class PublicationListExtractor(object):
"""
PublPageMetaExtractor handles harvests metadata from web pages containing
references (publication list). For parsing sequences in HTML dom we use
extractors.sequencewrapper.HTMLSequenceWrapper. For parsing citations
(records in data regions, which were found by sequencewrapper) we use
extractors.citationentityextractor.CitationEntityExtractor.
To improve accuracy of this system, we check headers wheather they contain some
keyword, which could help us to determine the correct type of publication.
From headers we also harvest keywords.
"""
entitydefstr = {
'216': 'O',
'217': 'U',
'214': 'O',
'197': 'A',
'198': 'E',
'210': 'O',
'211': 'O',
'195': 'A',
'194': 'A',
'196': 'A',
'193': 'A',
'192': 'A',
'251': 'u',
'252': 'u',
'238': 'i',
'239': 'i',
'235': 'e',
'234': 'e',
'212': 'O',
'236': 'e',
'237': 'i',
'230': 'e',
'231': 'c',
'232': 'e',
'213': 'O',
'224': 'a',
'249': 'u',
'253': 'y',
'248': 'o',
'243': 'o',
'255': 'y',
'250': 'u',
'233': 'e',
'201': 'E',
'200': 'E',
'203': 'E',
'202': 'E',
'205': 'I',
'204': 'I',
'207': 'I',
'206': 'I',
'242': 'o',
'220': 'U',
'245': 'o',
'244': 'o',
'246': 'o',
'241': 'n',
'218': 'U',
'229': 'a',
'228': 'a',
'227': 'a',
'226': 'a',
'225': 'a',
'219': 'U',
'221': 'Y',
# these are added
'248': 'r',
'185': 's',
'174': 'Z',
'232': 'c',
'200': 'C',
'169': 'S',
'190': 'z',
'199': 'C',
'amp': '&',
'nbsp': ' ',
'quot': '\"'
}
def __init__(self, xmlcompatibility='db09'):
self.seqwrapper = HTMLSequenceWrapper(childcoef=7.0,
headercoef=3.0,
mintextlen=30)
self.citaextractor = CitationEntityExtractor(
ALL, xmlcompatibility=xmlcompatibility)
self.ee = EntityExtractor()
self.mime = MIMEhandler()
self.crawler = Crawler()
self.bibtex = BibTeXParser()
self.xmlcompatibility = xmlcompatibility
self._xmlvalid = int(xmlcompatibility.lstrip('db'))
self._publ_list = []
def _set_new_topic(self, publ, kw):
"""
This method adds new topic to publication.
"""
if not re.search("[a-z]{4,}", kw):
return publ
if re.search("publi|paper", kw, re.I):
return publ
t = RRSTopic(title=kw)
publ.set('topic', t)
return publ
def _set_publ_type(self, header, publ):
def _floor(i):
if i > 100: i = 100
return i
if header is None: return publ
# try to set publication type from header
for _type in RRSPublication.publication_types:
if re.search(_type, header, re.I):
if publ.get('type') == _type:
publ.set('credibility', _floor(publ.get('credibility')))
else:
publ.set('type', _type)
return publ
if re.search("dissertation", header, re.I):
publ.set('type', 'phdthesis')
return publ
if re.search('technical report', header, re.I):
publ.set('type', 'techreport')
return publ
# make keyword from header
return self._set_new_topic(publ, header)
def translate_html_entities(self, text):
ents = re.findall(r'&(#?)(x?)(\w+);', text)
for ent in set(ents):
try:
text = re.sub('&(#?)' + re.escape(ent[2]) + ";",
self.entitydefstr[ent[2]], text)
except:
pass
return text
def compare_chunks_to_extracted(self, chunks, publ):
if not publ.get('title'): return publ
title = self.translate_html_entities(publ.get('title'))
authors = publ.get('person_author')
author_names = [a.get('name')[0].get('full_name') for a in authors]
for ch in chunks:
l = ch.get_link()
# get chunk text
ch = self.translate_html_entities(ch.get_text())
# add url if available
if l is not None and not l.startswith("javascript") and l != "#":
u = RRSUrl(type='publication', title=ch, link=l)
publ.set('url', u)
# repair title if needed
if ch in title or ch == title:
if float(len(ch)) / float(len(title)) > 0.4:
publ.set('title', ch)
# repair names if needed
for a in author_names:
if a in ch:
authors_extracted = self.ee.find_authors(ch)
publ.person_author = authors_extracted[0]
break
return publ
def _fill_citation(self, publ):
c = RRSCitation()
c.set('content', self.cita_text)
if publ.get('event'):
c.set('event', publ.get('event')[0].get('title'))
return c
def _handle_bibtex_pages(self):
urls = {}
for i, p in enumerate(self._publ_list):
pub_u = p.get('url')
for u in pub_u:
urls[u.get('link')] = i
#if link is web page, not pdf
urls_to_download = []
content_types = self.mime.start(urls.keys())
for k in urls.keys():
if content_types[k] in ('text/html', 'application/xhtml+xml',
'application/x-httpd-php',
'text/javascript'):
urls_to_download.append(k)
# download page a try it for bibtex
pages = self.crawler.start(urls_to_download)
for u in urls_to_download:
bibtex = self.bibtex.parse(pages[u])
# if bibtex on page, set publication
if bibtex is not None:
self._publ_list[urls[u]] = bibtex
def _empty(self):
for x in range(len(self._publ_list)):
self._publ_list.pop()
self.cita_text = None
def _handle_document(self, doc):
self._empty()
# for all regions which were found
for reg in doc.get_regions():
# get their header
header = reg.get_name()
# for all records in region
for rec in reg._manual_process_page():
# create empty citation object
c = RRSCitation()
# harvest citation record text (probably citation we hope)
self.cita_text = self.translate_html_entities(rec.get_text())
# set the content of record to citation object
c.set('content', self.cita_text)
# fill object it wih extracted data
c = self.citaextractor.extract(c)
# get extracted publication
publ = c.get('publication_cited')
# if sequencewrapper extracted come text chunks, it helps us a lot,
# beacause we can compare extracted data to chunks and if not matched
# we can fix it
publ = self.compare_chunks_to_extracted(rec.get_chunks(), publ)
# insert citation into publication
# !!! we are extracting publications, not citations. Because we dont
# want tree like this: citation->publication but this:
# publication->citation
publ.set('citation', self._fill_citation(publ))
# try to find publication type in header of data region
publ = self._set_publ_type(header, publ)
# add to publication list
self._publ_list.append(publ)
#self._handle_bibtex_pages()
return self._publ_list
#---------------------------------------------------------------------------
# public methods
#---------------------------------------------------------------------------
def extract_data(self, tree, url):
"""
Main method for extracting publication metadata from page.
