def born_date_and_death_in_parenthesis(Subject, Object):
"""
Ex: Carl Bridgewater (January 2, 1965 - September 19, 1978) was shot dead
"""
anything = Star(Any())
return Subject + Pos("-LRB-") + Object + Token("-") + anything + Pos(
"-RRB-") + anything
def was_born_explicit_mention(Subject, Object):
"""
Ex: Shamsher M. Chowdhury was born in 1950.
"""
anything = Star(Any())
return anything + Subject + Token("was born") + Pos(
"IN") + Object + anything
def mentions_real_name(Subject, Object):
"""
Ex: Harry Pilling, born Ashtonunder-Lyne, Lancashire on 2 February 1943, played ...
"""
anything = Star(Any())
real_name = Plus(Pos("NNP") + Question(Token(",")))
return Subject + Token("born") + real_name + Pos("IN") + Object + anything
def was_born_and_mentions_place_2(Subject, Object):
"""
Ex: Theodone C. Hu was born in 1872 in Huangpu town, Haizhu District, Guangzhou, Guangdong, China.
"""
anything = Star(Any())
place = Plus(Pos("NNP") + Question(Token(",")))
return Subject + Token("was born") + Pos("IN") + Object + Pos(
"IN") + place + anything
def company_relationship(Subject, Object):
"""
Ex: Gary Sykes (Born 13 February 1984) is a British super featherweight boxer.
"""
anything = Star(Any())
born = Star(Pos(":")) + Question(Token("Bought") | Token("buy")) + Question(Token("c."))
entity_leftover = Star(Pos("NNP"))
return Subject + entity_leftover + Pos("-LRB-") + born + Object + Pos("-RRB-") + anything
def incorrect_labeling_of_place_as_person(Subject, Object):
"""
Ex: Sophie Christiane of Wolfstein (24 October 24, 1667 - 23 August 1737)
Wolfstein is a *place*, not a *person*
"""
anything = Star(Any())
person = Plus(Pos("NNP") + Question(Token(",")))
return anything + person + Token("of") + Subject + anything
def born_date_in_parenthesis(Subject, Object):
"""
Ex: Gary Sykes (Born 13 February 1984) is a British super featherweight boxer.
"""
anything = Star(Any())
born = Star(Pos(":")) + Question(Token("Born") | Token("born")) + Question(
Token("c."))
entity_leftover = Star(Pos("NNP"))
return Subject + entity_leftover + Pos("-LRB-") + born + Object + Pos(
"-RRB-") + anything
def born_two_dates_in_parenthesis(Subject, Object):
"""
Ex: James Cunningham (born 1973 or 1974) is a Canadian stand-up comedian and TV host.
"""
anything = Star(Any())
born = Question(Token("Born") | Token("born"))
entity_leftover = Star(Pos("NNP"))
subject = Subject + entity_leftover
or_object = (Object + Token("or") + Pos("CD")
| Pos("CD") + Token("or") + Object)
return subject + Pos("-LRB-") + born + or_object + Pos("-RRB-") + anything
def customize_rules():
# some rules for matching
# TODO: customize your own rules here
person = (W(pos="nr") | W(pos="x") | W(pos="nrt") | W(pos="nz"))
movie = (W(pos="nz"))
place = (W("出生地") | W("出生"))
intro = (W("简介") | W(pos="介绍"))
rules = [
Rule(condition=W(pos="r") + W("是") + person | \
person + W("是") + W(pos="r"),
action=who_is_question),
Rule(condition=person + Star(Any(), greedy=False) + place + Star(Any(), greedy=False),
action=where_is_from_question),
Rule(condition=movie + Star(Any(), greedy=False) + intro + Star(Any(), greedy=False) ,
action=movie_intro_question)
]
return rules
class LowMatchAny(RegexTemplate):
weight = 0.5
regex = Star(Any())
def semantics(self, match):
expr = None
for word in match.words:
if expr is not None:
expr += HasKeyword(word.token)
else:
expr = HasKeyword(word.token)
return expr
class LowMatchAny(QuestionTemplate):
weight = 0.5
regex = Star(Any())
def interpret(self, match):
expr = None
for word in match.words:
if expr is not None:
expr += HasKeyword(word.token)
else:
expr = HasKeyword(word.token)
return expr
class RegexTemplate(object):
"""
Regex base.
Subclass from this to implement a regex handler.
