Buhwan Jeong

A novel method for measuring semantic similarity for XML schema matching

Enterprises integration has recently gained great attentions, as never before. The paper deals with an essential activity enabling seamless enterprises integration, that is, a similarity-based schema matching. To this end, we present a supervised approach to measure semantic similarity between XML schema documents, and, more importantly, address a novel approach to augment reliably labeled training data from a given few labeled samples in a semi-supervised manner. Experimental results reveal the proposed method is very cost-efficient and reliably predicts semantic similarity.

Improving memory-based collaborative filtering via similarity updating and prediction modulation

Memory-based collaborative filtering (CF) makes recommendations based on a collection of user preferences for items. The idea underlying this approach is that the interests of an active user will more likely coincide with those of users who share similar preferences to the active user. Hence, the choice and computation of a similarity measure between users is critical to rating items. This work proposes a similarity update method that uses an iterative message passing procedure. Additionally, this work deals with a drawback of using the popular mean absolute error (MAE) for performance evaluation, namely that ignores ratings distribution. A novel modulation method and an accuracy metric are presented in order to minimize the predictive accuracy error and to evenly distribute predicted ratings over true rating scales. Preliminary results show that the proposed similarity update and prediction modulation techniques significantly improve the predicted rankings.

On the functional quality of service (FQoS) to discover and compose interoperable web services

Despite its prevalence, the service-oriented architecture (SOA) still has an imperative challenge to achieve interoperability within and across enterprise applications. Minimal conditions for interoperability are: (1) to discover and plug in proper services for integration and (2) to support for seamless data exchanges between component services. A similarity-based approximate matching is a practical approach for both, in that the service discovery relies on functional matches between a query and service descriptions, and the seamless data exchange is granted by mapping information from a service to others. To these ends, this paper comprehensively investigates functional attributes of web services and their manipulation, and particularly highlights an information compatibility and mapping analysis. The functional quality of service (FQoS) allows service discovery and selection to step forward. Simulation results show that the present FQoS metrics are effective in service discovery.

An iterative semi-explicit rating method for building collaborative recommender systems

Collaborative filtering plays the key role in recent recommender systems. It uses a user-item preference matrix rated either explicitly (i.e., explicit rating) or implicitly (i.e., implicit feedback). Despite the explicit rating captures the preferences better, it often results in a severely sparse matrix. The paper presents a novel iterative semi-explicit rating method that extrapolates unrated elements in a semi-supervised manner. Extrapolation is simply an aggregation of neighbor ratings, and iterative extrapolations result in a dense preference matrix. Preliminary simulation results show that the recommendation using the semi-explicit rating data outperforms that of using the pure explicit data only.

Discovering and integrating distributed manufacturing services with semantic manufacturing capability profiles

Integrating distributed manufacturing systems is a longstanding dream of industrial engineers. The advent of internet technologies has provided opportunities to fulfill this dream, and has presented new challenges to overcome. Since most current internet technologies (e.g. SOA, web service) originated in business applications, it is difficult to apply them directly to manufacturing systems. The difficulties stem particularly from differences in meaning and usage of manufacturing terms and an inability to express semantic information about manufacturing services. The present paper aims to extend the universal description, discovery, and integration (UDDI) registry specification to include semantic descriptions about manufacturing services and to support reasoning of those descriptions for service discovery. Specifically, we provide OWL-based definitions for manufacturing service capability profiles and a description logic (DL)-based reasoning procedure for matching queries to service descriptions. An illustrative process is presented with a prototype implementation for a discrete part manufacturing case.

User credit-based collaborative filtering

Memory-based collaborative filtering is the state-of-the-art method in recommender systems and has proven to be successful in various applications. In this paper we develop novel memory-based methods that incorporate the level of a user credit instead of using similarity between users. The user credit is the degree of ones rating reliability that measures how adherently the user rates items as others do. Preliminary simulation results show that the proposed methods outperform the conventional memory-based ones. The methods are effective in a cold-starting problem.

A Multi-Criteria Web Services Composition Problem

With its prevalence in enterprise applications integration, the service-oriented approach has been studied in various ways. The popularity, however, results in a number of different standards and implementations. The approach needs agreed-upon definitions and assumptions. To this end, the paper presents a multi-criteria service composition problem and positions to state service engineering functions associated with the problem in a formal form. The paper also introduces an information compatibility concept to measure the degree of interoperable data exchanges between services.

A kernel method for measuring structural similarity between XML documents

Measuring structural similarity between XML documents has become a key component in various applications, including XML data mining, schema matching, web service discovery, among others. The paper presents a novel structural similarity measure between XML documents using kernel methods. Results on preliminary simulations show that this outperforms conventional ones.

JESS-based web interface for XML document validation

Ensuring consistency among data exchange specifications in XML is critical to seamless integration of various business-to-business (B2B) applications. To this end, a specification should be thoroughly verified in manifold perspectives such as grammar/syntax conformance, design compliance, and canonical semantics accordance. A single hand-woven testbed implementation will fail to versatilely support such a variety of testings. In this paper, we propose a novel validation tool of XML documents using an expert system and open validation rule scripts. Particularly, we illustrate a web-based implementation to ensure an XML schemas design compliance to design and naming rules (NDR), namely a quality of schema design (QoD) test.

Support for seamless data exchanges between web services through information mapping analysis using kernel methods

A challenging issue to web services interoperability is seamless data exchanges between web services to be composed. A solution to this problem is to establish semantic mappings from an information item to another. To do that, we present an approximate information mapping analysis. We propose a kernel-based structural similarity measure for XML documents. Simulation results with industrial XML data show that the proposed kernel-based measure outperforms other existing methods.