"""
# wrap html document
document = self.seqwrapper.wrap_h(tree, url)
# handle it and return the result
return self._handle_document(document)
class PublicationListExtractor(object):
"""
PublPageMetaExtractor handles harvests metadata from web pages containing
references (publication list). For parsing sequences in HTML dom we use
extractors.sequencewrapper.HTMLSequenceWrapper. For parsing citations
(records in data regions, which were found by sequencewrapper) we use
extractors.citationentityextractor.CitationEntityExtractor.
To improve accuracy of this system, we check headers wheather they contain some
keyword, which could help us to determine the correct type of publication.
From headers we also harvest keywords.
"""
entitydefstr = {'216': 'O', '217': 'U', '214': 'O', '197': 'A',
'198': 'E', '210': 'O', '211': 'O', '195': 'A', '194': 'A',
'196': 'A', '193': 'A', '192': 'A', '251': 'u', '252': 'u', '238': 'i',
'239': 'i', '235': 'e', '234': 'e', '212': 'O', '236': 'e', '237': 'i',
'230': 'e', '231': 'c', '232': 'e', '213': 'O', '224': 'a', '249': 'u',
'253': 'y', '248': 'o', '243': 'o', '255': 'y', '250': 'u',
'233': 'e', '201': 'E', '200': 'E', '203': 'E', '202': 'E', '205': 'I',
'204': 'I', '207': 'I', '206': 'I', '242': 'o', '220': 'U',
'245': 'o', '244': 'o', '246': 'o', '241': 'n', '218': 'U', '229': 'a',
'228': 'a', '227': 'a', '226': 'a', '225': 'a', '219': 'U', '221': 'Y',
# these are added
'248': 'r', '185': 's', '174': 'Z', '232': 'c', '200': 'C', '169': 'S', '190': 'z',
'199': 'C', 'amp': '&', 'nbsp': ' ', 'quot': '\"'
}
def __init__(self, xmlcompatibility='db09'):
self.seqwrapper = HTMLSequenceWrapper(childcoef=7.0, headercoef=3.0, mintextlen=30)
self.citaextractor = CitationEntityExtractor(ALL, xmlcompatibility=xmlcompatibility)
self.ee = EntityExtractor()
self.mime = MIMEhandler()
self.crawler = Crawler()
self.bibtex = BibTeXParser()
self.xmlcompatibility = xmlcompatibility
self._xmlvalid = int(xmlcompatibility.lstrip('db'))
self._publ_list = []
def _set_new_topic(self, publ, kw):
"""
This method adds new topic to publication.
"""
if not re.search("[a-z]{4,}", kw):
return publ
if re.search("publi|paper", kw, re.I):
return publ
t = RRSTopic(title=kw)
publ.set('topic', t)
return publ
def _set_publ_type(self, header, publ):
def _floor(i):
if i > 100: i=100
return i
if header is None: return publ
# try to set publication type from header
for _type in RRSPublication.publication_types:
if re.search(_type, header, re.I):
if publ.get('type') == _type:
publ.set('credibility', _floor(publ.get('credibility')))
else:
publ.set('type', _type)
return publ
if re.search("dissertation", header, re.I):
publ.set('type', 'phdthesis')
return publ
if re.search('technical report', header, re.I):
publ.set('type', 'techreport')
return publ
# make keyword from header
return self._set_new_topic(publ, header)
def translate_html_entities(self, text):
ents = re.findall(r'&(#?)(x?)(\w+);', text)
for ent in set(ents):
try:
text = re.sub('&(#?)'+re.escape(ent[2])+";", self.entitydefstr[ent[2]], text)
except: pass
return text
def compare_chunks_to_extracted(self, chunks, publ):
if not publ.get('title'): return publ
title = self.translate_html_entities(publ.get('title'))
authors = publ.get('person_author')
author_names = [a.get('name')[0].get('full_name') for a in authors]
for ch in chunks:
l = ch.get_link()
# get chunk text
ch = self.translate_html_entities(ch.get_text())
# add url if available
if l is not None and not l.startswith("javascript") and l != "#":
u = RRSUrl(type='publication', title=ch, link=l)
publ.set('url', u)
# repair title if needed
if ch in title or ch == title:
if float(len(ch))/float(len(title)) > 0.4:
publ.set('title', ch)
# repair names if needed
for a in author_names:
if a in ch:
authors_extracted = self.ee.find_authors(ch)
publ.person_author = authors_extracted[0]
break
return publ
def _fill_citation(self, publ):
c = RRSCitation()
c.set('content', self.cita_text)
if publ.get('event'):
c.set('event', publ.get('event')[0].get('title'))
return c
def _handle_bibtex_pages(self):
urls = {}
for i, p in enumerate(self._publ_list):
pub_u = p.get('url')
for u in pub_u:
urls[u.get('link')] = i
#if link is web page, not pdf
urls_to_download = []
content_types = self.mime.start(urls.keys())
for k in urls.keys():
if content_types[k] in ('text/html', 'application/xhtml+xml',
'application/x-httpd-php', 'text/javascript'):
urls_to_download.append(k)
# download page a try it for bibtex
pages = self.crawler.start(urls_to_download)
for u in urls_to_download:
bibtex = self.bibtex.parse(pages[u])
# if bibtex on page, set publication
if bibtex is not None:
self._publ_list[urls[u]] = bibtex
def _empty(self):
for x in range(len(self._publ_list)):
self._publ_list.pop()
self.cita_text = None
def _handle_document(self, doc):
self._empty()
# for all regions which were found
for reg in doc.get_regions():
# get their header
header = reg.get_name()
# for all records in region
for rec in reg._manual_process_page():
# create empty citation object
c = RRSCitation()
# harvest citation record text (probably citation we hope)
self.cita_text = self.translate_html_entities(rec.get_text())
# set the content of record to citation object
c.set('content', self.cita_text)
# fill object it wih extracted data
c = self.citaextractor.extract(c)
# get extracted publication
publ = c.get('publication_cited')
# if sequencewrapper extracted come text chunks, it helps us a lot,
# beacause we can compare extracted data to chunks and if not matched
# we can fix it
publ = self.compare_chunks_to_extracted(rec.get_chunks(), publ)
# insert citation into publication
# !!! we are extracting publications, not citations. Because we dont
# want tree like this: citation->publication but this:
# publication->citation
publ.set('citation', self._fill_citation(publ))
# try to find publication type in header of data region
publ = self._set_publ_type(header, publ)
# add to publication list
self._publ_list.append(publ)
#self._handle_bibtex_pages()
return self._publ_list
#---------------------------------------------------------------------------
# public methods
#---------------------------------------------------------------------------
def extract_data(self, tree, url):
"""
Main method for extracting publication metadata from page.
