Basic of Ontology

This is opening of Chapter 5, Gene Ontology, of my thesis Empirical Study of Gene Ontology based Microarray Clustering. You can check abstract from here.

5.1 Basic of Ontology

“Ontology” has been a philosophy jargon since Aristotle times, and it means the nature of existence. Computer scientists adopted this word to express a formally structured vocabulary in a discipline. In this vocabulary, items and relations between two items are well defined to present the knowledge in this discipline.

Ontologies offer a mechanism by which knowledge can be represented in a form capable of machine processing[Lord 2003]. Ontologies can be provided in Rational Database format or XML format.

Now ontology becomes the core of Semantic Web, because the geographically distributed Web forms information islands in the Internet, and the use of ontology can interpret meanings of information in different islands, reduce the confusion, and integrate data automatically. The decentralized infrastructure makes the communication and collaboration over Internet easy. Every one can focus on her own part of the project independently and integration of their work will be streamlined since every part of the collaboration follows the same ontology and plays her own role. Every one can also build new ontologies, and cooperate with the third part without the permission of her collaborators. The collaboration will be stronger and stronger as more and more collaborators join in and share their knowledge. This decentralized infrastructure breaks down the barrier between languages, geographical distance, automates the integration of knowledge, and leads to the evolution of knowledge.

Empirical Study of Gene Ontology based Microarray Clustering

Abstract:

This thesis project studies the current several similarity measures over Gene Ontology and introduces a new measure combined with Euclidean distance to perform Microarray analysis. The new combined measures contain both the expression data and (known) biological information from Gene Ontology to express the real biological relation between gene products. In order to adapt the similarity measure to the Gene Ontology, an On-The-Fly probability is initially defined to calculate the probability of a term in the current problem space. A similarity measure between a term and a set of terms is defined, as well as a similarity measure between sets. The performance of applying these similarity measures is compared by clustering a dataset of which the correct clustering scheme is known. The results of the comparison are analyzed and some conclusions are drawn about the similarity measure.

A reminder

The paper, Measuring semantic similarity between Gene Ontology terms by F.M. Couto, can be integrated into my thesis.

Different "context" can use to perform comparison

GO-MAC: Gene Ontology based MicroArray Clustering

The story starts from DNA. DNA of human and animal consists of millions of genes, and different genes can be applied to produce different proteins. If we know the function of each kind of protein, we can control the quantity of the corresponding gene, then the creation of protein is under our control, and we can use it to fight for heart-stroke, cancer... Yes, this is a big topic in Bio-Informatics.

Using the technilogy of MicroArray, DNA is cut into gene slides, when each slide has a single gene. In a small chip there are thousands of grids, and in each grid there is one kind of gene slide. The chip will be put in a changing environment such as a heating room. When the temperature is increasing, some genes will duplicate itself so that more protein will be created, when the quantity of some genes remain the same, because those genes and corresponding proteins have nothing to do with the temperature. We record the quantity of the genes, and study the pattern of the changing curve, then we can know (a little bit of) the function of the gene in the condition of changing environment.
The creation of MicroArray technology is an evolutional event, because it enables the high-throughput study of the function of genes, and highly increase the productivity of the research, because the previous research methods only allow us to study 2-3 genes at the same time, compare to thousands of genes studied in MicroArray.

After the lab experiment, we can the numbers of different genes during different environment. For example, the number of 6,000 gene slides in the 37C degree, in 37.5C degree, and in 38C degree. That will be 18,000 numbers we need to study. The traditional way to study the 18,000 numbers is to cluster them using k-means or hierarychy clustering method. When clustered, genes with similar changing curve will be put in one group, and if we know the function of ONE gene in the group, we can assume that other genes has the same function.

The disadvantage of this method is: When the k-means or hierarchy clustering method is applied, the program treats the genes as numbers. It doesn't understand the meaning of the genes at all. In other words, the biological meaning is lost.