"""
regex = Star(Any()) # Must define when subclassing
weight = 1 # Redefine this to give different priorities to your regexes.
def semantics(self, match):
"""
Returns the semantics of the regex.
`match` is of type `quepy.regex.Match` and is analogous to a python re
match. It contains matched groups in the regular expression.
When implementing a regex one must fill this method.
"""
raise NotImplementedError()
def get_semantics(self, words):
rulename = self.__class__.__name__
logger.debug("Trying to match with regex: {}".format(rulename))
match = refo.match(self.regex + Literal(_EOL), words + [_EOL])
if not match:
logger.debug("No match")
return None, None
try:
match = Match(match, words)
result = self.semantics(match)
except BadSemantic as error:
logger.debug(str(error))
return None, None
try:
expression, userdata = result
except TypeError:
expression, userdata = result, None
expression.rule_used = rulename
return expression, userdata
for w in word_objects:
if w.pos == pos_book_or_movie:
e_douban = u"?m :movie_info_name '{movie}'.\n" \
u"?m :has_director ?a.\n" \
u"?a :movie_person_name ?x".format(movie=w.token)
e_db = u"?m rdfs:label '{movie}'@en.\n" \
u"?m dbo:director ?p.\n" \
u"?p foaf:name ?x".format(movie=re.sub(r"(\w)([A-Z])", r"\1 \2", w.token))
sparql = SPARQL_SELECT_TEM_FD.format(prefix=SPARQL_PREFIX,
select=select,
expression_douban=e_douban,
expression_db=e_db)
break
return sparql
movie_info_rules_fd = [
Rule(condition_num=11, condition=book_or_movie_entity + Star(Any(), greedy=False) + movie_info + Star(Any(), greedy=False), action=QuestionSet.has_movie_info),
Rule(condition_num=11,condition=(book_or_movie_entity + Star(Any(), greedy=False) + actor + Star(Any(), greedy=False)) | (actor + Star(Any(), greedy=False) + book_or_movie_entity + Star(Any(), greedy=False)), action=QuestionSet.has_actor),
Rule(condition_num=11,condition=(book_or_movie_entity + Star(Any(), greedy=False) + writer + Star(Any(), greedy=False)) | (writer + Star(Any(), greedy=False) + book_or_movie_entity + Star(Any(), greedy=False)), action=QuestionSet.has_writer),
Rule(condition_num=11,condition=(book_or_movie_entity + Star(Any(), greedy=False) + director + Star(Any(), greedy=False)) | (writer + Star(Any(), greedy=False) + book_or_movie_entity) + Star(Any(), greedy=False), action=QuestionSet.has_director),
]
basic_movie_info_fd = [
KeywordRule(condition=(book_or_movie_entity + Star(Any(), greedy=False) + country + Star(Any(), greedy=False))|( country + Star(Any(), greedy=False) + book_or_movie_entity + Star(Any(), greedy=False)), action=MoviePropertyValueSet.return_movie_info_country_value_FD),
KeywordRule(condition=(book_or_movie_entity + Star(Any(), greedy=False) + language + Star(Any(), greedy=False))|( language + Star(Any(), greedy=False) + book_or_movie_entity + Star(Any(), greedy=False)), action=MoviePropertyValueSet.return_movie_info_language_value_FD),
KeywordRule(condition=(book_or_movie_entity + Star(Any(), greedy=False) + duration + Star(Any(), greedy=False))|( duration + Star(Any(), greedy=False) + book_or_movie_entity + Star(Any(), greedy=False)), action=MoviePropertyValueSet.return_movie_info_duration_value_FD),
KeywordRule(condition=(book_or_movie_entity + Star(Any(), greedy=False) + summary + Star(Any(), greedy=False))|( summary + Star(Any(), greedy=False) + book_or_movie_entity + Star(Any(), greedy=False)), action=MoviePropertyValueSet.return_movie_info_summary_value_FD),
]
def just_born(Subject, Object):
"""
Ex: Lyle Eugene Hollister, born 6 July 1923 in Sioux Falls, South Dakota, enlisted in the Navy....
"""
anything = Star(Any())
return Subject + Token(", born") + Object + anything
def is_born_in(Subject, Object):
"""
Ex: Xu is born in 1902 or 1903 in a family of farmers in Hubei ..