"""
# wrap html document
document = self.seqwrapper.wrap_h(tree, url)
# handle it and return the result
return self._handle_document(document)
class PublicationPageExtractor(object):
"""
This class wraps all methods for recognition publication and it's description
on the web page.
The result of page-processing is ORM object rrslib.db.model.RRSPublication
with extracted data.
"""
# what could an abstract start with
_abstract_startswith = ('We present', 'This paper', 'This publica',
'In this p', 'The paper', 'This book')
_abstract_blacklist = ('CiteU', 'Was ', 'I ha', 'I pre', 'I was', 'CiteS',
'Microso')
# tags representing lists in HTML
_list_tags = ('ol', 'ul', 'dl')
# tags representing list items in HTML
_list_item_tags = ('li', 'dd', 'dt')
# ommitted tags - they are useless for this reason
_omitted_tags = ('form', 'option', 'link', 'style', 'meta', 'head',
'script')
# acceptable mime types for documents (publications)
_accepted_mime = ('application/pdf', 'application/rtf',
'application/postscript', 'application/msword')
def __init__(self, headercoef=2.5):
"""
Constructor.
@param headercoef: lower border of elemet's visibility to be handled as header
"""
self.generalizer = _RRSPropertyGeneralizer()
self.ee = EntityExtractor()
self.headercoef = headercoef
self.bibtexparser = BibTeXParser()
self.crawler = Crawler()
self.mime_handler = MIMEhandler()
self.crawler.set_handler(FileDownloader)
def _get_visibility2elem_map(self, etree):
htmlroot = etree.getroot()
visibility2elem_map = {}
for elem in htmlroot.iterdescendants():
# no need to get those
if elem.tag in self._omitted_tags: continue
assert hasattr(elem, 'style')
v = elem.style.get_visibility()
if v in visibility2elem_map:
visibility2elem_map[v].append(elem)
else:
visibility2elem_map[v] = [elem]
return visibility2elem_map
def _classify_publ_title(self, title, init=70):
def _bracket_alone(text):
if text is not None and re.search(" [\(\)\{\}\[\]] ", text):
return True
return False
# default credibility
cred = init
# remove prepositions and words with len(w) < 3
title = re.sub("(?<= )(?:[fF]or|[iI]n|[oO]f|[oO]n|[aA]t|[Tt]he|"\
"[Aa]nd|[aA]|[Ii]s|[Ww]e|[Tt]o)(?= )", "", title)
title = re.sub("^(?:A|The|In|Is|To) ", "", title)
title = re.sub("[ ]+", " ", title)
# split into chunks
title_sp = title.split(" ")
_bad = 0
# if there are many chunks (almost-words) with no meaning, reduce
# credibility value
_blacklisted = '(?<![a-z])(?:department|faculty|universtiy|society|lab|press|)(?![a-z]+)'
for chunk in title_sp:
if re.search(_blacklisted, chunk, re.I):
_bad += 1
elif re.search("[a-z]{3,}", chunk, re.I):
pass
else:
_bad += 1
# guess accuracy
negative = float(_bad) / float(len(title_sp))
cred = float(cred) - negative * 65
# bonus if all chunks are OK
if _bad == 0:
cred += 20
# if there in title is bracket alone, reduce credibility
if _bracket_alone(title):
cred -= 15
# floor
if cred > 100: cred = 100
if cred < 0: cred = 0
return cred
def _most_alike_term(self, term, target, threshold=0.3):
assert len(target) > 0
assert term is not None
l = ListedDict()
for t in target:
s = SequenceMatcher(None, term, t)
l[float(s.ratio())] = t
m = max(l)
if m < float(threshold):
return None
return l[m][0]
def _add_property(self, property, values):
# Add the property and its value into publication
# maybe it is not a property, but an entity
firstval = values[0]
if firstval is None: return
# First try to get attributes of the publication
if property in ('abstract', 'isbn', 'issn', 'volume', 'number',
'acronym', 'issn', 'note'):
_type = self._publ.__types__[property]
if _type is basestring:
_type = unicode
self._publ[property] = (_type)(firstval)
elif property == 'title':
self._publ[property] = firstval
# the origin is from meta-tag, so we are pretty sure about this information
self._publ['credibility'] = 95
elif property == 'publisher':
self._publ['publisher'] = RRSOrganization(title=firstval)
elif property == 'date':
r = self.ee.find_published_date(firstval)
if not r[0]: return
rrsdate = r[0][0]
for attr in ('year', 'month'):
if rrsdate.get(attr) is not None:
self._publ[attr] = rrsdate.get(attr)
elif property == 'type':
# choose better heuristics
publtype = self._most_alike_term(firstval, publication_types, 0.4)
if publtype is not None:
self._publ['type'] = RRSPublication_type(type=publtype)
elif property == 'pages':
if re.search("[0-9]+\-{1,2}[0-9]+", firstval):
self._publ['pages'] = firstval
elif property == 'start page':
if not re.search("^[0-9]+$", firstval): return
if 'end page' in self._storage and not 'pages' in self._publ:
self._publ['pages'] = "%s-%s" % (firstval,
self._storage['end page'][0])
else:
self._storage[property] = [firstval]
elif property == 'end page':
if not re.search("^[0-9]+$", firstval): return
if 'start page' in self._storage and not 'pages' in self._publ:
self._publ['pages'] = "%s-%s" % (
self._storage['start page'][0], firstval)
else:
self._storage[property] = [firstval]
# --------------------------------------------------
# Now other entities connected with the publiacation
# --------------------------------------------------
elif property == 'topic':
for topictitle in values:
rel = RRSRelationshipPublicationTopic()
rel.set_entity(RRSTopic(title=topictitle))
self._publ['topic'] = rel
elif property == 'url':
for link in values:
try:
rel = RRSRelationshipPublicationUrl()
u = RRSUrl(link=link)
except (RRSDatabaseAttributeError, RRSDatabaseEntityError,
RRSDatabaseValueError):
return
u['type'] = RRSUrl_type(type='publication')
rel.set_entity(u)
self._publ['url'] = rel
elif property == 'keywords':
for kw in values:
rel = RRSRelationshipPublicationKeyword()
rel.set_entity(RRSKeyword(title=kw))
self._publ['keyword'] = rel
elif property in ('author', 'editor'):
for person in values:
rel = RRSRelationshipPersonPublication()
rel['editor'] = property == 'editor'
r = self.ee.find_authors(person)
if not r[0]: return
rel.set_entity(r[0][0])
rel['author_rank'] = len(self._publ['person']) + 1
self._publ['person'] = rel
elif property == 'reviews':
# TODO if real reviews would be implemented in DB schema,
# change this: add RRSReview objects into publication
self._publ.set("review", values, strict=False)
def _parse_meta(self, document):
doc_meta = document.get_meta_map()
# transform into generalized form
for key in doc_meta:
property = self.generalizer.generalize(key)
if property is None: continue
if property in self._storage:
for val in doc_meta[key]:
if val not in self._storage[property]:
self._storage[property].append(val)
else:
self._storage[property] = doc_meta[key]
# make authors and editors disjoint sets
if 'author' in self._storage and 'editor' in self._storage:
to_del = []
for a in self._storage['author']:
if a in self._storage['editor']:
to_del.append(a)
for a in to_del:
self._storage['author'].remove(a)
# and now just set the values into real RRS objects
for property in self._storage:
self._add_property(property, self._storage[property])
self._storage = {}
def _find_local_sequence(self, header_elem, h_func):
