A study of similar question retrieval method in online health communities

Purpose The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing. Design/methodology/approach This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics. Findings The experiment results show that the proposed method outperforms the baseline methods. Originality/value This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.

Organizing Tagged Knowledge: Similarity Measures and Semantic Fluency in Structure Mining

Recovering a system’s underlying structure from its historical records (also called structure mining) is essential to making valid inferences about that system’s behavior. For example, making reliable predictions about system failures based on maintenance work order data requires determining how concepts described within the work order are related. Obtaining such structural information is challenging, requiring system understanding, synthesis, and representation design. This is often either too difficult or too time consuming to produce. Consequently, a common approach to quickly elicit tacit structural knowledge from experts is to gather uncontrolled keywords as record labels—i.e., “tags.” One can then map those tags to concepts within the structure and quantitatively infer relationships between them. Existing models of tag similarity tend to either depend on correlation strength (e.g., overall co-occurrence frequencies) or on conditional strength (e.g., tag sequence probabilities). A key difficulty in applying either model is understanding under what conditions one is better than the other for overall structure recovery. In this paper, we investigate the core assumptions and implications of these two classes of similarity measures on structure recovery tasks. Then, using lessons from this characterization, we borrow from recent psychology literature on semantic fluency tasks to construct a tag similarity measure that emulates how humans recall tags from memory. We show through empirical testing that this method combines strengths of both common modeling paradigms. We also demonstrate its potential as a preprocessor for structure mining tasks via a case study in semi-supervised learning on real excavator maintenance work orders.

Trust , Knowledge Similarity and Cooperation in Coopetitive Strategic Alliances

Cooperative relationships between rival firms, which engage in interfirm strategic alliances, assume active transfer, reception and recombination of competencies and co-production of novel knowledge. These processes facilitate joint technological development and improve the participating firms’ competitiveness. Nevertheless, competitive interactions between the partners can impede the achievement of the alliance’s cooperative objectives. This paper investigates the impact of trusting attitudes developed between the partners on the effectiveness of interfirm collaboration under conditions of competitive rivalry in the broader industrial environment and similarity of initial knowledge at the partners’ disposal. It offers a number of hypotheses, which determine the interrelationships between the levels of interfirm trust, transaction costs associated with transferring knowledge and the successful attainment of alliance goals. The paper pays specific attention to the effects arising from the commonality of knowledge possessed by cooperating rivals. It also analyzes the influence of particular types of coopetitive strategic alliances on the success of collaborative arrangements under the conditions of knowledge commonality among the partners.

Effect of Product Involvement on Panels’ Vocabulary Generation, Attribute Identification, and Sample Configurations in Beer

The aim of this study was to compare the performance of two semi-trained panels with different degrees of self-reported beer involvement in terms of beer consumption pattern. The two panels were beer non-drinkers (indicating willingness to taste beer) and craft-style beer drinkers. Eleven modified beer samples were evaluated during three separate tasks by both panels. The tasks were (1) a vocabulary generation on a sample level, (2) an attribute identification task with a list of attributes to choose from, and (3) a descriptive analysis. The performance of the two panels was evaluated and compared using three parameters, as follows: Descriptive similarity, attribute knowledge similarity, and perceptual similarity. The results showed that the craft-style beer drinkers generated the most precise vocabulary and correctly identified more attributes, compared to the beer non-drinkers. Furthermore, the sample sensory spaces generated by the two panels were different before the training period, but were perceptually similar post training. To conclude, the beer consumption pattern influenced all aspects of panel performance before training, with the craft-style panel performing better than the non-drinkers panel. However, the panels’ performance became more similar after a short period of training sessions.