"""
anything = Star(Any())
return Subject + Token("is born in") + Object + anything
#普通名词
common_noun = W(pos=pos_common_noun)
#比较关键词
larger = (W("bigger") | W("larger"))
smaller = (W("smaller") | W("lower"))
compare = (larger | smaller)
rules = [
# What is the name of sb-uname？
# What is the age of sb-uname ?
# What is the username of sb-uname?
# What is the phone number of sb-uname?
# What is the password of sb-name?
Rule(condition_num=3,
condition=what + Star(Any(), greedy=False) + attr_noun +
Star(Any(), greedy=False) + of + common_noun +
Star(Any(), greedy=False),
action=QuestionSet.proccess_attr_noun),
# Whose age is larger than 18 ?
Rule(condition_num=4,
condition=whose + attr_noun + Star(Any(), greedy=False) + compare +
Star(Any(), greedy=False) + number_entity + Star(Any(), greedy=False),
action=QuestionSet.who_age_compare)
]
# 合并的好处是，减少上面的规则数，遍历更快，如果吧下面的放在上面，拿那么如果访问比较多最后一类，那效率就很低
attr_keyword_rules = [
KeywordRule(condition=what + Star(Any(), greedy=False) + name +
Star(Any(), greedy=False) + of + common_noun +
Star(Any(), greedy=False),
def apply(self, sentence):
matches = []
for m in finditer(self.condition, sentence):
i, j = m.span()
matches.extend(sentence[i:j])
return self.action(matches), self.condition_num
rules = [
# 【疾病】是什么？
Rule(
condition_num=1,
condition=Words.WORD_DISEASE + Words.WORD_IS + Words.WORD_ASK +
Star(Any(), greedy=False),
action=QuestionSet.disease_info,
),
# 【药品】是什么？
Rule(
condition_num=1,
condition=Words.WORD_MEDICINE + Words.WORD_IS + Words.WORD_ASK +
Star(Any(), greedy=False),
action=QuestionSet.medicine_info,
),
# 【疾病】怎么办？
Rule(
condition_num=1,
condition=Words.WORD_DISEASE + Star(Any(), greedy=False) +
Words.WORD_ASK + Star(Any(), greedy=False),
action=QuestionSet.medicine_for_disease,
# TODO 问题模板/匹配规则
"""
1. 某校区包含哪些校园（多少个）
2. 某校区/校园包含哪些学院（多少个）
3. 某校区/校园/学院包含哪些专业（多少个）
4. 某专业属于哪些（个）学院
5. 某专业/学院/栋楼属于哪些（个）校园
6. 某专业/学院/校园/栋楼属于哪些（个）校区
7. 某校园的通讯地址/面积
8. 某学院的通讯地址 || 某栋楼的地址/tag || 某校园/校区包含哪些楼
"""
rules = [
# 属于
Rule(condition_num=3,
condition=my_entity + Star(Any(), greedy=False) +
((belongTo + Star(Any(), greedy=False) + zone) |
(W("是") + Star(Any(), greedy=False) + zone +
Star(Any(), greedy=False) + W("的"))) + Star(Any(), greedy=False),
action=QuestionSet.belongTo_zone_question),
Rule(condition_num=3,
condition=my_entity + Star(Any(), greedy=False) +
((belongTo + Star(Any(), greedy=False) + campus) |
(W("是") + Star(Any(), greedy=False) + campus +
Star(Any(), greedy=False) + W("的"))) + Star(Any(), greedy=False),
action=QuestionSet.belongTo_campus_question),
Rule(condition_num=3,
condition=my_entity + Star(Any(), greedy=False) +
((belongTo + Star(Any(), greedy=False) + faculty) |
(W("是") + Star(Any(), greedy=False) + faculty +
Star(Any(), greedy=False) + W("的"))) + Star(Any(), greedy=False),
war_death = (W("战损") | W("死亡人数"))
when = (W("哪个地方") | W("何时") | W("什么时候"))
where = (W("在哪里") | W("哪儿") | W("何地") | W("何处") | W("在") + W("哪"))
# TODO 问题模板/匹配规则
"""
1. 某个国家打赢了什么战争
2. 某场战争有哪些参战者
3. 某国参加了多少战争
4. 某场战争在何时发生
5. 某场战争的战损是多少
"""
rules = [
Rule(condition_num=2,
condition=combatant_entity + Star(Any(), greedy=False) + war +
Star(Any(), greedy=False),
action=QuestionSet.participate_war),
Rule(condition_num=2,
condition=(war_entity + Star(Any(), greedy=False) + combatant +
Star(Any(), greedy=False)) |
(combatant + Star(Any(), greedy=False) + war_entity +
Star(Any(), greedy=False)),
action=QuestionSet.combatant_participate),
Rule(condition_num=3,
condition=combatant_entity + Star(Any(), greedy=False) + several +