# lightweight version of sequencewrapper (rrslib.web.sequencewrapper)
# targeted to repeated sequence of tags in one level of DOM - there's no
# analysis of data regions (we already found one), just looking for data
# records in a very straightforward (and very stupid) way.
# @param header_elem - lxml.html.HtmlElement representing header element
# @param h_func - heuristic function returning true/false, has to accept
# one parameter - element (instance of lxml.html.HtmlElement)
# @return tuple (records, likelihood of data)
tags = ListedDict()
for elem in header_elem.iterchildren():
if elem.tag in self._omitted_tags: continue
tags[elem.tag] = elem
(tag, elements) = tags.item_by_longest_value()
if len(elements[0]) < 2:
return (None, 0.0)
res = []
grouped_elements = []
for e_group in elements:
res.extend(filter(h_func, e_group))
grouped_elements.extend(e_group)
return (res, float(len(res)) / float(len(grouped_elements)))
def _get_data_below_header(self, elem, hdrtext, to_be_processed):
# Try to iter over siblings of the header element and get text
siblings = [sib.tag for sib in elem.itersiblings()]
# the header is abstract
if hdrtext == 'abstract':
txts = {}
paragraphs = []
par_stop = False
for sib in elem.itersiblings():
content = sib.text_content()
if sib in to_be_processed:
par_stop = True
if sib.tag == 'p' and len(content) > 50 and not par_stop:
paragraphs.append(content)
chunk = content[0:20].lower()
score = 1.0
for st in self._abstract_startswith:
if chunk.startswith(st): score *= 5.0
score *= len(content)
txts[score] = SimpleHTMLCleaner.clean(content)
if paragraphs:
self._storage[hdrtext] = [
SimpleHTMLCleaner.clean(" ".join(paragraphs))
]
else:
self._storage[hdrtext] = [txts[max(txts.keys())]]
# related publications
elif hdrtext == 'related':
list_tags = ('ul', 'ol', 'dl')
return # TODO
for ltag in list_tags:
if ltag in siblings:
for sib in elem.itersiblings():
pass
# keywords
elif hdrtext == 'keywords':
# create function returning elements containing possible keywords
is_keyword = lambda kw: re.search("^(([a-z]{3,}( |,)){1,3} ?)+([a-z]{3,} ?){1,3}$", \
kw.text_content(), re.I) \
and not re.search("[@#\$%\^&\*\(\)]", kw.text_content())
# iter over siblings of header a try to get keywords from its children
likelihood_to_keyword_tags = ListedDict()
for s in elem.itersiblings():
(kw_elems,
likelihood) = self._find_local_sequence(s, is_keyword)
if kw_elems is None: continue
likelihood_to_keyword_tags[likelihood] = kw_elems
if not likelihood_to_keyword_tags: return
# if found some keywords, store them
self._storage[hdrtext] = [
kw.text_content() for kw in likelihood_to_keyword_tags[max(
likelihood_to_keyword_tags.keys())][0]
]
# references
elif hdrtext == 'references':
pass # TODO
# chapters ??
elif hdrtext == 'chapters':
pass # TODO
# reviews?
elif hdrtext == 'reviews':
if hdrtext in self._storage: return
# create function returning elements containing possible reviews
is_review = lambda r: (len(r.text_content()) > 100
) or r.tag == 'blockquote'
probability = ListedDict()
# iter over siblings of header a try to get reviews from its children
for s in elem.itersiblings():
(elems, prob) = self._find_local_sequence(s, is_review)
if elems is None: continue
probability[prob] = elems
review_texts = []
if not probability: return
for e in probability[max(probability.keys())][0]:
review_texts.append(SimpleHTMLCleaner.clean(e.text_content()))
# set all the elements as "processed" to avoid further processing
for d in e.iter():
d.processed = True
self._storage[hdrtext] = review_texts
def _parse_visibility(self, document):
vis_map = self._get_visibility2elem_map(document.get_etree())
if len(vis_map) < 2: return
sorted_vis = sorted(vis_map.keys(), reverse=True)
if len(sorted_vis) < 2: return
to_be_processed = None
while 42: #:)
to_be_processed = []
for i in xrange(0, len(sorted_vis)):
if sorted_vis[i] < self.headercoef: continue
to_be_processed.extend(vis_map[sorted_vis[i]])
if len(to_be_processed) < 2:
self.headercoef -= 0.5
else:
break
# storage for possible titles
possible_titles = ListedDict()
# loop over all headers (elements containing very visible texts)
for elem in to_be_processed:
# get cleaned text content of the tag
txt = SimpleHTMLCleaner.clean(elem.text_content())
# generalize: maybe it is something useful
hdrtext = self.generalizer.generalize(txt)
# generalization found header beeing TITLE -> data are below header
if hdrtext is not None:
# found some useful header, try to get data below
# what is below? probably sibling tags and their descendants
self._get_data_below_header(elem, hdrtext, to_be_processed)
# generalization wasnt successful -> maybe the header contains data
else:
