def test_semantic_similarity_for_alignment(self):
     from oke.oak.util import extract_type_label
     from oke.oak.util import get_URI_fragmentIdentifier
     
     entityClasses_labels=["company"]
     all_rdf_types_labels=["Manufacturer", "Companies Listed On The Singapore Exchange"]
     most_similiar_dul_classes = self.semantic_similarity_for_alignment(entityClasses_labels, all_rdf_types_labels)
     print("most_semantic_similiar_dul_classes for 'company': ",most_similiar_dul_classes)
     
     entity_dbpedia_URI="http://dbpedia.org/resource/Brian_Banner"
     entityClasses_labels=["Fictional Villain","Villain"]
     linked_data_suggested_dulClasses, all_rdf_types =self.linked_data_discovery_for_alignment(entity_dbpedia_URI, entityClasses_labels)
     all_rdf_types_labels=set([extract_type_label(get_URI_fragmentIdentifier(rdftype_uri)) for rdftype_uri in all_rdf_types])
     most_similiar_dul_classes = self.semantic_similarity_for_alignment(entityClasses_labels, all_rdf_types_labels)
     print("most_semantic_similiar_dul_classes for 'Villain': ",most_similiar_dul_classes)
     
     entity_dbpedia_URI="http://dbpedia.org/resource/Dalavia_Far_East_Airways"
     entityClasses_labels=["Airline"]
     linked_data_suggested_dulClasses, all_rdf_types =self.linked_data_discovery_for_alignment(entity_dbpedia_URI, entityClasses_labels)
     all_rdf_types_labels=set([extract_type_label(get_URI_fragmentIdentifier(rdftype_uri)) for rdftype_uri in all_rdf_types])
     most_similiar_dul_classes = self.semantic_similarity_for_alignment(entityClasses_labels, all_rdf_types_labels)
     print("most_semantic_similiar_dul_classes for 'Airline': ",most_similiar_dul_classes)
      
     entity_dbpedia_URI="http://dbpedia.org/resource/Danderyds_sjukhus_metro_station"
     entityClasses_labels=["Station","Metro Station"]
     linked_data_suggested_dulClasses, all_rdf_types =self.linked_data_discovery_for_alignment(entity_dbpedia_URI, entityClasses_labels)
     all_rdf_types_labels=set([extract_type_label(get_URI_fragmentIdentifier(rdftype_uri)) for rdftype_uri in all_rdf_types])
     most_similiar_dul_classes = self.semantic_similarity_for_alignment(entityClasses_labels, all_rdf_types_labels)
     print("most_semantic_similiar_dul_classes for 'Metro Station': ",most_similiar_dul_classes)
    def test_semantic_similarity_for_alignment(self):
        from oke.oak.util import extract_type_label
        from oke.oak.util import get_URI_fragmentIdentifier

        entityClasses_labels = ["company"]
        all_rdf_types_labels = [
            "Manufacturer", "Companies Listed On The Singapore Exchange"
        ]
        most_similiar_dul_classes = self.semantic_similarity_for_alignment(
            entityClasses_labels, all_rdf_types_labels)
        print("most_semantic_similiar_dul_classes for 'company': ",
              most_similiar_dul_classes)

        entity_dbpedia_URI = "http://dbpedia.org/resource/Brian_Banner"
        entityClasses_labels = ["Fictional Villain", "Villain"]
        linked_data_suggested_dulClasses, all_rdf_types = self.linked_data_discovery_for_alignment(
            entity_dbpedia_URI, entityClasses_labels)
        all_rdf_types_labels = set([
            extract_type_label(get_URI_fragmentIdentifier(rdftype_uri))
            for rdftype_uri in all_rdf_types
        ])
        most_similiar_dul_classes = self.semantic_similarity_for_alignment(
            entityClasses_labels, all_rdf_types_labels)
        print("most_semantic_similiar_dul_classes for 'Villain': ",
              most_similiar_dul_classes)

        entity_dbpedia_URI = "http://dbpedia.org/resource/Dalavia_Far_East_Airways"
        entityClasses_labels = ["Airline"]
        linked_data_suggested_dulClasses, all_rdf_types = self.linked_data_discovery_for_alignment(
            entity_dbpedia_URI, entityClasses_labels)
        all_rdf_types_labels = set([
            extract_type_label(get_URI_fragmentIdentifier(rdftype_uri))
            for rdftype_uri in all_rdf_types
        ])
        most_similiar_dul_classes = self.semantic_similarity_for_alignment(
            entityClasses_labels, all_rdf_types_labels)
        print("most_semantic_similiar_dul_classes for 'Airline': ",
              most_similiar_dul_classes)