Star(Any(), greedy=False) + (war | Star(Any(), greedy=False)),
action=QuestionSet.count_participated_wars),
Rule(condition_num=2,
condition=war_entity + Star(Any(), greedy=False) + when,
action=QuestionSet.war_take_place),
break
return sparql_list
# 问题模版, 匹配规则
"""
# 某书籍的图片/出版社/出版日期/页数/简介/目录/评分/评价人数
# 某书籍的类别
# 某书籍的作者
# 某书籍的译者
# 某书籍的详细信息
"""
book_info_rules = [
Rule(condition_num=3,
condition=book_or_movie_entity + Star(Any(), greedy=False) +
book_info + Star(Any(), greedy=False),
action=QuestionSet.has_book_info),
Rule(condition_num=3,
condition=book_or_movie_entity + Star(Any(), greedy=False) + genre +
Star(Any(), greedy=False),
action=QuestionSet.has_book_genre),
Rule(condition_num=3,
condition=book_or_movie_entity + Star(Any(), greedy=False) + author +
Star(Any(), greedy=False),
action=QuestionSet.has_author),
Rule(condition_num=3,
condition=book_or_movie_entity + Star(Any(), greedy=False) +
translator + Star(Any(), greedy=False),
action=QuestionSet.has_translator),
Rule(condition_num=3,
8. 某某人是哪个朝代的。
9. 某某人的出生日期。
10. 某某人的忌日。
11. 某某人有哪些职位。
12. 某某人在哪里当的某官。
13. 某某人在什么时候当的某官。
14. 某某人有什么作品。
15. 某某人的职业/身份。
16. 某某人怎么入仕的。
17. 和某某人拥有某关系的人有哪些。
18. 某某人和某某人是什么关系。
19. 某人认识哪些人。
"""
rules = [
Rule(condition_num=2,
condition=person + Star(Any(), greedy=False) + kinship_combination,
action=PersonalOtherInfoQuestionSet.has_kinship_question),
Rule(condition_num=3,
condition=person + Star(Any(), greedy=False) + several +
kinship_combination,
action=PersonalOtherInfoQuestionSet.has_kinship_number_question),
Rule(condition_num=2,
condition=person + Star(Any(), greedy=False) +
appellation_combination,
action=PersonalBasicInfoQuestionSet.has_appellation_question),
Rule(condition_num=3,
condition=person + Star(Any(), greedy=False) + beenTo +
Star(Any(), greedy=False) + (where | location),
action=PersonalOtherInfoQuestionSet.has_been_to_place_question),
Rule(condition_num=3,
condition=(person + Star(Any(), greedy=False) + place_combination +
ethnic = (W("族") | W("民族"))
# print W(pos="nr")
# print person
rules = [
Rule(condition=W(pos="r") + W("是") + person | \
person + W("是") + W(pos="r"),
action=who_is_question),
Rule(condition=person + W("来自") + Star(W("哪"), greedy=False),
action=where_is_from_question),
Rule(condition=person + Star(Any(), greedy=False) + ethnic,
action=whose_nationality_question)
]
# matching and querying
for seg in seg_lists:
# display question each
for s in seg:
print s.token
print
for rule in rules:
query = rule.apply(seg)
if query is None:
"""
1.某company的英文名 经营范围 公司类型 公司口号 年营业额 董事长 成立时间 总部地址 电话 网站
2.某company的成立时间[什么时候/何时成立?]
3.某person/company
4.某company提供/有 哪些类型的服务/服务的类型
5.某company 有哪些 XX 类型 的 服务 (有哪些)
6.某company提供/有 哪些服务
7.某company XX 服务
7. 某演员出演了多少部电影。
8. 某演员是喜剧演员吗。
"""
rules = [
Rule(condition_num=2,
condition=company_entity + Star(Any(), greedy=False) + company_basic +
Star(Any(), greedy=False),
action=QuestionSet.has_company_basicinfo_question),
Rule(condition_num=2,
condition=company_entity + Star(Any(), greedy=False) + when +
incorporate + Star(Any(), greedy=False),
action=QuestionSet.has_company_basicinfo_question),
Rule(condition_num=1,
condition=Star(Any(), greedy=False) +
(company_entity | person_entity) + Star(Any(), greedy=False),
action=QuestionSet.has_companyOrPerson_all_basicinfo_question),
#4
Rule(condition_num=3,
condition=company_entity + Star(Any(), greedy=False) +
(word_type + Star(Any(), greedy=False) + word_service
| word_service + Star(Any(), greedy=False) + word_type),
parser = argparse.ArgumentParser(description="Prints an xml document tree" +
"... sort of.")
