree similarity join using different similarity functions and on the top of different storage models. The results show that the authors’ proposal delivers performance and scalability without hurting the accuracy.

Similarity join is a fundamental operation for data integration. Unfortunately, none of the XDBMS architectures proposed so far provides an efficient support for this operation. Evaluating similarity joins on XML is challenging, because it requires similarity matching on the text and structure. In this work, the authors integrate similarity joins into an XDBMS. To the best of the authors’ knowledge, this work is the first to leverage the storage scheme of an XDBMS to support XML similarity join processing.

The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure.

The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part.

The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain.

In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers.

The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

The objective of this paper is to propose a practical and operable method to identify and fill organisational knowledge gaps during new product development.

From a microscopic view, this paper introduces the tree-shaped organisational knowledge structure to formalise the knowledge gaps and their internal hierarchical relationships. Based on the organisational knowledge structure, organisational knowledge gaps are identified through tree matching algorithm. The tree-edit-distance method is introduced to calculate the similarity between two organisational knowledge structures for filling knowledge gap.

The proposed tree-shaped organisational knowledge structure can represent organisations' knowledge and their hierarchy relationships in a structured format, which is useful for identifying and filling organisational knowledge gaps.

The proposed concept of organisational knowledge structure can quantify organisational knowledge. The approach is valuable for strategic decisions regarding new product development. The organisational knowledge gaps identified with this method can provide real-time and accurate guidance for the product development path. More importantly, this method can accelerate the organisational knowledge gap filling process and promote organisational innovation.

The purpose of this paper is to introduce a new similarity method to gauge the differences between two subject hierarchical structures.

In the proposed similarity measure, nodes on two hierarchical structures are projected onto a two-dimensional space, respectively, and both structural similarity and subject similarity of nodes are considered in the similarity between the two hierarchical structures. The extent to which the structural similarity impacts on the similarity can be controlled by adjusting a parameter. An experiment was conducted to evaluate soundness of the measure. Eight experts whose research interests were information retrieval and information organization participated in the study. Results from the new measure were compared with results from the experts.

The evaluation shows strong correlations between the results from the new method and the results from the experts. It suggests that the similarity method achieved satisfactory results.

Hierarchical structures that are found in subject directories, taxonomies, classification systems, and other classificatory structures play an extremely important role in information organization and information representation. Measuring the similarity between two subject hierarchical structures allows an accurate overarching understanding of the degree to which the two hierarchical structures are similar.

Both structural similarity and subject similarity of nodes were considered in the proposed similarity method, and the extent to which the structural similarity impacts on the similarity can be adjusted. In addition, a new evaluation method for a hierarchical structure similarity was presented.

The mere presence of categories, irrespective of their content, positively influences the satisfaction of choosers who are unfamiliar with the choice domains. In the present research the main goal is to analyze how a complex product category is categorized internally by consumers, with and without price information available, and explore the effect of product involvement and category knowledge on such structures within the wine category.

Additive tree analysis allows us to visualize the perceptual structure of complex sets of alternatives and the multinomial logit model enables us to analyze the influence of these inherent personal characteristics.

The results show that consumers organize information on red wines and rosés according to a “type→origin” cognitive construct and use “price”, if available, as a third attribute. Consumers comparing red wines and white wines organize the available information according to a “type” construct in which “price”, even if available, plays no role. Subjects with a greater knowledge of the category exhibit more complex structures.

A broader database would be necessary to draw further conclusions with respect to the specific category of wine. Another possible limitation may arise from the chosen set of alternatives. There are more red wines among the stimuli for the survey. As a result of this numerical imbalance between the red wines on the one hand and the rosé/white wines on the other, the red are perceived to be more similar to each other. It would therefore be useful in future research to try to obtain the same number of alternatives for each attribute level. Meanwhile, research on other product categories would be useful to provide further validation of these findings.

The main implication of the authors' findings for retailers is that an understanding of the internal categorization structures underlying consumers' product similarity judgments will enable them to organize their shelf space layout to match the way it is processed by consumers. Furthermore, the findings suggest that high‐knowledge consumers may be selectively targeted by using store layouts arranged on the basis of complex structures, while low‐knowledge consumers may be selectively targeted using store layouts organized on the basis of simple structures.