# date?
d = self.ee.find_published_date(txt)
if d[0]:
rrsdate = d[0][0]
for attr in ('year', 'month'):
if rrsdate.get(attr) is not None:
self._publ[attr] = rrsdate.get(attr)
txt = d[1]
# maybe title
if len(txt.split(" ")) > 3: # probably more than three words
# is there a domain name in the title? So it is probably
# general name of the website
if len(self.domain) > 6 and re.search(
re.escape(self.domain), txt, re.I):
continue
# preprocessing - remove standalone brackets
txt = re.sub("[\(\[][^\)\]]*[\)\]]+", "", txt).strip()
if document.name is not None and re.search(
re.escape(txt), document.name, re.I):
possible_titles[int(
self._classify_publ_title(txt, init=100))] = txt
elif len(txt.split(" ")) > 5:
possible_titles[int(
self._classify_publ_title(txt, init=60))] = txt
if possible_titles:
titles = possible_titles[max(possible_titles)]
if len(titles) > 1:
title = self._get_longest_string(titles)
else:
title = titles[0]
self._publ['title'] = title
self._publ['credibility'] = max(possible_titles)
else:
self._publ['credibility'] = 0
# store all new properties and their values
for property in self._storage:
self._add_property(property, self._storage[property])
def _get_longest_string(self, l):
mx = None
maxlen = 0
for t in l:
if len(t) > maxlen:
maxlen = len(t)
mx = t
return mx
def _find_abstract(self, etree):
c = Cleaner(scripts=True,
javascript=True,
comments=True,
style=True,
meta=True,
page_structure=False,
processing_instructions=True,
embedded=True,
frames=False,
forms=True,
annoying_tags=True,
add_nofollow=False,
remove_unknown_tags=False)
etree_copy = deepcopy(etree)
etree_copy = c.clean_html(etree_copy)
html = tostring(etree_copy.getroot())
# XXX this may be probably useful, to delete all <p> tags...
html = re.sub("</?p[^>]*>", " ", html)
possible = []
txts = re.findall("(?<=\>)[^>]+(?=\<)", html, re.U)
for txt in txts:
txt = SimpleHTMLCleaner.clean(txt)
if len(txt) > 200:
do_not_append = False
for bl in self._abstract_blacklist:
if txt.startswith(bl):
do_not_append = True
break
if not do_not_append:
possible.append(txt)
continue
for st in self._abstract_startswith:
if txt.startswith(st):
possible.append(txt)
break
return self._get_longest_string(possible)
def _find_unbound_entities(self, page):
root = page.get_etree().getroot()
# get abstract
if not 'abstract' in self._publ:
abst = self._find_abstract(page.get_etree())
if abst is not None:
self._publ['abstract'] = abst
# find url of publication (pdf, ps, doc...)
if 'url' not in self._publ:
to_be_checked = []
for (element, attribute, link, pos) in root.iterlinks():
if re.search("(pdf|doc|odt|ps)$", link, re.I):
to_be_checked.append(link)
# TODO try to get links with no suffix (queries etc.)
# ----------------------------------------------------
# ADD THE CODE HERE
# ----------------------------------------------------
if to_be_checked:
documents = []
mimes = self.mime_handler.start(to_be_checked)
for link in mimes:
if mimes[link] in self._accepted_mime:
documents.append(link)
dl = len(documents)
doc_link = None
if dl == 1: # exactly one link
doc_link = documents[0]
elif dl != 0: # more than one
# try to guess out of the name of the publication
if 'title' in self._publ:
doc_link = self._most_alike_term(
self._publ['title'], documents, 0.5)
if doc_link is not None:
try:
rel = RRSRelationshipPublicationUrl()
u = RRSUrl(link=doc_link)
except (RRSDatabaseAttributeError, RRSDatabaseEntityError,
RRSDatabaseValueError):
return
u['type'] = RRSUrl_type(type='publication')
rel.set_entity(u)
self._publ['url'] = rel
# Now extract unbound entities from the plaintext.
# Every time there is a high probability that the relationship will be
# miss-recognitized.
# get keywords if there are no such
if not 'keyword' in self._publ:
# Try to get keywords from the text. They are probably in the format:
# Keywords: algorithm, algorithmic process, random, some other keyword
kwre = re.search(
"keywords?:?[\t\n\r ]+(([a-z]{3,}( |,)){1,3} ?)+([a-z]{3,} ?){1,3}",
self.pagetext, re.I)
if kwre is not None:
kwstr = kwre.group(0)
kwstr = re.sub("[kK]eywords?:?[\t\n\r ]+", "", kwstr)
keywords = [x.strip() for x in kwstr.split(",")]
for kw in keywords:
rel = RRSRelationshipPublicationKeyword()
rel.set_entity(RRSKeyword(title=kw))
self._publ['keyword'] = rel
def _parse_bibtex(self, page):
# Parse all possible bibtex on the page.
# At first parse plaintext of the page and try to get BibTeX string out
# of there. Then try to find possible links fo bibtex files, download
# them and parse them.
bibtexpubl = self.bibtexparser.parse(self.pagetext)
if bibtexpubl:
pass
# try to get .bib or .bibtex files from the page
else:
html_tag = page.get_etree().getroot()
bibtex_links = set()
for l in html_tag.iterlinks():
if re.search("\.bib(tex)?$", l[2], re.I):
bibtex_links.add(l[2])
if len(bibtex_links) == 1:
r = self.crawler.start(bibtex_links)
for link in bibtex_links:
bibtex_file = r[link]
if isinstance(bibtex_file, basestring):
bibtexpubl = self.bibtexparser.parse(bibtex_file)
else:
return
else:
# TODO handle more than one bibtex files???
return
# process found bibtex
publ = bibtexpubl[0]
for attr in publ:
value = publ[attr]
# not set, useless
if value is None:
continue
# list of relationship attrs
elif isinstance(value, list):
for v in value:
self._publ[attr] = v
# own attribute
else:
self._publ[attr] = value
def extract_data(self, etree, url):
"""
Extract all possible data about the publication from the web page.
@param etree - parsed DOM tree of the web page (has to be instance of
lxml.etree._ElementTree)
@param url - url of the web page
@return RRSPublication object containing extracted data
"""
assert isinstance(url, basestring)
assert isinstance(etree, _ElementTree)
#c = Cleaner(scripts=True, javascript=True, comments=True, style=False,
# meta=False, page_structure=False, processing_instructions=True,
# embedded=True, frames=False, forms=True, annoying_tags=False,
# add_nofollow=False, remove_unknown_tags=False)
#etree = c.clean_html(etree)
self.url = url
self.domain = re.sub("http://(www)?", "", self.url).split(".")[0]
self._storage = {}
self._publ = RRSPublication()
cleaned_etree = SimpleHTMLCleaner.clean_html(etree)
page = HTMLDocument(cleaned_etree, url)
self.pagetext = page.get_etree().getroot().text_content()
# parse CSS and metadata on the page
page.parse_document()
# get data from <meta> tags nad convert to RRS format
self._parse_meta(page)
# get data on the basis of the text visbility and recognized headers
self._parse_visibility(page)
# and now guess :)
self._find_unbound_entities(page)
# and parse BibTeX
self._parse_bibtex(page)
return self._publ
class PublicationPageExtractor(object):
"""
This class wraps all methods for recognition publication and it's description
on the web page.
