Weichang Du

A knowledge encapsulation approach to ontology modularization

The development of monolithic ontologies for complex domains may face various challenges in reasoning and implementation. The notion of modularity can be employed for developing more efficient ontologies, especially in distributed environments. In this paper, we introduce a framework for developing ontologies in a modular manner. We describe the interface-based modular ontology formalism, (IBF), which theoretically supports the framework. The main feature of the framework is its support for knowledge encapsulation, i.e., it allows ontologies to define their main content using well-defined interfaces, such that their knowledge bases can only be accessed by other ontologies through these interfaces. An important implication of the proposed framework is that ontology modules can be developed completely independent of each other’s signature and languages. Such modules are free to only utilize the required knowledge segments of the others. We also investigate the issues of inconsistency in the proposed modular ontology framework. We provide solutions for isolating inconsistent ontology modules from the other parts of a modular ontology and also resolve inconsistencies which may be arisen by integrating consistent knowledge bases.

Semantics-Enabled User Interest Detection from Twitter

Social networks enable users to freely communicate with each other and share their recent news, ongoing activities or views about different topics. As a result, user interest detection from social networks has been the subject of increasing attention. Some recent works have proposed to enrich social posts by annotating them with unambiguous relevant ontological concepts extracted from external knowledge bases and model user interests as a bag of concepts. However, in the bag of concepts approach, each topic of interest is represented as an individual concept that is already predefined in the knowledge base. Therefore, it is not possible to infer fine-grained topics of interest, which are only expressible through a collection of multiple concepts or emerging topics, which are not yet defined in the knowledge base. To address these issues, we view each topic of interest as a conjunction of several concepts, which are temporally correlated on Twitter. Based on this, we extract active topics within a given time interval and determine a users inclination towards these active topics. We demonstrate the effectiveness of our approach in the context of a personalized news recommendation system. We show through extensive experimentation that our work is able to improve the state of the art.

A semantic metrics suite for evaluating modular ontologies

Ontologies, which are formal representations of knowledge within a domain, can be used for designing and sharing conceptual models of enterprises information for the purpose of enhancing understanding, communication and interoperability. For representing a body of knowledge, different ontologies may be designed. Recently, designing ontologies in a modular manner has emerged for achieving better reasoning performance, more efficient ontology management and change handling. One of the important challenges in the employment of ontologies and modular ontologies in modeling information within enterprises is the evaluation of the suitability of an ontology for a domain and the performance of inference operations over it. In this paper, we present a set of semantic metrics for evaluating ontologies and modular ontologies. These metrics measure cohesion and coupling of ontologies, which are two important notions in the process of assessing ontologies for enterprise modeling. The proposed metrics are based on semantic-based definitions of relativeness, and dependencies between local symbols, and also between local and external symbols of ontologies. Based on these semantic definitions, not only the explicitly asserted knowledge in ontologies but also the implied knowledge, which is derived through inference, is considered for the sake of ontology assessment. We present several empirical case studies for investigating the correlation between the proposed metrics and reasoning performance, which is an important issue in applicability of employing ontologies in real-world information systems.

A Multi-Agent Framework for Ambient Systems Development

This paper proposes a multi-agent framework to support applications development of ambient systems. In the programming model of the framework, ambient systems can be developed by collaboration of mobile agents and service (or resident) agents, where resident agents provide application services on devices and mobile agents provide communication services on behalf of owner applications. On top of device infrastructure, the architecture of the framework consists of three layers: framework service layer, software agents layer and application layer, to fully support dynamic and collaborative tasks of ambient systems.

Dynamic Software Product Line Engineering: A Reference Framework

Runtime adaptive systems are able to dynamically transform their internal structure, and hence their behavior, in response to internal or external changes. Such transformations provide the basis for new functionalities or improvements of the non-functional properties that match operational requirements and standards. Software Product Line Engineering (SPLE) has introduced several models and mechanisms for variability modeling and management. Dynamic software product lines (DSPL) engineering exploits the knowledge acquired in SPLE to develop systems that can be context-aware, post-deployment reconfigurable, or runtime adaptive. This paper focuses on DSPL engineering approaches for developing runtime adaptive systems and proposes a framework for classifying and comparing these approaches from two distinct perspectives: adaptation properties and adaptation realization. These two perspectives are linked together by a series of guidelines that help to select a suitable adaptation realization approach based on desired adaptation types.