The main contribution of this paper to the existing literature on perceptual organization is an analysis of the influence of price on respondents' internal categorization structures, when price is considered a key variable in the formation of consumers' impressions of a product. Furthermore, in the novel context of the wine category, the authors describe the moderating effect of two variables, involvement and knowledge, on the results of previous literature on perceptual organization. These two variables have potential as segmentation criteria to enable category managers to tailor their products to target markets. Secondly, though no less importantly, the authors accompany the qualitative additive tree methodology used to derive the perceived structures with an analysis of variance to achieve a more objective interpretation of the additive trees.

This paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.

First, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.

Folksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.

The proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

This paper describes the simulation of a nearest neighbour searching algorithm for document retrieval using a pool of microprocessors. The documents in a database are organised in a multi‐dimensional binary search tree, and the algorithm identifies the nearest neighbour for a query by a backtracking search of this tree. Three techniques are described which allow parallel searching of the tree. A PASCAL‐based, general purpose simulation system is used to simulate these techniques, using a pool of Transputer‐like microprocessors with three standard document test collections. The degree of speed‐up and processor utilisation obtained is shown to be strongly dependent upon the characteristics of the documents and queries used. The results support the use of pooled microprocessor systems for searching applications in information retrieval.

We have proposed and implemented AgentMatcher, an architecture for match‐making in e‐Business applications. It uses arc‐labeled and arc‐weighted trees to match buyers and sellers via our novel similarity algorithm. This paper adapts the architecture for match‐making between learners and learning objects (LOs). It uses the Canadian Learning Object Metadata (CanLOM) repository of the eduSource e‐Learning project. Through AgentMatcher’s new indexing component, known as Learning Object Metadata Generator (LOMGen), metadata is extracted from HTML LOs for use in CanLOM. LOMGen semi‐automatically generates the LO metadata by combining a word frequency count and dictionary lookup. A subset of these metadata terms can be selected from a query interface, which permits adjustment of weights that express user preferences. Web‐based pre‐filtering is then performed over the CanLOM metadata kept in a relational database. Using an XSLT (Extensible Stylesheet Language Transformations) translator, the pre‐filtered result is transformed into an XML representation, called Weighted Object‐Oriented (WOO) RuleML (Rule Markup Language). This is compared to the WOO RuleML representation obtained from the query interface by AgentMatcher’s core Similarity Engine. The final result is presented as a ranked LO list with a user‐specified threshold.

Multidimensional scaling and cluster analysis techniques are commonly employed for the analysis of consumer perceptions of products. However, within the past 10‐15 years, a growing volume of research has shown that the processes underlying similarities judgements of stimuli are incompatible with the fundamental underlying axioms of these techniques. A series of papers in the psychometrics and cognitive psychology literatures by Tversky and his associates have demonstrated the inability of these procedures to handle similarities data from many domains by virtue of the restrictive assumptions they impose on the data. Recently, several procedures have been proposed that overcome the limitations of traditional multidimensional scaling and cluster analysis techniques. The potential benefits are illustrated of applying two of these newer techniques, additive similarity trees (ADDTREE) and extended similarity trees (EXTREE) in the context of marketing research. Consumers′ similarity judgements data are presented from three disparate product domains (newspapers, shops and breakfast cereals). In each case, non‐metric multidimensional scaling and average linkage cluster analysis yield less interpretable solutions than ADDTREE. In the case of the newspapers data, much richer insights are obtained with reference to EXTREE. The paper concludes with a discussion of the implications for market research studies and the development of consumer behaviour theory.

Perceptual maps and trees are widely used for business applications like advertising development, product design and product positioning. Maps and trees are however intrinsically different in terms of how well they can represent consumer perceptions of product‐market structure. Draws on recent advances in the academic literature to evaluate the relative strengths and weaknesses of maps and trees. This theoretical evaluation is used to develop a grid‐based framework which is then used to provide guidelines to managers about what kind of visual representation to use, and what type of input data to collect, for some different real‐life marketing tasks. This conceptual framework is also used to indicate directions for future academic research in the area of visual representation models.