As the result of a siloed approach, each group may have slightly different semantic integration and data processing logic.
由于采用了竖井工作方法，每个组的语义集成和数据处理逻辑可能略有不同。
Semantic mismatches and incompatible data formats are a staple of data integration and are not likely to vanish.
语义不匹配和不兼容数据格式是数据集成的主要问题来源，不太可能消失。
The answer, according to PwC, is Semantic Web techniques. PwC believes that the Semantic Web offers a practical way to address the problem of large-scale data integration.
根据普华永道的报告，问题的答案是语义网技术，普华永道相信，语义网为解决大规模的数据整合问题提供了一种实际可行的方法。
The Semantic Web will enable better data integration by allowing everyone who puts individual items of data on the Web to link them with other pieces of data using standard formats.
语义网将带来更好的数据集成，它允许每个人把自己的数据按照标准的数据格式和任何其它人的数据进行链接。
PwC lists a number of Semantic technology vendors that focus on enterprise data integration techniques.
普华永道罗列出了许多致力于企业数据整合技术的语义技术提供商。
There are three major information integration patterns to achieve semantic interoperability: data federation, data consolidation and Enterprise Application Integration (EAI).
有三种主要的信息集成模式可用于实现语义互操作性：数据联合、数据整合和企业应用集成（EAI）。
Semantic Web services (SWS) can be considered an integration layer on top of Web services; they use ontologies as data model and they have a rich conceptual model.
语义Web服务(SWS)可看作是Web服务之上的集成层；它们使用本体作为数据模型并且拥有丰富的概念模型。
The Semantic Web has the goal of creating Web infrastructure that augments data with metadata to give it meaning, thus making it suitable for automation, integration, reasoning, and re-use.
语义web的目标是创建Web基础设施，使用元数据对数据进行增强，从而使数据变得有意义，最终使数据变得适合进行自动化、集成、推理和重用。
Resolved the semantic heterogeneous problem in the distribution heterogeneous data integration, combining with ontology technology.
结合本体技术解决分布式异构数据集成中的语义异构问题。
Data Integration Challenges: Semantic Meaning and Data quality.
数据集成挑战：语义和数据的品质。
The paper discusses the heterogeneous problems in data integration, especially on the semantic heterogeneity.
该文主要讨论集成过程中的异构问题，尤其是语义异构问题。
In the process of information integration, ontology solved the semantic heterogeneity problem of heterogeneous and autonomous data.
在信息集成过程中，本体解决了异构信息的语义异构问题，实现了信息语义上的互操作。
Ontology-based semantic information integration resolves the schema level heterogeneity and part of data level heterogeneity between distributed data sources.
基于本体的语义信息集成主要解决分布异构的数据源之间的模式级异构和部分数据异构（包括同义字和同音异义字）。
By the semantic data model, an integration method to express design entities and their interdependence is provided.
语义数据模型提供了表达设计实体及其相互依赖关系的一体化方法。
Ontology can specify terms and relations among them. Therefore, an ontology-based data integration might be used to solve the semantic heterogeneity problem.
本体能够明确表示一定领域的概念和概念之间的关系，利用这一特点，基于本体的数据集成能够解决这个问题。
Three main research aspects as below are contained in this thesis: 1. Semantic model-driven approach for enterprise data integration.
为此，论文的研究主要集中在语义模型理论与语义模型进化两方面，包含以下主要内容。
Three main research aspects as below are contained in this thesis: 1. Semantic model-driven approach for enterprise data integration.
为此，论文的研究主要集中在语义模型理论与语义模型进化两方面，包含以下主要内容。
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