Will They Blend? Experiments in Data & Tool Blending. Today: MS Word meets Web Crawling. Identifying the Secret Ingredient

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

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Today: MS Word meets Web Crawling. Identifying the Secret Ingredient

Authors: Roland Burger and Heather Fyson

The Challenge

It’s Christmas again, and like every Christmas, I am ready to bake my famous Christmas cookies. They’re great and have been praised by many! My recipes are well-kept secrets as I am painfully aware of the competition. There are recipes galore on the web nowadays. I need to find out more about these internet-hosted recipes and their ingredients, particularly any ingredient I might have overlooked over the years.

My recipes are stored securely in an MS Word document on my computer, while a very well regarded web site for cookie recipes is http://www.handletheheat.com/peanut-butter-snickerdoodles/. I need to compare my own recipe with this one on the web and find out what makes them different. What is the secret ingredient for the ultimate Christmas cookie?

In practice, on one side I want to read and parse my Word recipe texts and on the other side I want to use web crawling to read and parse this web-hosted recipe. The question as usual is: will they blend?

Topic. Christmas cookies.

Challenge. Identifying secret ingredients in cookie recipes from the web by comparing them to my own recipes stored locally in an MS Word document.

Access Mode. MS Word .docx file reader and parser and web crawler nodes.

The Experiment

Parsing the Word document

The Text Processing Extension of KNIME Analytics Platform version 3.3 includes the Tika library with a Tika Parser node that can read and parse virtually any kind of document: docx, pdf, 7z, bmp, epub, gif, groovy, java, ihtml, mp3, mp4, odc, odp, pptx, pub, rtf, tar, wav, xls, zip, and many more!

The KNIME Text Processing extension can be installed from KNIME Labs Extension / KNIME TextProcessing. (A video on YouTube explains how to install KNIME extensions, if you need it.)

Reading the MS Word file

  1. After installing the KNIME Text Processing extension, we take the Tika Parser node in KNIME Labs/Text Processing/IO to read and parse the content of the SnickerdoodleRecipes.docx file. The content of the file is then available at the node output port.
  2. The Strings to Document node converts a String type column into a Document type column. Document is a special data type necessary for many Text Processing nodes. After that, a Sentence Extractor node splits the document into the different recipes in it.

Extracting the list of ingredients

  1. Next, we extract the paragraph with the cookie ingredients. This is implemented through a series of dedicated String Manipulation nodes, all grouped together in a metanode called “Ingredients only”.
  2. The “Text Processing” metanode works on the ingredient sections after they have been transformed into Documents: Punctuation Erasure, Case Conversion, Stop Word Filtering, Number Filtering, and N Char Filtering.
  3. Inside the same “Text Processing” metanode, a POS Tagger node tags each word according to its part of speech. Thus the Tag Filter node keeps only nouns and discard all other parts of speech. Finally, the Stanford Lemmatizer node reduces all words to their lemma, removing their grammar inflections.
  4. The data table at the output port of the “Text Processing” metanode now contains only nouns and is free of punctuation, numbers, and other similarly uninteresting words (i.e. non-ingredients). We now use the Bag of Words node to move from the Document cells to their list of words.
  5. Duplicate items on the list are then removed by a GroupBy node in the Unique List metanode.

Crawling the web page

Here we use a KNIME Community Extension, the Palladian extension, to crawl the web. It’s available under KNIME Community Contributions – Other / Palladian for KNIME. (A video on YouTube explains how to install KNIME extensions.) After installing it, we proceed to crawl the page http://www.handletheheat.com/peanut-butter-snickerdoodles/.

Crawling the web page.

  1. First a Table Creator node is introduced to contain the URL of the web page.
  2. Then the HttpRetriever node from the Palladian extension is applied to retrieve the content of the web page.
  3. The HtmlParser node uses the nu HTML Parser to extract the content of an HTML document and to store it in an XML data cell.
  4. Two XPath nodes extract the recipe from the XML document.

Now that we have the recipe text, we repeat the same steps described above in “Extracting the list of ingredients”.

Comparing the two lists of ingredients

Two lists of ingredients have emerged from the two workflow branches. We need to compare them and discover the secret ingredient – if any – in the new recipe as downloaded from the web.

  1. A Reference Row Filter node compares the new list of ingredients with the list of ingredients from the existing recipes in the Word document. The ingredients from the old recipes are used as a reference dictionary. Only the new ingredients, missing from the previous recipes but present in the web-derived recipe, are transmitted to the node output port.
  2. New and old ingredients are then labeled as such and concatenated together to produce a color-coded word cloud with the Tag Cloud node. Here, new ingredients are highlighted in red while old ingredients are gray.
  3. Finally, the word cloud image is exported to a report, which is shown in figure 2.

The workflow used for this experiment is available for download from the EXAMPLES server as usual under 08_Other_Analytics_Types/01_Text_Processing/10_Discover_Secret_Ingredient08_Other_Analytics_Types/01_Text_Processing/10_Discover_Secret_Ingredient*

Figure 1. This workflow successfully blends ingredients from recipes in a Word document with ingredients from a recipe downloaded from the Web.


(click on the image to see it in full size)

The Results

Yes, they blend!

The main result is that data from a MS Word document and data from web crawling do blend! We have also shown that ingredients from recipes in a Word document and ingredients from web-based recipe also blend!

But what about the secret ingredient for the ultimate cookie? Well, thanks to this experiment I have discovered something I did not know: peanut butter cookies. If you check the final report in figure 2, you can see the word “peanut” emerging red from the gray of traditional ingredients. I will try out this new recipe and see how it compares with my own cookie recipes. If the result is a success I might add it to my list of closely guarded secret recipes!

Happy Holidays everyone!

Figure 2. The report resulting from the workflow above, identifying peanut (in red) as the secret ingredient of the cookie competition.


(click on the image to see it in full size)

Credit for the web hosted recipe goes to Tessa. More infos at http://www.handletheheat.com/peanut-butter-snickerdoodles/

Coming Next …

If you enjoyed this, please share it generously and let us know your ideas for future blends.

We will pause now with the “Will they blend?” series for the Christmas vacation. We will be back in early January 2017. There, we will mix JSON with XML formatted data. Will they blend?

 


* The link will open the workflow directly in KNIME Analytics Platform (requirements: Windows; KNIME Analytics Platform must be installed with the Installer version 3.2.0 or higher)