Formalizing the Role of Goals in the Development of Domain-Specific Ontological Frameworks

In this paper we propose a high-level scheme that assists ontology engineers develop appropriate ontological frameworks. By ontological frameworks we mean those structures that specify particular phases and also provide implemented components for developing ontologies. Based on the i* conceptual modeling framework, our proposed scheme guides ontology engineers by customizing a suitable ontological framework based on their preferences and their specific domain necessities. In the proposed scheme, We specify the users of an ontological framework, their high-level softgoals as well as the goals that contribute to these softgoals. We exploit business processes and bind them to the goals in order to implement the framework.

Aspects of inconsistency resolution in modular ontologies

Modularization entails more efficient reasoning and better performance in the ontology manipulation process. Therefore, the development of modular ontologies has recently received much attention. One of the most important issues in modular ontologies is dealing with inconsistencies. An inconsistent module may affect the other modules and cause a modular ontology to become inconsistent. Furthermore, the integration of different consistent modules may also result in inconsistency. In this paper, we investigate various types of inconsistencies in modular ontologies. We mostly focus on an interface-based ontology modularity formalism and propose a strategy and an algorithm for isolating inconsistent modules and resolving inconsistencies arisen from the integration of different ontology modules.

Finding Diachronic Like‐Minded Users

User communities in social networks are usually identified by considering explicit structural social connections between users. While such communities can reveal important information about their members such as family or friendship ties and geographical proximity, just to name a few, they do not necessarily succeed at pulling like‐minded users that share the same interests together. Therefore, researchers have explored the topical similarity of social content to build like‐minded communities of users. In this article, following the topic‐based approaches, we are interested in identifying communities of users that share similar topical interests with similar temporal behavior. More specifically, we tackle the problem of identifying temporal (diachronic) topic‐based communities, i.e., communities of users who have a similar temporal inclination toward emerging topics. To do so, we utilize multivariate time series analysis to model the contributions of each user toward emerging topics. Further, our modeling is completely agnostic to the underlying topic detection method. We extract topics of interest by employing seminal topic detection methods; one graph‐based and two latent Dirichlet allocation‐based methods. Through our experiments on Twitter data, we demonstrate the effectiveness of our proposed temporal topic‐based community detection method in the context of news recommendation, user prediction, and document timestamp prediction applications, compared with the nontemporal as well as the state‐of‐the‐art temporal approaches.

A Modular Approach to Scalable Ontology Development

The increasing desire for applying semantic web techniques for describing large and complex domains demanded scalable methods for developing ontologies. Modularity is an emerging approach for developing ontologies that leads to more scalable development process and better reasoning performance. In this chapter, we describe the interface-based modular ontology formalism and its capabilities for developing scalable ontologies.We present an extension to OWL-DL as well as tool support for creating scalable ontologies through the formalism. Furthermore, we introduce a set of metrics for evaluating modular ontologies and argue how these metrics can be applied for analyzing the scalability and reasoning performance of ontologies. We investigate a number of case studies from real-world ontologies, redesign them based on the interface-based modular formalism and analyze them through the introduced metrics.

Automated Composition of Service Mashups Through Software Product Line Engineering

The growing number of online resources, including data and services, has motivated both researchers and practitioners to provide methods and tools for non-expert end-users to create desirable applications by putting these resources together leading to the so called mashups. In this paper, we focus on a class of mashups referred to as service mashups. A service mashup is built from existing services such that the developed service mashup offers added-value through new functionalities. We propose an approach which adopts concepts from software product line engineering and automated AI planning to support the automated composition of service mashups. One of the advantages of our work is that it allows non-experts to build and optimize desired mashups with little knowledge of service composition. We report on the results of the experimentation that we have performed which support the practicality and scalability of our proposed work.