Mining the Social Web, 2nd Edition

Chapter 8: Mining the Semantically Marked-Up Web: Extracting Microformats, Inferencing Over RDF, and More

This IPython Notebook provides an interactive way to follow along with and explore the numbered examples from Mining the Social Web (2nd Edition). The intent behind this notebook is to reinforce the concepts from the sample code in a fun, convenient, and effective way. This notebook assumes that you are reading along with the book and have the context of the discussion as you work through these exercises.

In the somewhat unlikely event that you've somehow stumbled across this notebook outside of its context on GitHub, you can find the full source code repository here.

You are free to use or adapt this notebook for any purpose you'd like. However, please respect the Simplified BSD License that governs its use.

Example 1. Extracting geo-microformatted data from a Wikipedia page

In []:
import requests # pip install requests
from BeautifulSoup import BeautifulSoup # pip install BeautifulSoup

# XXX: Any URL containing a geo microformat...

URL = ',_Tennessee'

# In the case of extracting content from Wikipedia, be sure to
# review its "Bot Policy," which is defined at

req = requests.get(URL, headers={'User-Agent' : "Mining the Social Web"})
soup = BeautifulSoup(req.text)

geoTag = soup.find(True, 'geo')

if geoTag and len(geoTag) > 1:
    lat = geoTag.find(True, 'latitude').string
    lon = geoTag.find(True, 'longitude').string
    print 'Location is at', lat, lon
elif geoTag and len(geoTag) == 1:
    (lat, lon) = geoTag.string.split(';')
    (lat, lon) = (lat.strip(), lon.strip())
    print 'Location is at', lat, lon
    print 'No location found'

Example 2. Displaying geo-microformats with Google Maps in IPython Notebook

In []:
from IPython.display import IFrame
from IPython.core.display import display

# Google Maps URL template for an iframe

google_maps_url = "{0}+{1}&" + \
  "ie=UTF8&t=h&z=14&{0},{1}&output=embed".format(lat, lon)

display(IFrame(google_maps_url, '425px', '350px'))

Example 3. Extracting hRecipe data from a web page

In []:
import sys
import requests
import json
import BeautifulSoup

# Pass in a URL containing hRecipe...

URL = ''

# Parse out some of the pertinent information for a recipe.
# See

def parse_hrecipe(url):
    req = requests.get(URL)
    soup = BeautifulSoup.BeautifulSoup(req.text)
    hrecipe = soup.find(True, 'hrecipe')

    if hrecipe and len(hrecipe) > 1:
        fn = hrecipe.find(True, 'fn').string
        author = hrecipe.find(True, 'author').find(text=True)
        ingredients = [i.string 
                            for i in hrecipe.findAll(True, 'ingredient') 
                                if i.string is not None]

        instructions = []
        for i in hrecipe.find(True, 'instructions'):
            if type(i) == BeautifulSoup.Tag:
                s = ''.join(i.findAll(text=True)).strip()
            elif type(i) == BeautifulSoup.NavigableString:
                s = i.string.strip()

            if s != '': 
                instructions += [s]

        return {
            'name': fn,
            'author': author,
            'ingredients': ingredients,
            'instructions': instructions,
        return {}

recipe = parse_hrecipe(URL)
print json.dumps(recipe, indent=4)

Example 4. Parsing hReview-aggregate microformat data for a recipe

In []:
import requests
import json
from BeautifulSoup import BeautifulSoup

# Pass in a URL that contains hReview-aggregate info...

URL = ''

def parse_hreview_aggregate(url, item_type):
    req = requests.get(URL)
    soup = BeautifulSoup(req.text)
    # Find the hRecipe or whatever other kind of parent item encapsulates
    # the hReview (a required field).
    item_element = soup.find(True, item_type)
    item = item_element.find(True, 'item').find(True, 'fn').text
    # And now parse out the hReview
    hreview = soup.find(True, 'hreview-aggregate')
    # Required field
    rating = hreview.find(True, 'rating').find(True, 'value-title')['title']
    # Optional fields
        count = hreview.find(True, 'count').text
    except AttributeError: # optional
        count = None
        votes = hreview.find(True, 'votes').text
    except AttributeError: # optional
        votes = None

        summary = hreview.find(True, 'summary').text
    except AttributeError: # optional
        summary = None

    return {
        'item': item,
        'rating': rating,
        'count': count,
        'votes': votes,
        'summary' : summary

# Find hReview aggregate information for an hRecipe

reviews = parse_hreview_aggregate(URL, 'hrecipe')

print json.dumps(reviews, indent=4)

Note: You may also want to try Google's structured data testing tool to extract semantic markup from a webpage

Note: You can use bash cell magic as shown below to invoke FuXi on the sample data file introduced at the end of the chapter as follows:

In []:
FuXi --rules=resources/ch08-semanticweb/chuck-norris.n3 --ruleFacts --naive

You can explore other options for FuXi by invoking its --help command

In []:
FuXi --help