The result of page-processing is ORM object rrslib.db.model.RRSPublication
with extracted data.
"""
# what could an abstract start with
_abstract_startswith = ('We present', 'This paper', 'This publica',
'In this p', 'The paper', 'This book')
_abstract_blacklist = ('CiteU', 'Was ', 'I ha', 'I pre', 'I was', 'CiteS',
'Microso')
# tags representing lists in HTML
_list_tags = ('ol', 'ul', 'dl')
# tags representing list items in HTML
_list_item_tags = ('li', 'dd', 'dt')
# ommitted tags - they are useless for this reason
_omitted_tags = ('form', 'option', 'link', 'style', 'meta', 'head', 'script')
# acceptable mime types for documents (publications)
_accepted_mime = ('application/pdf', 'application/rtf', 'application/postscript', 'application/msword')
def __init__(self, headercoef=2.5):
"""
Constructor.
@param headercoef: lower border of elemet's visibility to be handled as header
"""
self.generalizer = _RRSPropertyGeneralizer()
self.ee = EntityExtractor()
self.headercoef = headercoef
self.bibtexparser = BibTeXParser()
self.crawler = Crawler()
self.mime_handler = MIMEhandler()
self.crawler.set_handler(FileDownloader)
def _get_visibility2elem_map(self, etree):
htmlroot = etree.getroot()
visibility2elem_map = {}
for elem in htmlroot.iterdescendants():
# no need to get those
if elem.tag in self._omitted_tags: continue
assert hasattr(elem, 'style')
v = elem.style.get_visibility()
if v in visibility2elem_map:
visibility2elem_map[v].append(elem)
else:
visibility2elem_map[v] = [elem]
return visibility2elem_map
def _classify_publ_title(self, title, init=70):
def _bracket_alone(text):
if text is not None and re.search(" [\(\)\{\}\[\]] ", text):
return True
return False
# default credibility
cred = init
# remove prepositions and words with len(w) < 3
title = re.sub("(?<= )(?:[fF]or|[iI]n|[oO]f|[oO]n|[aA]t|[Tt]he|"\
"[Aa]nd|[aA]|[Ii]s|[Ww]e|[Tt]o)(?= )", "", title)
title = re.sub("^(?:A|The|In|Is|To) ", "", title)
title = re.sub("[ ]+", " ", title)
# split into chunks
title_sp = title.split(" ")
_bad = 0
# if there are many chunks (almost-words) with no meaning, reduce
# credibility value
_blacklisted = '(?<![a-z])(?:department|faculty|universtiy|society|lab|press|)(?![a-z]+)'
for chunk in title_sp:
if re.search(_blacklisted, chunk, re.I):
_bad += 1
elif re.search("[a-z]{3,}", chunk, re.I):
pass
else:
_bad += 1
# guess accuracy
negative = float(_bad) / float(len(title_sp))
cred = float(cred) - negative * 65
# bonus if all chunks are OK
if _bad == 0:
cred += 20
# if there in title is bracket alone, reduce credibility
if _bracket_alone(title):
cred -= 15
# floor
if cred > 100: cred = 100
if cred < 0: cred = 0
return cred
def _most_alike_term(self, term, target, threshold=0.3):
assert len(target) > 0
assert term is not None
l = ListedDict()
for t in target:
s = SequenceMatcher(None, term, t)
l[float(s.ratio())] = t
m = max(l)
if m < float(threshold):
return None
return l[m][0]
def _add_property(self, property, values):
# Add the property and its value into publication
# maybe it is not a property, but an entity
firstval = values[0]
if firstval is None: return
# First try to get attributes of the publication
if property in ('abstract', 'isbn', 'issn', 'volume',
'number', 'acronym', 'issn', 'note'):
_type = self._publ.__types__[property]
if _type is basestring:
_type = unicode
self._publ[property] = (_type)(firstval)
elif property == 'title':
self._publ[property] = firstval
# the origin is from meta-tag, so we are pretty sure about this information
self._publ['credibility'] = 95
elif property == 'publisher':
self._publ['publisher'] = RRSOrganization(title=firstval)
elif property == 'date':
r = self.ee.find_published_date(firstval)
if not r[0]: return
rrsdate = r[0][0]
for attr in ('year', 'month'):
if rrsdate.get(attr) is not None:
self._publ[attr] = rrsdate.get(attr)
elif property == 'type':
# choose better heuristics
publtype = self._most_alike_term(firstval, publication_types, 0.4)
if publtype is not None:
self._publ['type'] = RRSPublication_type(type=publtype)
elif property == 'pages':
if re.search("[0-9]+\-{1,2}[0-9]+", firstval):
self._publ['pages'] = firstval
elif property == 'start page':
if not re.search("^[0-9]+$", firstval): return
if 'end page' in self._storage and not 'pages' in self._publ:
self._publ['pages'] = "%s-%s" % (firstval, self._storage['end page'][0])
else:
self._storage[property] = [firstval]
elif property == 'end page':
if not re.search("^[0-9]+$", firstval): return
if 'start page' in self._storage and not 'pages' in self._publ:
self._publ['pages'] = "%s-%s" % (self._storage['start page'][0], firstval)
else:
self._storage[property] = [firstval]
# --------------------------------------------------
# Now other entities connected with the publiacation
# --------------------------------------------------
elif property == 'topic':
for topictitle in values:
rel = RRSRelationshipPublicationTopic()
rel.set_entity(RRSTopic(title=topictitle))
self._publ['topic'] = rel
elif property == 'url':
for link in values:
try:
rel = RRSRelationshipPublicationUrl()
u = RRSUrl(link=link)
except (RRSDatabaseAttributeError, RRSDatabaseEntityError, RRSDatabaseValueError):
return
u['type'] = RRSUrl_type(type='publication')
rel.set_entity(u)
self._publ['url'] = rel
elif property == 'keywords':
for kw in values:
rel = RRSRelationshipPublicationKeyword()
rel.set_entity(RRSKeyword(title=kw))
self._publ['keyword'] = rel
elif property in ('author', 'editor'):
for person in values:
rel = RRSRelationshipPersonPublication()
rel['editor'] = property == 'editor'
r = self.ee.find_authors(person)
if not r[0]: return
rel.set_entity(r[0][0])
rel['author_rank'] = len(self._publ['person']) + 1
self._publ['person'] = rel
elif property == 'reviews':
# TODO if real reviews would be implemented in DB schema,
# change this: add RRSReview objects into publication
self._publ.set("review", values, strict=False)
def _parse_meta(self, document):
doc_meta = document.get_meta_map()
# transform into generalized form
for key in doc_meta:
property = self.generalizer.generalize(key)
if property is None: continue
if property in self._storage:
for val in doc_meta[key]:
if val not in self._storage[property]:
self._storage[property].append(val)
else:
self._storage[property] = doc_meta[key]
# make authors and editors disjoint sets
if 'author' in self._storage and 'editor' in self._storage:
to_del = []
for a in self._storage['author']:
if a in self._storage['editor']:
to_del.append(a)
for a in to_del:
self._storage['author'].remove(a)
# and now just set the values into real RRS objects
for property in self._storage:
self._add_property(property, self._storage[property])
self._storage= {}
def _find_local_sequence(self, header_elem, h_func):
