语义WEB之本体工程[A Semantic Web Primer阅读笔记]

1. 本体开发过程的主要步骤：

1. Determine scope.

2. Consider reuse.

3. Enumerate terms.

4. Define taxonomy.

5. Define properties.

6. Define facets.

7. Define instances.

8. Check for anomalies.

2. 发现异常数据(abnormality data)和检测不一致和错误的数据detect potential inconsistencies and misconceptions。

3. 本体的引入和不同本体的映射是个问题，The general question of importing ontologies and establishing mappings between different mappings is still wide open。

4. 以下任务可能需要机器学习技术加以支持：

1. Extraction of ontologies from existing data on the Web

2. Extraction of relational data and metadata from existing data on the Web

3. Meging and mapping ontologies by analyzing extensions of concepts

4. Maintaining ontologies by analyzing instance data

5. Improving Semantic Web applications by observing users

机器学习记住可以辅助以下任务：

1. Clustering

2. Incremental ontology updates

3. Support for the knowledge engineer

4. Improving large natural language ontologies

5. Pure (domain) ontology learning

5. 本体学习的一些任务：

1. Ontology creation from scratch by the knowledge engineer. In this task machine learning assists the knowledge engineer by suggesting the most important relations in the field or checking and verifying the constructed knowledge bases.

2. Ontology schema extraction from Web documents. In this task machine learning systems take the data and metaknowledge (like a metaontology) as input and generate the ready-to-use ontology as output with the possible help of the knowledge engineer.

3. Extraction of ontology instances populates given ontology schemas and extracts the instances of the ontology presented in the Web documents. This task is similar to information extraction and page annotation, andcan apply the techniques developed in these areas.

4. Ontology integration and navigation deal with reconstructing and navigating in large and possibly machine-learned knowledge bases. For example, the task can be to change the propositional-level knowledge base of the machine learner into a first-order knowledge base.

5. An ontology maintenance task is updating some parts of an ontology that are designed to be updated (like formatting tags that have to track the changes made in the page layout).

6. Ontology enrichment (or ontology tuning) includes automated modification of minor relations into an existing ontology. This does not change major concepts and structures but makes an ontology more precise.

本体需要的一些应用算法：

1. Propositional rule learning algorithms learn association rules or other forms of attribute-value rules.

2. Bayesian learning is mostly represented by the Naive Bayes classifier. It is based on the Bayes theorem and generates probabilistic attribute-value rules based on the assumption of conditional independence between the attributes of the training instances.

3. First-order logic rules learning induces the rules that contain variables, called first-order Horn clauses.

4. Clustering algorithms group the instances together based on the similarity or distance measures between a pair of instances defined in terms of their attribute values.

6. 本体映射：ontology integration、ontology alignment、ontology mapping

目前解决本体映射的方法有很多，按照所属领域不同可以分为：linguistic、statistical、structural和logical的方法。