DNA MANIA

Ontology, thesaurus, taxonomy meta-model and semantic Web.

Frédéric — Sun, 08 Apr 2007 14:50:00 +0200

The first common question for the neophyte can be : What is the purpose of having a vocabulary ? The man for a few centuries likes to organize, cut out, structure, treat on a hierarchical basis. Sometimes this hierarchisation is so excessive that one loses the direction of them first as often arrived oneself among the naturalists of the 19th century. To have a particular vocabulary to describe a field allows to organize your knowledge.

Ontology

Let us approach a first painful point immediately to thus evacuate it and concentrate on the subject. The origin of the word ontology such as defined in the majority of the dictionaries will not find any reference to data processing.

ontology |änˈtäləjē| noun
The branch of metaphysics dealing with the nature of being.
ORIGIN early 18th cent.: from modern Latin ontologia, from Greek ōn, ont- ‘being’ + -logy .

In Philosophy. Part of the metaphysics which applies to the being as being, independently of its particular determinations.
“Being And Nothingness, phenomenologic test of ontology”, Jean-Paul Sartre.

The world of the semantic Web and information sciences especially adapted this term, I do not know by which intermediary, but it is a fact and with the largest prejudice of many philosophers.

Difference between controlled vocabularies, thesaurus, taxonomy and ontology.

A controlled vocabulary is a list of terms that have been enumerated explicitly. This list is controlled by and is available from a controlled vocabulary registration authority. All terms in a controlled vocabulary should have an unambiguous, non-redundant definition. This is a design goal that may not be true in practice. It depends on how strict the controlled vocabulary registration authority is regarding registration of terms into a controlled vocabulary. At a minimum, the following two rules should be enforced:

  1. If the same term is commonly used to mean different concepts in different contexts, then its name is explicitly qualified to resolve this ambiguity.
  2. If multiple terms are used to mean the same thing, one of the terms is identified as the preferred term in the controlled vocabulary and the other terms are listed as synonyms or aliases.

A taxonomy is a collection of controlled vocabulary terms organized into a hierarchical structure. Each term in a taxonomy is in one or more parent-child relationships to other terms in the taxonomy. There may be different types of parent-child relationships in a taxonomy (e.g., whole-part, genus-species, type-instance), but good practice limits all parent-child relationships to a single parent to be of the same type. Some taxonomies allow poly-hierarchy, which means that a term can have multiple parents. This means that if a term appears in multiple places in a taxonomy, then it is the same term. Specifically, if a term has children in one place in a taxonomy, then it has the same children in every other place where it appears.

A thesaurus is a networked collection of controlled vocabulary terms. This means that a thesaurus uses associative relationships in addition to parent-child relationships. The expressiveness of the associative relationships in a thesaurus vary and can be as simple as “related to term” as in term A is related to term B.

People use the word ontology to mean different things, e.g. glossaries & data dictionaries, thesauri & taxonomies, schemas & data models, and formal ontologies & inference. A formal ontology is a controlled vocabulary expressed in an ontology representation language. This language has a grammar for using vocabulary terms to express something meaningful within a specified domain of interest. The grammar contains formal constraints (e.g., specifies what it means to be a well-formed statement, assertion, query, etc.) on how terms in the ontology’s controlled vocabulary can be used together.

People make commitments to use a specific controlled vocabulary or ontology for a domain of interest. Enforcement of an ontology’s grammar may be rigorous or lax. Frequently, the grammar for a "light-weight" ontology is not completely specified, i.e., it has implicit rules that are not explicitly documented.

A meta-model is an explicit model of the constructs and rules needed to build specific models within a domain of interest. A valid meta-model is an ontology, but not all ontologies are modeled explicitly as meta-models. A meta-model can be viewed from three different perspectives:

  1. as a set of building blocks and rules used to build models
  2. as a model of a domain of interest, and
  3. as an instance of another model.

When comparing meta-models to ontologies, we are talking about meta-models as models (perspective 2).

Note: Meta-modeling as a domain of interest can have its own ontology. For example, the CDIF Family of Standards, which contains the CDIF Meta-meta-model along with rules for modeling and extensibility and transfer format, is such an ontology. When modelers use a modeling tool to construct models, they are making a commitment to use the ontology implemented in the modeling tool. This model making ontology is usually called a meta-model, with “model making” as its domain of interest.

Bottom line: Taxonomies and Thesauri may relate terms in a controlled vocabulary via parent-child and associative relationships, but do not contain explicit grammar rules to constrain how to use controlled vocabulary terms to express (model) something meaningful within a domain of interest. A meta-model is an ontology used by modelers. People make commitments to use a specific controlled vocabulary or ontology for a doma

Semantic Web: which tools for the Power? Part I

Frédéric — Sun, 01 Apr 2007 23:40:00 +0200

Well, well, Well. One day, a friend of mine told me about Semantic Web tools and technologies. It's all about how machine can figure out, interpret and used natural language contents on the Web. There are lot of languages so complex to deal with information. You can have a look here to discove the big cake! However, I think the idea to manage web content by the machine could be sexy. When I read through this last link I thought what is this soup?

