Abstract

Below the first draft of the abstract of my paper. It doesn’t yet include the results/conclusion. Word count: 127

Semantic annotation uses human knowledge formalized in ontologies to enrich texts, by providing structured and machine-understandable information of its content. This paper proposes an approach for automatically annotating texts of the Cyttron Scientific Image Database, using the NCI Thesaurus ontology. Several frequency-based keyword extraction algorithms, aiming to extract core concepts and exclude less relevant concepts, were implemented and evaluated. Furthermore, text classification algorithms were applied to identify important concepts which do not occur in the text. The algorithms were evaluated by comparing them to annotations provided by experts. Semantic networks were generated from these annotations and an ontology-based similarity metric was used to cross-compare them. Finally the networks were visualized to provide further insights into the differences of the semantic structure generated by humans, and the algorithms.

Tags: Semantic annotation, ontology-based semantic similarity, semantic networks, keyword extraction, text classification, network visualization, text mining

Results? Thesis #5

As promised, I have spent the last two weeks generating a lot (but not quite 120) results. So let’s take a quick look at what I’ve done and found.

First of all, the Cyttron DB. Here I show 4 different methods of representing the Cyttron database, the 1st is as-is (literal), the 2nd by keyword extraction (10 most frequently occurring words, after filtering for stopwords), the 3rd is by generating synonyms with WordNet for each word in the database, the 4th is by generating synonyms with WordNet for each word of the keyword representation.

Cy-literal Cy-keywords Cy-WN Cy-key-WN
Unique 19,80 3,23 97,58 17,59
Total 30,58 3,18 248,19 25,53

Next up, the Wikipedia-page for Alzheimer’s disease. Here I have used the literal text, the 10 most frequently occurring bigrams (2-word words), the 10 most frequently occurring trigrams (3-word words), the 10 most frequently occurring keywords (after stopwords filtering) and the WordNet-boosted text (generating synonyms with WordNet for each word).

Alz-Literal Alz-bigrams Alz-trigrams Alz-keywords Alz-WN
Unique 803 8 1 5 1385
Total 3292 8 1 6 22.195

The other approach, using the ontologies’ term’s descriptions didn’t quite fare as well as I’d hoped. I used Python’s built-in difflib module, which at the time seemed like the right module to use, but after closer inspection did not quite get the results I was looking for. The next plan is to take a more simple approach, by extracting keywords from the description texts to use as a counting measure in much the same way I do the literal matching.

All the results I generated are hard to evaluate, as long as I do not have a method to measure the relations between the found labels. More labels is not necesarily better, more relevant labels is the goal. When I ‘WordNet’-boost a text (aka generate a bunch of synonyms for each word I find), I do get a lot more literal matches: but I will only know if this makes determining the subject easier or harder once I have a method to relate all found labels to each other and maybe find a cluster of terms which occur frequently.

What’s next?

I am now working on a simple breadth-first search algorithm, which takes a ‘start’-node and a ‘goal’-node, queries for direct neighbours of the start-node one ‘hop’ at a time, until it reaches the goal-node. It will then be possible to determine the relation between two nodes. Note that this will only work within one ontology, if the most frequent terms come from different ontologies, I am forced to use simple linguistic matching (as I am doing now), to determine ‘relatedness’. But as the ontologies all have a distinct field, I imagine the most frequent terms will most likely come from one ontology.

So, after I’ve finished the BFS algorithm, I will have to determine the final keyword-extraction methods, and ways of representing the source data. My current keyword-extraction methods (word frequency and bi/trigrams) rely on a large body of reference material, the longer the DB entry the more effective these methods are (or at least, the more ‘right’ the extracted keywords are, most frequent trigrams from a 10-word entry makes no sense).

Matching terms from ontologies is much more suited for smaller texts. And because of the specificity of the ontologies’ domain, there is an automatic filter of non-relevant words. Bio-ontologies contain biological terms: matching a text to those automatically keeps only the words I’m looking for. The only problem is that you potentially miss out on words which are missing from the ontology, which is an important part of my thesis.

Ideally, the final implementation will use both approaches; ontology matching to quickly find the relevant words, calculate relations, and then keyword extraction to double-check if no important or relevant words have been skipped.

To generate the next bunch of results, I am going to limit the size of both the reference-ontologies as the source data. As WordNet-boosted literal term-matching took well over 20 hours on my laptop, I will limit the ontologies to 1 or 2, and will select around 10 represenatitive Cyttron DB-entries.

I am now running my Sesame RDF Store on my eee-pc (which also hosts @sem_web), which is running 24/7 and accessible from both my computers (desktop and laptop)! Also, I am now on GitHub. There’s more results there, check me out » http://github.com/dvdgrs/thesis.

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Graduation pt. 4: What’s next

Just a quick update to let myself know what’s going to happen next: It’s time to produce some results! While I was getting quite stuck in figuring out the best – or rather, most practical – way to extract keywords from a text (and not just any text, mind you, but notes of biologists), my supervisor told me it’s time to get some results. Hard figures. I decided to scrap POS-tagging the notes to extract valuable phrases, after I noticed the accuracy of the default NLTK POS-tagger was way below practical usage. Not too surprising, considering the default NLTK tagger is probably not trained on biologists’ notes.

Anyway, we came up with the following tests:

I use two sources:

  1. The first being the biologist’s notes (the Cyttron DB).
  2. The second being specific Wikipedia pages on recurring topics of the Cyttron DB:
    Alzheimer, Apoptis, Tau protein & Zebrafish.

From these two sources, I will use five different methods of representing the information:

  1. Literal representation (using each word, no edit)
  2. Simple keyword extraction (using word frequency after subtracting english stopwords)
  3. Bigram collocations
  4. Trigram collocations
  5. Keyword combo (word frequency + bigrams + trigrams)

Each of these ways of representing the source information can then be ‘boosted’  by using WordNet to generate synonyms, doubling the ways of representing the data (2×5=10!).

With these 10 different representations of each of the two datasources (2×10), I will use 3 ways to try to determine the subject:

  1. Literal label matching using two different sets of ontologies:
    1. Cyttron-set: Gene Ontology, Human Disease Ontology, Mouse Pathology & National Cancer Institute Thesaurus
    2. DBPedia ontology
  2. Matching the sources to descriptions of ontologyterms, using the same two sets of ontologies.
  3. If I manage: Matching the datasources to ‘context‘ of ontology terms.
    I started working on a method to take a term in an ontology and explore its surrounding nodes. I will collect all ‘literals’ attached to a node, and throw them in a big pile of text. I will then use this pile of text as a bag of words, to match to the datasources.

This will bring the total amount of tests to be done to 120:

  2 sources (wiki/cyttron)
 10 representations of these sources
  3 methods (literal/desc/context)
  2 ontologies (cyttron-set/dbpedia)
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  2 * 10 * 3 * 2 = 120

And in-between I also have Lowlands 2011 and Vollt to attend. Oh gosh…

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