parser.add_argument("filename", action="store")
cfg = parser.parse_args()
text = open(cfg.filename).read()
from refo import finditer, Predicate, Literal, Any, Group, Star
def notin(xs):
return lambda x: x not in xs
name = Predicate(notin("/")) + Star(Predicate(notin(" >")))
name = Group(name, "name")
inside = name + Star(Any(), greedy=False)
opentag = Literal("<") + inside + Literal(">")
opentag = Group(opentag, "open")
closetag = Literal("<") + Literal("/") + inside + Literal(">")
closetag = Group(closetag, "close")
regex = closetag | opentag
depth = 0
for m in finditer(regex, text):
if "open" in m:
i, j = m["name"]
print(" " * depth + text[i:j])
depth += 1
else:
assert "close" in m
depth -= 1
"default_field": "dynasty",
"query": w.token
}
}]
}
},
"size": 100
}
break
return query
rules = [
Rule(condition_num=1,
condition=((poet_entity + Star(Any(), greedy=False) + poem +
Star(Any(), greedy=False)) |
(poem + Star(Any(), greedy=False) + poet_entity +
Star(Any(), greedy=False))),
action=Question.poet_search_question),
Rule(condition_num=2,
condition=((poem_entity + Star(Any(), greedy=False) + poet +
Star(Any(), greedy=False)) |
(poet + Star(Any(), greedy=False) + poem_entity +
Star(Any(), greedy=False))),
action=Question.poem_search_question),
Rule(condition_num=3,
condition=(poet_entity + Star(Any(), greedy=False) + dynasty +
Star(Any(), greedy=False)),
action=Question.poet_search_question),
Rule(condition_num=4,
break
return sparql_list
# 问题模版, 匹配规则
"""
# 某书籍的图片/出版社/出版日期/页数/简介/目录/评分/评价人数
# 某书籍的类别
# 某书籍的作者
# 某书籍的译者
# 某书籍的详细信息
"""
book_info_rules = [
Rule(condition_num=3, condition=book_or_movie_entity + Star(Any(), greedy=False) + book_info + Star(Any(), greedy=False), action=QuestionSet.has_book_info),
Rule(condition_num=3, condition=book_or_movie_entity + Star(Any(), greedy=False) + genre + Star(Any(), greedy=False), action=QuestionSet.has_book_genre),
Rule(condition_num=3, condition=book_or_movie_entity + Star(Any(), greedy=False) + author + Star(Any(), greedy=False), action=QuestionSet.has_author),
Rule(condition_num=3, condition=book_or_movie_entity + Star(Any(), greedy=False) + translator + Star(Any(), greedy=False), action=QuestionSet.has_translator),
Rule(condition_num=3, condition=book_or_movie_entity + Star(Any(), greedy=False) + detail_information + Star(Any(), greedy=False), action=QuestionSet.has_detail_information)
]
basic_book_info = [
KeywordRule(condition=book_or_movie_entity + Star(Any(), greedy=False) + image_url + Star(Any(), greedy=False), action=BookPropertyValueSet.return_book_info_image_url_value),
KeywordRule(condition=book_or_movie_entity + Star(Any(), greedy=False) + press + Star(Any(), greedy=False),action=BookPropertyValueSet.return_book_info_press_value),
KeywordRule(condition=book_or_movie_entity + Star(Any(), greedy=False) + publish_year + Star(Any(), greedy=False),action=BookPropertyValueSet.return_book_info_publish_year_value),
KeywordRule(condition=book_or_movie_entity + Star(Any(), greedy=False) + page_num + Star(Any(), greedy=False),action=BookPropertyValueSet.return_book_info_page_num_value),
KeywordRule(condition=book_or_movie_entity + Star(Any(), greedy=False) + price + Star(Any(), greedy=False),action=BookPropertyValueSet.return_book_info_price_value),