So I think, the first step in my case should be to build massive, huge free, open repositories of information through collaborative way.

My last blog contact, Pedro Beltrao (thanks Pedro), gave me an example of this kind of repository: freebase I don't have any invitation yet. I will inform you in next weeks. I hope.

Come back to my friend. Finally, I asked him where is a frontier between Semantic Web tools and Web 2.0 tools?

We will discuss this point next week...

Some new vocabulary you can try with your girlfriend :) Good luck

- FOAF (http://www.foaf-project.org/) - SIOC (http://sioc-project.org/)

Christophe

Bringing Semantic Web to real life?

Frédéric — Sun, 25 Mar 2007 23:24:00 +0200

Thanks to the cool Apps with Web 2.0 features that promote people's thinkings, I end up to believe there is a road to Semantic Web aka Web 3.0. The potential applicability of the Semantic Web is very broad.

So, what 's the Semantic Web? The Wikipedia defines the Semantic Web as a project that intends to create a universal medium for information sharing by transforming documents with computer-processable meaning on the Web. The core idea is to create the meta data describing the data, which will enable computers to process the meaning of things. Once computers are equipped with semantics, they will be capable of solving complex semantical optimization problems. Meaning, computers help us to go through information to avoid time-consuming tasks.

Semantic Web is Personalized Web. So far people use mediation tools, for example, annotations to define information into flexible and open information, persistent personal preferences, even relevant data as filters. That's the key, actually. Others tools are Knowledge Discovery, Knowledge Management and Service-Oriented Architectures (SOA). Plus, a new challenge is to give for all of us end-user apps which integrate, combine and deduce information neede to assist people in performing tasks.

Could Semantic Web give the Power to the People? Not quite but one day...

Christophe

Next week: Semantic Web: which tools for the power?

Further Information:

- - The Semantic Web by Time Berners-Lee.

- - Jena.

- - Joseki.

How Google Books Search can change Academic Science

Frédéric — Fri, 23 Mar 2007 13:17:00 +0100

In a follow-up from this post i've picked up: O'Reilly radar , here's the future of Scholarship and education, I truly think.

Google Books Serch has been undoubtedly useful but, they don't seem to be digitising classic books in law, medicine and various sciences, which are very famous and now in public domain.

However, publishers who have transcribed and published them, have put in Google Books where only few pages/lines can be accessed.

Books are horribly expensive, especially classic legal tomes, which have a smaller market than computer books, to make it economically viable for companies.

In the same post, a Berkeley grad student disses the experience of the Berkeley library system and lauds Google." Jo Guldi, the author of that blog entry, wrote:

I was idle trying a search on "roads" to see what sort of a literature would turn up for the period of my dissertation research, 1740-1850.

I didn't expect much. I've spent the last two years wandering through the Yale, Harvard, and California libraries, the British Library, Britain's National Archives, and the immense reserves of North American Inter Library Loan reading every book on London, pavement, or travel I could get my hands on."

For academic historians , this is turbo chargingwith online access to full text book.

What about books in Sciences, law, medicine and technologies , Mr Google ?

Excellent Overview of Benefits of RDF and SPARQL

Frédéric — Mon, 19 Mar 2007 14:13:00 +0100

This article on XML.com is a very good summary of the benefits of RDF and SPARQL -- two of the key technologies of the emerging Semantic Web.

Is Information Extraction from the scientific litterature ready for Life science ?

Frédéric — Mon, 19 Mar 2007 13:30:00 +0100

For the average biologist, hands-on literature mining currently means a keyword search in PubMed. However, methods for extracting biomedical facts from the scientific literature have improved considerably, and the associated tools will probably soon be used in many laboratories to automatically annotate and analyse the growing number of system-wide experimental data sets."

Extract from Nature Review Genetics : Literature mining for the biologist: from information retrieval to biological discovery by Peer Bork et al. 2006

Simply put, Information extraction ( IE) accomplish these tasks :

* Take natural language text from a document source , and extract the essential facts about one or more predefined fact types.

* Represent each fact as a template whose slots are filled on the basis of what is found in the text.

IE is typically carried out in support of other tasks, usually forms part of application or pipeline of processes. The results of IE is either stored in a databases or subjected to querying or data mining; integrated in knowledge bases to allow reasoning or presented to users for annotation or curation tasks .

Thus, IE is an application of Natural language processing (NLP). As the term implies, the goal is to extract information from text , and the aim is to do so without requiring the end user to read the text. In contrast, information Retrieval (IR) like Search engine is the activity of finding documents that answer an information need with the help of an index.

IE have dealt primarily with news resources , and more recently with scientific publications. In sciences, general language grammar and dictionary are not enough. Scientific fields use many technical terms, only a few are found in common discourses. To some extends, this kind of terms can be listed in auxiliary terminologies. however, automatic term recognition ( ATR) is useful for IE to extract named entities on the basis of their internal structures.

