数字图书馆的多平台语义元数据生态(SMESE)：集成与互操作解决方案

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本文主要探讨了在软件行业日益复杂的多产品和多平台开发背景下，如何利用数字图书馆的语义元数据丰富技术构建一个高效、可扩展的软件生态系统——SMESE（Semantic Metadata Enrichment Software Ecosystem）。SMESE的提出是为了解决软件产品线工程（Software Product Line Engineering, SPLE）中的元数据管理和互操作性问题。在传统软件开发中，SPLE通过整合专有和开源组件实现应用的标准化和复用。然而，当前元数据在这样的整合过程中并未得到充分运用。为了改进这一状况，作者首先构建了一个以多平台元数据驱动的应用程序为核心的概念，即SMESE。这个生态系统旨在利用语义元数据的强大功能，提高数据的可理解和互操作性。 SMESE的设计包括九个关键子系统： 1. **元数据计划和一致性规则**：确保元数据策略的规划与实施，维持数据的一致性和有效性。 2. **网络元数据和数据收集**：从多个来源收集原始元数据，并将其纳入生态系统。 3. **授权元数据和数据收集**：处理版权和许可问题，确保合法的数据获取和使用。 4. **基于规则的语义外部充实**：通过规则引擎将外部源的非结构化数据转化为语义化的元数据。 5. **基于规则的语义内部充实**：利用业务规则对内部数据进行深度分析，增强其语义表示。 6. **同步外部和内部充实**：保持内外部元数据的一致性，防止数据冗余或不一致。 7. **基于用户兴趣的网关**：根据用户需求提供个性化内容，提升用户体验。 8. **语义主目录**：创建集中管理的语义目录，便于查找和导航。文章还介绍了SMESE如何通过本体映射模型来实现不同元数据模型（如Dublin Core、UNIMARC、MARC21、RDF/RDA和BIBFRAME）之间的互操作性，这有助于跨越数据孤岛，促进数据共享和协作。最后，文章提出了一个名为SPLE决策支持过程（SPLE-DSP）的决策支持流程，该流程与SMESE紧密结合，以支持动态重用和快速适应变化的需求。通过SMESE，数字图书馆和其他企业应用程序可以更加有效地利用和管理元数据，从而提高整个软件生态系统的效率和灵活性。这项研究对于推动软件工程领域尤其是数字图书馆领域的元数据管理具有重要意义，对未来软件生态系统的可持续发展具有积极的推动作用。

R. Brisebois et al.

376

literature review of SECO research, Manikas and Hansen [2] report that while

research on SECO is increasing:

1. There is little consensus on what constitutes a SECO.

2. Few analytical models of SECO exist.

3. Little research is done in the context of real-world SECO.

They define a SECO as the interaction of a set of actors on top of a common

technological platform that results in a number of software solutions or services

where each actor is motivated by a set of interests or business models while con-

nected to the rest of the actors. They also identify three main components of

SECO architecture:

1. SECO software engineering: focuses on technical issues related directly or in-

directly to the technological platform.

2. SECO business and management: focuses on the business, organizational and

management aspects.

3. SECO relationships: represent the social aspect of the architecture since it is

essential for SPLE actors to interact among themselves and with the platform.

2.3. SECO Architecture and SPLE

This section focuses on SECO architecture related to SPLE, beginning with an

industry perspective.

Christensen

et al.

[3] define the concept of SECO architecture as a set of

structures comprised of actors and software elements, the relationships among

them, and their properties. They present the Danish telemedicine SECO in terms

of this concept, and discuss challenges that are relevant in areas beyond teleme-

dicine. They also discuss how software engineering practice is affected by de-

scribing the creation and evolution of a central SECO architecture, namely

Net4Care, that serves as a reference architecture and learning vehicle for teleme-

dicine and for the actors within a single software organization.

Demir [34] also proposes a software architecture that is strongly related to a

defence system and limited to military personnel. Their multi-view SECO archi-

tecture design is described step by step. They begin by identifying the system

context, requirements, constraints, and quality expectations, but do not describe

the end products of the SECO architecture. They also introduce a novel archi-

tectural style, called “star-controller architectural style” [34] where synchroniza-

tion and control of the flow of information are handled by controllers. However,

a major drawback of this style is that failure of one controller disables all the

subcomponents attached to that controller.

Neves

et al.

[40] propose an architectural solution based on ontology and the

spreading algorithm that offers personalized and contextualized event recom-

mendations in the university domain. They use an ontology to define the do-

main knowledge model and the spreading activation algorithm to learn user

patterns through discovery of user interests. The main limitation of their archi-

tectural context-aware recommender system is that it is specific to university

populations and does not present the actual model of the system that shows the

interactions between the components and the data.

R. Brisebois et al.

377

Alferez

et al.

[45] propose a framework that uses semantically rich variability

models at runtime to support the dynamic adaptation of service compositions.

They argue that should problematic events occur, functional pieces may be add-

ed, removed, replaced, split or merged from a service composition at runtime,

hence delivering a new service composition configuration. Based on this argu-

ment, they propose that service compositions be abstracted as a set of features in

a variability model. They define a feature as a logical unit of behavior specified

by a set of functional and non-functional requirements. Thus, they propose

adaptation policies that describe the dynamic adaptation of a service composi-

tion in terms of the activation or deactivation of features in the causally con-

nected variability model. Unfortunately, this variability model is limited to acti-

vation and deactivation of services. Indeed, the model should allow adaptation of

services or include a service interoperability protocol (SIP) rather than composi-

tions only according to changes in the computing infrastructure.

In component based software development (CBSD), the fuzzy logic approach

[27] [28] is largely used to select components. Singh

et al.

[27] explored the var-

ious measures such as separation of concerns (SoC), coupling, cohesion, and size

measure that affect the reusability of aspect oriented software. The main draw-

back of their contribution is that the fuzzy logic rules are static. They do not

propose a way to improve the rules based on developer satisfaction of the fuzzy

inference system (FIS) output. In addition, their fuzzy inference system is li-

mited to reusability of software.

2.4. Semantic Metadata Enrichment (SME)

Bontcheva

et al.

[46] investigate semantic metadata automatic enrichment and

search methods. In particular, the benefits of enriching articles with knowledge

from linked open data resources are investigated with a focus on the environ-

mental science domain. They also propose a form-based semantic search inter-

face to facilitate environmental science researchers in carrying out better seman-

tic searches. Their proposed model is limited to linking terms with DBpedia URI

and does not take into account the semantic meaning of terms in order to detect

the best DBpedia URI.

Some authors focus their enrichment model on person mobility trace data

[47] [48] [49] [50]. Krueger

et al.

[47] show how semantic insights can be gained

by enriching trajectory data with place of interest (POI) information using social

media services. They handle semantic uncertainties in time and space, which re-

sult from noisy, imprecise, and missing data, by introducing a POI decision

model in combination with highly interactive visualizations. However, this

model is limited to POI detection.

Kunze and Hecht [48] propose an approach to processing semantic informa-

tion from user-generated OpenStreetMap (OSM) data that specifies non-resi-

dential use in residential buildings based on OSM attributes, so-called tags,

which are used to define the extent of non-residential use.

Our conclusions from these related works are:

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