Yes, BIOLOGICAL MEANING. You can see mist on top of a cup of hot water and a cup of ice cream, but they are different!

That's why I adopted Gene Ontology in the study of MicroArray. What is Ontology? Basically, Ontology is a dictionary of a knowledge domain. For example, an Ontology of a software company can be defined as:

____

Gene Ontology (GO) is defined by biologiests around the world. I can give a sample of the Gene Ontology here:


_____

Before applying the Gene Ontology into application, we annotate genes using the terms of GO. ____ is annotated as _____, ______, ____. From the annotation we can have some basic idea of this gene already. There are some automatic annotation tools deployed to extract terms of existing thesis, papers, databases all over the world to annotate the genes that we have studied. We are not working from void. We use all the knowledge human knows.

Gene Ontology Application in Microarray clustering

Attached is a survey about GO Application in Microarray clustering.

Abstract
Microarray enables high-through analysis of gene information, thus lead to revolutionary changes in Bioinformatics research. Clustering is the basic method applied in microarray processing. Different clustering algorithms from pattern-recognition are adopted in microarray clustering. But during the processing, biology knowledge of genes is lost, and researchers found the majority of clusters are not related to known biology. Gene Ontology provides a valuable, computationally accessible form of the community’s knowledge in a structured way, and many studies have revealed that GO should be applied in microarray analysis.



Keywords: Microarray, Clustering, Gene Ontology, Interoperability, Collaboration, Decentralization, Inference, Visualization


BTW: http://fadshop.net/blog/others/569 The Semantic Web.ppt is slides about Tim Berners-Lee's article in Scietific American: The Semantic Web A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. ...

The Semantic Web and Decentralization

Last week I made a presentation in the 569 class. Here is the slides. It's a talk about Tim Berners-Lee's article in Scietific American: The Semantic Web A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities

After the presentation, most questions are related to "Decentralization". Why we need a decentralized Web? Why don't we build a well-organized central database, shared for everyone?

The traditional Knowledge Representation has been developed for many years. It has a central database, but how to control this database, and who should take charge of it? These are becomming bigger and bigger questions. Usually we don't trust a central database. What if the controller make mistakes? what if he is evil? Also, increasing the size of a central database becomes unmanageable.

15 years ago, when the new World Wide Web was released, as Tim said, "detractors pointed out that it could never be a well-organized library; withougt a central database and tree structure, one would never be sure of finding everything". But now you can see how successful the Web is.

In a decentralized system, everyone is able to express her idea, even when the idea is not quite clear, she can publish the half-finished plan in the Web. 1 minute later somewhere in another corner of the world, somebody might recognize/understand this idea and have a better way to make it better. We don't need a central system to verify the ideas and prove them. Information exchange can be implement instantly, and we can create a new information evolution.

Maybe, that's the power of democracy.


Dr. Lu mentioned CYC in the class. OpenCYC is a subset of CYC, while CYC is a commercial database. It is an ontology without XML.

Weaving the web

这两天我正在看Tim Berners-Lee的Weaving the web一书。这本书在寻智书摘有中文介绍。看到他发明了ＷＷＷ之后，自己的工作单位不支持，到处奔波去推广，很黯然。

我还没有看完这本书，只看到他将要成立W3C。这时候他自己做了一个很简陋的浏览器，只能在NeXT这个操作系统上用；芬兰的研究生团队做了一个Erwise，毕业之后就解散了，留下一堆用芬兰语注释的代码；加州一个Wei Pei(魏培源?)为了展示他发明的一种语言（Viola），做了一个浏览器，但是必须先安装他那个语言包才能用，所以很复杂；只有NCSA做的Mosaic最好，流行起来；但是这时候NCSA正在考虑把Web归为专属，让Tim Berners-Lee充满危机感，所以一定要成立一个公共机构，把WEB推给全世界。