Regardless of what IE approaches was used in the passed, scientific fields, especially biology and Biomedicine is not well suited with IE systems that doesn't make used of ontology and linguistic lexicons. The best exemple is GenIE " Genome Information Extraction" from the institute for Computational linguistic at the University of Stuttgart. they uses Ontology-driven information Extraction technologies that goes behind extracting simple facts from sentences. their aim is to deal with anaphoric reference and information from each sentence merged or a relation must ne established between events.

For instance, if a sentence refers explicitly to a binding action, and the following sentence is pointing to the gene expression regulation du to the interaction between binding factors and promoters sequences, then the dependency between events should be capture.

A must read " Literature mining for the biologist: from information retrieval to biological discovery" by Peer Bork et al. Nature Review Genetics 2006.

DNA MANIA

Open Text Mining Interface

Frédéric — Sun, 11 Mar 2007 15:04:00 +0100

Nature might not quite be in the Open Publishing business like PLoS, but they are an important player nevertheless. I hope the OTMI gets picked up by other publications. It would be nice to have a publication data standard and as one of the top two scientific journals, Nature has the clout to make this happen. Being able to mine journals and search for information is invaluable (open or otherwise), and using standard formats like OPML is an excellent idea.

Being able to share

The Open Text Mining Interface (OTMI) is an initiative from Nature Publishing Group (NPG). It aims to enable scholarly publishers, among others, to disclose their full text for indexing and text-mining purposes but without giving it away in a form that is readily human-readable. Here is their wiki page.

The Open Text Mining Interface provides for a range of structured disclosure options, from word vectors (list of word occurences with frequency counts) and the presentation of text 'snippets' out of narrative order, to the presentation of full text in "raw" or "reduced" form.

The Open Text Mining Interface (OTMI) is aimed to initiate text mining capabilities in sciences publications. Something researchers are waiting for decades now. Nature Blog NASCENT initial demo uses the 23 March issue of Nature .

Embedded in the HTML of the abstract and full-text file for each article is a tag like this:

   <link rel="OTMI" type="application/atom+xml" href="../otmi/otmi-nature04614.xml"/>

which points to an OTMI file — a machine-readable representation of the text. (Technically, it's an Atom Entry document with various XML namespace extensions to allow us to include additional information.) As I write this, the example files for our test issue contain the following information:

   * Bibliographic details (of the kind you might also find in the table RSS feeds)
   * Word vectors. That is, a list of all the words that appear in the article and the number of occurrences. (There's also a stop-word list of very common words that have been excluded.) This enables the construction of the most basic types of search index.
   * 'Snippets'. Basically sentences, presented out of order, which allows more sophisticated indexing and text mining (e.g., the kind that looks out for common constructions such as "A binds to B" or "X inhibits Y"), but not, of course, anything that looks across sentence boundaries.

Note for that for both words and sentences — actually quite hard concepts to define in strict computational terms — the algorithms used to tokenize the text are defined in the OTMI file using regular expressions, so anyone — or anything — examining the file can in principle know exactly how the text was processed to create the respective lists. Note also that the word vectors will usually be redundant if you have the sentences, but they include both for the purposes of this demo (and who know, maybe it's useful to some people if they provide both).

There are still a lot of things that could be improved here. For example:

  1. Allow for text from different sections of an article (e.g., abstract, figure legends) to be labelled as such.
  2. Allow for text to be presented in normal human-readable form for publishers who are willing to provide this.
  3. Add a list of cited articles, providing at least DOIs but perhaps other information too. This would, of course, open up the content to citation analysis.
  4. Add references to the OTMI files from the corresponding RSS feed items (and from the log-in page where content is access-controlled).
  5. Add references to a common stop-word list instead of repeating it in each OTMI file.
  6. Add rights information.
  7. Add references to associated data files and/or database entries.
  8. Provide an actual spec. ;)

They intend to make at least some of these changes (and perhaps others besides) over the coming weeks, so expect the example files to change before your eyes. There's also an even more basic issue around whether an Atom entry document is the right starting point. For example, perhaps an RDF/XML format would be more useful, at least to some people.

The example of RSS shows how powerful a relatively simple common standard can be when it comes to aggregating content from multiple sources (even when it's messed up as badly as RSS ;). So maybe an approach like OTMI (or a better one dreamt up by someone else) can help those who want to index and text-mine scientific and other content. Like RSS, I think publishers might also come to see this as a kind of advert for their content because it should help interested readers to discover it. And on the basis that a something is always better than nothing, it also doesn't force publishers to give away the human-readable form of their content — they can limit themselves to snippets or even just word vectors if they want to.

Frederic

Wellcome DNA maniac !

Frédéric — Sun, 11 Mar 2007 13:14:00 +0000

This is our Blog mainly about our interest in Bioinformatic, Molecular Biology, Text Mining and Semantic Web
We wish you to find interesting matters on this blog.

Our Blog's name "DNA Mania" was taken from an humorous post from Duncan's Blog that we loved to read. In this post, he pointed to some amazing books. I enjoyed The Music of Life: Biology Beyond the Genome of Denis Noble (2006) and realized we were DNA maniac..:-)

Frederic & Christophe