KeywordRule(condition=book_or_movie_entity + Star(Any(), greedy=False) + content_abstract + Star(Any(), greedy=False),action=BookPropertyValueSet.return_book_info_content_abstract_value),
KeywordRule(condition=book_or_movie_entity + Star(Any(), greedy=False) + catalog + Star(Any(), greedy=False),action=BookPropertyValueSet.return_book_info_catalog_value),
KeywordRule(condition=book_or_movie_entity + Star(Any(), greedy=False) + rating + Star(Any(), greedy=False),action=BookPropertyValueSet.return_book_info_rating_value),
drug_to_person_entity = (W(pos=pos_drug_to_person))
case = (W("案件") | W("案子") | W("参案"))
person = (W("人") | W("人员") | W("被告人"))
cooperation = (W("同伙") | W("同伴"))
trade = (W("交易关系") | W("交易"))
drug = (W("毒品"))
several = (W("多少") | W("几次"))
seller = (W("贩毒人员"))
information = (W("基本信息") | W("基本资料"))
sell = (W("贩卖") | W("卖"))
time = (W("年") | W("时间"))
rules = [
Rule(condition_num=2,
condition=person_entity + Star(Any(), greedy=False) + case +
Star(Any(), greedy=False),
action=QuestionSet.has_case_question),
Rule(condition_num=2,
condition=case_entity + Star(Any(), greedy=False) + person +
Star(Any(), greedy=False),
action=QuestionSet.has_person_question),
Rule(condition_num=3,
condition=person_entity + Star(Any(), greedy=False) + person_entity +
Star(Any(), greedy=False) + cooperation,
action=QuestionSet.has_cooperation_question),
Rule(condition_num=3,
condition=person_entity + Star(Any(), greedy=False) +
drug_to_person_entity + Star(Any(), greedy=False) + trade,
action=QuestionSet.has_trade_question),
Rule(condition_num=3,
# TODO 问题模板/匹配规则
"""
1. 某演员演了什么电影
2. 某电影有哪些演员出演
3. 演员A和演员B合作出演了哪些电影
4. 某演员参演的评分大于X的电影有哪些
5. 某演员出演过哪些类型的电影
6. 某演员出演的XX类型电影有哪些。
7. 某演员出演了多少部电影。
8. 某演员是喜剧演员吗。
9. 某演员的生日/出生地/英文名/简介
10. 某电影的简介/上映日期/评分
"""
rules = [
Rule(condition_num=2,
condition=person_entity + Star(Any(), greedy=False) + movie +
Star(Any(), greedy=False),
action=QuestionSet.has_movie_question),
Rule(condition_num=2,
condition=(movie_entity + Star(Any(), greedy=False) + actor +
Star(Any(), greedy=False)) |
(actor + Star(Any(), greedy=False) + movie_entity +
Star(Any(), greedy=False)),
action=QuestionSet.has_actor_question),
Rule(condition_num=3,
condition=person_entity + Star(Any(), greedy=False) + person_entity +
Star(Any(), greedy=False) + (movie | Star(Any(), greedy=False)),
action=QuestionSet.has_cooperation_question),
Rule(condition_num=4,
condition=person_entity + Star(Any(), greedy=False) + compare +
number_entity + Star(Any(), greedy=False) + movie +
# 某人演了哪些电影
# 某人写了哪些电影
# 某人指导了哪些电影
# 某人的详细信息
# 未完成
# 某人出演了多少部电影
# 某人写了多少部电影
# 某人导演了多少部电影
# 某演员参演的评分大于X的电影有哪些
# 某演员出演过哪些类型的电影
# 演员A和演员B合作出演了哪些电影
"""
movie_person_rules = [
Rule(condition_num=2,
condition=person_entity + Star(Any(), greedy=False) +
movie_person_info + Star(Any(), greedy=False),
action=QuestionSet.has_movie_person_info),
Rule(condition_num=2,
condition=person_entity + Star(Any(), greedy=False) + director +
Star(Any(), greedy=False),
action=QuestionSet.has_directed_in),
Rule(condition_num=2,
condition=person_entity + Star(Any(), greedy=False) + writer +
Star(Any(), greedy=False),
action=QuestionSet.has_writed_in),
Rule(condition_num=2,
condition=person_entity + Star(Any(), greedy=False) + actor +
Star(Any(), greedy=False),
action=QuestionSet.has_acted_in),
Rule(condition_num=2,
def test_rule(Subject, Object):
anything = Question(Star(Any()))
return Subject + Token("(") + Object + Token("-") + anything