# lightweight version of sequencewrapper (rrslib.web.sequencewrapper)
# targeted to repeated sequence of tags in one level of DOM - there's no
# analysis of data regions (we already found one), just looking for data
# records in a very straightforward (and very stupid) way.
# @param header_elem - lxml.html.HtmlElement representing header element
# @param h_func - heuristic function returning true/false, has to accept
# one parameter - element (instance of lxml.html.HtmlElement)
# @return tuple (records, likelihood of data)
tags = ListedDict()
for elem in header_elem.iterchildren():
if elem.tag in self._omitted_tags: continue
tags[elem.tag] = elem
(tag, elements) = tags.item_by_longest_value()
if len(elements[0]) < 2:
return (None, 0.0)
res = []
grouped_elements = []
for e_group in elements:
res.extend(filter(h_func, e_group))
grouped_elements.extend(e_group)
return (res, float(len(res))/float(len(grouped_elements)))
def _get_data_below_header(self, elem, hdrtext, to_be_processed):
# Try to iter over siblings of the header element and get text
siblings = [sib.tag for sib in elem.itersiblings()]
# the header is abstract
if hdrtext == 'abstract':
txts = {}
paragraphs = []
par_stop = False
for sib in elem.itersiblings():
content = sib.text_content()
if sib in to_be_processed:
par_stop = True
if sib.tag == 'p' and len(content) > 50 and not par_stop:
paragraphs.append(content)
chunk = content[0:20].lower()
score = 1.0
for st in self._abstract_startswith:
if chunk.startswith(st): score*=5.0
score *= len(content)
txts[score] = SimpleHTMLCleaner.clean(content)
if paragraphs:
self._storage[hdrtext] = [SimpleHTMLCleaner.clean(" ".join(paragraphs))]
else:
self._storage[hdrtext] = [ txts[max(txts.keys())] ]
# related publications
elif hdrtext == 'related':
list_tags = ('ul', 'ol', 'dl')
return # TODO
for ltag in list_tags:
if ltag in siblings:
for sib in elem.itersiblings(): pass
# keywords
elif hdrtext == 'keywords':
# create function returning elements containing possible keywords
is_keyword = lambda kw: re.search("^(([a-z]{3,}( |,)){1,3} ?)+([a-z]{3,} ?){1,3}$", \
kw.text_content(), re.I) \
and not re.search("[@#\$%\^&\*\(\)]", kw.text_content())
# iter over siblings of header a try to get keywords from its children
likelihood_to_keyword_tags = ListedDict()
for s in elem.itersiblings():
(kw_elems, likelihood) = self._find_local_sequence(s, is_keyword)
if kw_elems is None: continue
likelihood_to_keyword_tags[likelihood] = kw_elems
if not likelihood_to_keyword_tags: return
# if found some keywords, store them
self._storage[hdrtext] = [kw.text_content() for kw in likelihood_to_keyword_tags[max(likelihood_to_keyword_tags.keys())][0]]
# references
elif hdrtext == 'references':
pass # TODO
# chapters ??
elif hdrtext == 'chapters':
pass # TODO
# reviews?
elif hdrtext == 'reviews':
if hdrtext in self._storage: return
# create function returning elements containing possible reviews
is_review = lambda r: (len(r.text_content()) > 100) or r.tag == 'blockquote'
probability = ListedDict()
# iter over siblings of header a try to get reviews from its children
for s in elem.itersiblings():
(elems, prob) = self._find_local_sequence(s, is_review)
if elems is None: continue
probability[prob] = elems
review_texts = []
if not probability: return
for e in probability[max(probability.keys())][0]:
review_texts.append(SimpleHTMLCleaner.clean(e.text_content()))
# set all the elements as "processed" to avoid further processing
for d in e.iter():
d.processed = True
self._storage[hdrtext] = review_texts
def _parse_visibility(self, document):
vis_map = self._get_visibility2elem_map(document.get_etree())
if len(vis_map) < 2: return
sorted_vis = sorted(vis_map.keys(), reverse=True)
if len(sorted_vis) < 2: return
to_be_processed = None
while 42: #:)
to_be_processed = []
for i in xrange(0, len(sorted_vis)):
if sorted_vis[i] < self.headercoef: continue
to_be_processed.extend(vis_map[sorted_vis[i]])
if len(to_be_processed) < 2:
self.headercoef -= 0.5
else: break
# storage for possible titles
possible_titles = ListedDict()
# loop over all headers (elements containing very visible texts)
for elem in to_be_processed:
# get cleaned text content of the tag
txt = SimpleHTMLCleaner.clean( elem.text_content() )
# generalize: maybe it is something useful
hdrtext = self.generalizer.generalize(txt)
# generalization found header beeing TITLE -> data are below header
if hdrtext is not None:
# found some useful header, try to get data below
# what is below? probably sibling tags and their descendants
self._get_data_below_header(elem, hdrtext, to_be_processed)
# generalization wasnt successful -> maybe the header contains data
else:
