Search (9 results, page 1 of 1)

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Abstract

With the advent of various services and applications of Semantic Web, semantic annotation has emerged as an important research topic. The application of semantically annotated ontology had been evident in numerous information processing and retrieval tasks. One of such tasks is utilizing the semantically annotated ontology in product design which is able to suggest many important applications that are critical to aid various design related tasks. However, ontology development in design engineering remains a time consuming and tedious task that demands considerable human efforts. In the context of product family design, management of different product information that features efficient indexing, update, navigation, search and retrieval across product families is both desirable and challenging. For instance, an efficient way of retrieving timely information on product family can be useful for tasks such as product family redesign and new product variant derivation when requirements change. However, the current research and application of information search and navigation in product family is mostly limited to its structural aspect which is insufficient to handle advanced information search especially when the query targets at multiple aspects of a product. This paper attempts to address this problem by proposing an information search and retrieval framework based on the semantically annotated multi-facet product family ontology. Particularly, we propose a document profile (DP) model to suggest semantic tags for annotation purpose. Using a case study of digital camera families, we illustrate how the faceted search and retrieval of product information can be accomplished. We also exemplify how we can derive new product variants based on the designer's query of requirements via the faceted search and retrieval of product family information. Lastly, in order to highlight the value of our current work, we briefly discuss some further research and applications in design decision support, e.g. commonality analysis and variety comparison, based on the semantically annotated multi-facet product family ontology.

Source

Information processing and management. 46(2010) no.4, S.479-493

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Abstract

The number of digital immigrants using new technologies such as smartphones is rapidly increasing. However, digital immigrants still struggle to actually use and benefit from digital technology. This article examines the role of family support in digital immigrants' use of more smartphone functions based on information technology (IT) identity theory. We surveyed 241 digital immigrants who owned smartphones and used structural equation modeling (PLS-SEM) for analysis. We examined the contributing roles of family support for digital immigrants' IT identity and extended use behavior. Family cognitive and emotional support can shape IT identity by improving the smartphone-related experience. Family support has a positive impact on digital immigrants' self-efficacy, embeddedness, perceived usefulness, and perceived enjoyment of using a smartphone. Positive usage experience can also facilitate the establishment of IT identity, which is a key predictor of smartphone use behavior. A strong IT identity also promotes extended use behavior. We discuss the contributions and implications of our findings.

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Source

Information processing and management. 48(2012) no.4, S.791-801

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Abstract

Name ambiguity in the context of bibliographic citation affects the quality of services in digital libraries. Previous methods are not widely applied in practice because of their high computational complexity and their strong dependency on excessive attributes, such as institutional affiliation, research area, address, etc., which are difficult to obtain in practice. To solve this problem, we propose a novel coarse-to-fine framework for name disambiguation which sequentially employs 3 common and easily accessible attributes (i.e., coauthor name, article title, and publication venue). Our proposed framework is based on multiple clustering and consists of 3 steps: (a) clustering articles by coauthorship and obtaining rough clusters, that is fragments; (b) clustering fragments obtained in step 1 by title information and getting bigger fragments; (c) and clustering fragments obtained in step 2 by the latent relations among venues. Experimental results on a Digital Bibliography and Library Project (DBLP) data set show that our method outperforms the existing state-of-the-art methods by 2.4% to 22.7% on the average pairwise F1 score and is 10 to 100 times faster in terms of execution time.

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Abstract

Two forms of diffusion are studied: diffusion by publications, originating from the fact that a group publishes in different fields; and diffusion by citations, originating from the fact that the group's publications are cited in different fields. The first form of diffusion originates from an internal mechanism by which the group itself expands its own borders. The second form is partly driven by an external mechanism, in the sense that other fields use or become interested in the original group's expertise, and partly by the group's internal dynamism, in the sense that their articles, being published in more and more fields, have the potential to be applied in these other fields. In this contribution, we focus on basic counting measures as measures of diffusion. We introduce the notions of field diffusion breadth, defined as the number of for Essential Science Indicators (ESI) fields in which a set of articles is cited, and field diffusion intensity, defined as the number of citing articles in one particular ESI field. Combined effects of publications and citations can be measured by the Gini evenness measure. Our approach is illustrated by a study of mathematics at Tongji University (Shanghai, China).

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Abstract

With more and more information available on the Internet, the task of making personalized recommendations to assist the user's navigation has become increasingly important. Considering there might be millions of users with different backgrounds accessing a Web site everyday, it is infeasible to build a separate recommendation system for each user. To address this problem, clustering techniques can first be employed to discover user groups. Then, user navigation patterns for each group can be discovered, to allow the adaptation of a Web site to the interest of each individual group. In this paper, we propose to model user access sequences as stochastic processes, and a mixture of Markov models based approach is taken to cluster users and to capture the sequential relationships inherent in user access histories. Several important issues that arise in constructing the Markov models are also addressed. The first issue lies in the complexity of the mixture of Markov models. To improve the efficiency of building/maintaining the mixture of Markov models, we develop a lightweight adapt-ive algorithm to update the model parameters without recomputing model parameters from scratch. The second issue concerns the proper selection of training data for building the mixture of Markov models. We investigate two different training data selection strategies and perform extensive experiments to compare their effectiveness on a real dataset that is generated by a Web-based knowledge management system, Livelink.

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Abstract

The scale of knowledge is growing rapidly in the big data environment, and traditional knowledge organization and services have faced the dilemma of semantic inaccuracy and untimeliness. From a knowledge fusion perspective-combining the precise semantic superiority of traditional ontology with the large-scale graph processing power and the predicate attribute expression ability of property graph-this paper presents an ontology and property graph fusion framework (OPGFF). The fusion process is divided into content layer fusion and constraint layer fusion. The result of the fusion, that is, the knowledge representation model is called knowledge big graph. In addition, this paper applies the knowledge big graph model to the ownership network in the China's financial field and builds a financial ownership knowledge big graph. Furthermore, this paper designs and implements six consistency inference algorithms for finding contradictory data and filling in missing data in the financial ownership knowledge big graph, five of which are completely domain agnostic. The correctness and validity of the algorithms have been experimentally verified with actual data. The fusion OPGFF framework and the implementation method of the knowledge big graph could provide technical reference for big data knowledge organization and services.

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Date

22. 6.1997 16:34:51

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Date

20. 1.2015 18:30:22