我还没有看完，但是想起亞當正在写香港浏览器历史，不知道会怎么介绍 Netscape。看来NCSA真是想要控制整个万维网的，因为他后来开发了服务器软件，而且它的软件都收费，价格不菲。Netscape也是收费的软 件，后来当微软进入这个行当，免费推IE，才把这股歪风打下来了。至于后来微软垄断的事情，另当别论。

What is Ontology

[Ontology]

刚才花了半个钟头, 用一个书的例子写了"What is Ontology", 一发送,网络连接失败, 甚至没有存档!
keso建议我节哀顺便，我看还是重写一遍吧。

---

Ontology: 存在论，本体论。
这是一个2000年前希腊哲学家们提出的概念，最近由于W3C翻出这个词来，搞得脑袋乱乱。

搜索网络：

• an explicit formal specification of how to represent the objects, concepts and other entities that are assumed to exist in some area of interest and the relationships that hold among them.
• 共享概念模型的形式化规范说明

介绍Ontology，先得从Semantic Web（语义网）开始。

目前的万维网(WWW)是以读者为目标，用各种颜色、字体、多媒体技术表示琳琅满目的内容。但是如果要做软件来提取网页的意思，汇总网络的知识，有许多东西要改变。比如说：现在有一个书目网页上有这样一段：

书 名：What is Ontology
作 者：Ben
译 者：Tim Berners-Lee

你很容易知道这里要表示的内容，但是如果一个软件读到这个网页，它看到的是什么呢？

书＆nbsp;名：〈span style="font-weight: bold;">What is Ontology〈/span>
〈span style="color:red;">作＆nbsp;者：〈a href="http://fadshop.net/ben">Ben〈/a>〈/span>
译＆nbsp;者：Tim Berners-Lee

呜，乱糟糟。这个软件就需要从这里分析出书名、作者、译者等信息。更糟糕的是这个软件必须是对一个网页来定制；如果它来到另一个有相同内容的网页，所看到的原码又不一样，需要对这个做另一番的处理。即使同一个网站，老板随时可能修改版面风格，你的程序又跑不动了。

如果按照XML的方式来组织网站就好了：

〈book>
　　〈title>What is Ontology〈/title>
　　〈author>Ben〈/author>
　　〈translator>Tim Berners-Lee〈/translator>
〈/book>
〈person>
　　〈name> Ben 〈/name>
　　〈website>http://fadshop.net/ben〈/website>
〈/person>

然后再写清楚author是person的一种就可以了。

用这种构架来表示互联网，就可以轻易编写程序，在各大出版社或书店的网站里跑来跑去，收集所有书的内容，从里面找到你所需要的资料。找到你心仪的书之后， 程序还会检查一下书店的地址，再在邻近街上找到一个好的咖啡馆，告诉你在买书的时候不妨进去坐坐。程序再来到市政网站，找到公交车路线和时间，帮你把一切 弄得妥妥当当，买书的过程就是一个享受。
是啊，只要书店、咖啡馆、市政府的网站都用共同的方式来标注它们的内容，网络就成为一个统一的知识库，可以用程序找到最适合你的东西。

上面所说的就是Semantic Web（语义网）。这里还有一个小障碍：如果有一个书店用〈writer>而不是〈author>来表示作者，怎么办？Ontology就是要定义一个专业领域内的词汇，并且描述这些词汇之间的关系。比如说：〈author>是一个person，不是用来吃的，没有奶油味道；〈book>一定要有书号ISBN，否则就是盗版书...

简单来说，Ontology就是一个专业词汇表。我说“专业”是因为目前人们只能从专业开始，慢慢覆盖人类的所有知识领域，不可能一下子把所有都包含进来。

现在再来看看关于Ontology的定义，是不是另有一番味道？

• an explicit formal specification of how to represent the objects, concepts and other entities that are assumed to exist in some area of interest and the relationships that hold among them.
• 共享概念模型的形式化规范说明