# date?
d = self.ee.find_published_date(txt)
if d[0]:
rrsdate = d[0][0]
for attr in ('year', 'month'):
if rrsdate.get(attr) is not None:
self._publ[attr] = rrsdate.get(attr)
txt = d[1]
# maybe title
if len(txt.split(" ")) > 3: # probably more than three words
# is there a domain name in the title? So it is probably
# general name of the website
if len(self.domain) > 6 and re.search(re.escape(self.domain), txt, re.I):
continue
# preprocessing - remove standalone brackets
txt = re.sub("[\(\[][^\)\]]*[\)\]]+", "", txt).strip()
if document.name is not None and re.search(re.escape(txt), document.name, re.I):
possible_titles[int(self._classify_publ_title(txt, init=100))] = txt
elif len(txt.split(" ")) > 5:
possible_titles[int(self._classify_publ_title(txt, init=60))] = txt
if possible_titles:
titles = possible_titles[max(possible_titles)]
if len(titles) > 1:
title = self._get_longest_string(titles)
else:
title = titles[0]
self._publ['title'] = title
self._publ['credibility'] = max(possible_titles)
else:
self._publ['credibility'] = 0
# store all new properties and their values
for property in self._storage:
self._add_property(property, self._storage[property])
def _get_longest_string(self, l):
mx = None
maxlen = 0
for t in l:
if len(t) > maxlen:
maxlen = len(t)
mx = t
return mx
def _find_abstract(self, etree):
c = Cleaner(scripts=True, javascript=True, comments=True, style=True,
meta=True, page_structure=False, processing_instructions=True,
embedded=True, frames=False, forms=True, annoying_tags=True,
add_nofollow=False, remove_unknown_tags=False)
etree_copy = deepcopy(etree)
etree_copy = c.clean_html(etree_copy)
html = tostring(etree_copy.getroot())
# XXX this may be probably useful, to delete all <p> tags...
html = re.sub("</?p[^>]*>", " ", html)
possible = []
txts = re.findall("(?<=\>)[^>]+(?=\<)", html, re.U)
for txt in txts:
txt = SimpleHTMLCleaner.clean(txt)
if len(txt) > 200:
do_not_append = False
for bl in self._abstract_blacklist:
if txt.startswith(bl):
do_not_append = True
break
if not do_not_append:
possible.append(txt)
continue
for st in self._abstract_startswith:
if txt.startswith(st):
possible.append(txt)
break
return self._get_longest_string(possible)
def _find_unbound_entities(self, page):
root = page.get_etree().getroot()
# get abstract
if not 'abstract' in self._publ:
abst = self._find_abstract(page.get_etree())
if abst is not None:
self._publ['abstract'] = abst
# find url of publication (pdf, ps, doc...)
if 'url' not in self._publ:
to_be_checked = []
for (element, attribute, link, pos) in root.iterlinks():
if re.search("(pdf|doc|odt|ps)$", link, re.I):
to_be_checked.append(link)
# TODO try to get links with no suffix (queries etc.)
# ----------------------------------------------------
# ADD THE CODE HERE
# ----------------------------------------------------
if to_be_checked:
documents = []
mimes = self.mime_handler.start(to_be_checked)
for link in mimes:
if mimes[link] in self._accepted_mime:
documents.append(link)
dl = len(documents)
doc_link = None
if dl == 1: # exactly one link
doc_link = documents[0]
elif dl != 0: # more than one
# try to guess out of the name of the publication
if 'title' in self._publ:
doc_link = self._most_alike_term(self._publ['title'], documents, 0.5)
if doc_link is not None:
try:
rel = RRSRelationshipPublicationUrl()
u = RRSUrl(link=doc_link)
except (RRSDatabaseAttributeError, RRSDatabaseEntityError, RRSDatabaseValueError):
return
u['type'] = RRSUrl_type(type='publication')
rel.set_entity(u)
self._publ['url'] = rel
# Now extract unbound entities from the plaintext.
# Every time there is a high probability that the relationship will be
# miss-recognitized.
# get keywords if there are no such
if not 'keyword' in self._publ:
# Try to get keywords from the text. They are probably in the format:
# Keywords: algorithm, algorithmic process, random, some other keyword
kwre = re.search("keywords?:?[\t\n\r ]+(([a-z]{3,}( |,)){1,3} ?)+([a-z]{3,} ?){1,3}", self.pagetext, re.I)
if kwre is not None:
kwstr = kwre.group(0)
kwstr = re.sub("[kK]eywords?:?[\t\n\r ]+", "", kwstr)
keywords = [x.strip() for x in kwstr.split(",")]
for kw in keywords:
rel = RRSRelationshipPublicationKeyword()
rel.set_entity(RRSKeyword(title=kw))
self._publ['keyword'] = rel
def _parse_bibtex(self, page):
# Parse all possible bibtex on the page.
# At first parse plaintext of the page and try to get BibTeX string out
# of there. Then try to find possible links fo bibtex files, download
# them and parse them.
bibtexpubl = self.bibtexparser.parse(self.pagetext)
if bibtexpubl:
pass
# try to get .bib or .bibtex files from the page
else:
html_tag = page.get_etree().getroot()
bibtex_links = set()
for l in html_tag.iterlinks():
if re.search("\.bib(tex)?$", l[2], re.I):
bibtex_links.add(l[2])
if len(bibtex_links) == 1:
r = self.crawler.start(bibtex_links)
for link in bibtex_links:
bibtex_file = r[link]
if isinstance(bibtex_file, basestring):
bibtexpubl = self.bibtexparser.parse(bibtex_file)
else:
return
else:
# TODO handle more than one bibtex files???
return
# process found bibtex
publ = bibtexpubl[0]
for attr in publ:
value = publ[attr]
# not set, useless
if value is None:
continue
# list of relationship attrs
elif isinstance(value, list):
for v in value:
self._publ[attr] = v
# own attribute
else:
self._publ[attr] = value
def extract_data(self, etree, url):
"""
Extract all possible data about the publication from the web page.
@param etree - parsed DOM tree of the web page (has to be instance of
lxml.etree._ElementTree)
@param url - url of the web page
@return RRSPublication object containing extracted data
"""
assert isinstance(url, basestring)
assert isinstance(etree, _ElementTree)
#c = Cleaner(scripts=True, javascript=True, comments=True, style=False,
# meta=False, page_structure=False, processing_instructions=True,
# embedded=True, frames=False, forms=True, annoying_tags=False,
# add_nofollow=False, remove_unknown_tags=False)
#etree = c.clean_html(etree)
self.url = url
self.domain = re.sub("http://(www)?", "", self.url).split(".")[0]
self._storage= {}
self._publ = RRSPublication()
cleaned_etree = SimpleHTMLCleaner.clean_html(etree)
page = HTMLDocument(cleaned_etree, url)
self.pagetext = page.get_etree().getroot().text_content()
# parse CSS and metadata on the page
page.parse_document()
# get data from <meta> tags nad convert to RRS format
self._parse_meta(page)
# get data on the basis of the text visbility and recognized headers
self._parse_visibility(page)
# and now guess :)
self._find_unbound_entities(page)
# and parse BibTeX
self._parse_bibtex(page)
return self._publ