        entity_dbpedia_URI = "http://dbpedia.org/resource/Danderyds_sjukhus_metro_station"
        entityClasses_labels = ["Station", "Metro Station"]
        linked_data_suggested_dulClasses, all_rdf_types = self.linked_data_discovery_for_alignment(
            entity_dbpedia_URI, entityClasses_labels)
        all_rdf_types_labels = set([
            extract_type_label(get_URI_fragmentIdentifier(rdftype_uri))
            for rdftype_uri in all_rdf_types
        ])
        most_similiar_dul_classes = self.semantic_similarity_for_alignment(
            entityClasses_labels, all_rdf_types_labels)
        print("most_semantic_similiar_dul_classes for 'Metro Station': ",
              most_similiar_dul_classes)
    def ontology_alignment(self, context_data):
        '''
        ontology alignment for each context data
        step 1: linked data discovery
        step 2: terminological similarity alignment
        step 3: semantic similarity alignment
        
        return set, suggested aligned classes
        '''
        from oke.oak.util import extract_type_label
        from oke.oak.util import get_URI_fragmentIdentifier

        entity_dbpedia_URI = context_data.entity.taIdentRef
        entityClasses = context_data.entity.isInstOfEntityClasses

        entityClasses_labels = set(
            [entityClass.anchorOf for entityClass in entityClasses])
        #step 1: linked data discovery for alignment suggestions
        linked_data_suggested_alignments, all_rdf_types = self.linked_data_discovery_for_alignment(
            entity_dbpedia_URI, entityClasses_labels)
        '''
        if (len(linked_data_suggested_alignments) > 0):
            return linked_data_suggested_alignments
        '''
        all_rdf_types_labels = set([
            extract_type_label(get_URI_fragmentIdentifier(rdftype_uri))
            for rdftype_uri in all_rdf_types
        ])

        #step 2: terminological similarity computation for alignment suggestions
        term_similarity_suggested_alignments = self.terminology_similarity_for_alignment(
            entityClasses_labels, all_rdf_types_labels)
        if len(term_similarity_suggested_alignments) > 0:
            return term_similarity_suggested_alignments

        #step 3: semantic similarity computation for alignment suggestion
        #semantic_similarity_suggestions=set()

        semantic_similarity_suggested_DUL_class = self.semantic_similarity_for_alignment(
            entityClasses_labels, all_rdf_types_labels)

        #semantic_similarity_suggestions.add(semantic_similarity_suggested_DUL_class)
        semantic_similarity_suggestions = {
            semantic_similarity_suggested_DUL_class
        }
        print("return suggested DUL alignment from semantic computation:",
              semantic_similarity_suggestions)

        return semantic_similarity_suggestions
        '''
 def ontology_alignment(self, context_data):
     '''
     ontology alignment for each context data
     step 1: linked data discovery
     step 2: terminological similarity alignment
     step 3: semantic similarity alignment
     
     return set, suggested aligned classes
     '''
     from oke.oak.util import extract_type_label
     from oke.oak.util import get_URI_fragmentIdentifier
     
     entity_dbpedia_URI = context_data.entity.taIdentRef
     entityClasses = context_data.entity.isInstOfEntityClasses
     
     entityClasses_labels=set([entityClass.anchorOf for entityClass in entityClasses])
     #step 1: linked data discovery for alignment suggestions
     linked_data_suggested_alignments, all_rdf_types =self.linked_data_discovery_for_alignment(entity_dbpedia_URI,entityClasses_labels)
     '''
     if (len(linked_data_suggested_alignments) > 0):
         return linked_data_suggested_alignments
     '''
     all_rdf_types_labels=set([extract_type_label(get_URI_fragmentIdentifier(rdftype_uri)) for rdftype_uri in all_rdf_types])
     
     #step 2: terminological similarity computation for alignment suggestions
     term_similarity_suggested_alignments= self.terminology_similarity_for_alignment(entityClasses_labels, all_rdf_types_labels)
     if len(term_similarity_suggested_alignments) > 0:
         return term_similarity_suggested_alignments
     
     #step 3: semantic similarity computation for alignment suggestion
     #semantic_similarity_suggestions=set()
     
     semantic_similarity_suggested_DUL_class = self.semantic_similarity_for_alignment(entityClasses_labels,all_rdf_types_labels)
     
     #semantic_similarity_suggestions.add(semantic_similarity_suggested_DUL_class)
     semantic_similarity_suggestions={semantic_similarity_suggested_DUL_class}
     print("return suggested DUL alignment from semantic computation:",semantic_similarity_suggestions)
     
     return semantic_similarity_suggestions
     '''
    def compute_features(self, context_data):
        '''
        Maximum entropy model gives a better performance for sequence labelling problem. 
        By maximizing the entropy in our model, we are attempting to minimise the amount of the information the model carries.
        Design a language model to maximise the entropy and 
            feed our language model with a set of features associated with a given token we wish to classify
            and the system can then given us the probability that our token falls into any given class of token against which our language model was trained.
        '''
        from oke.oak.util import wordnet_shortest_path
        from oke.oak.util import extract_type_label
        from oke.oak.util import get_URI_fragmentIdentifier
        from oke.oak.util import contains_digits
        
        #words, contextURI, previousLabel, position
        if type(context_data) is not TaskContext:
            raise Exception('Type error: context_data must be the instance of oke.oak.TaskContext')
        
        context_words=word_tokenize(context_data.isString)
        tagged_context=pos_tag(context_words)
        sem_tagged_context=self.sem_tag(context_words,context_data)        
        
        entity_name=context_data.entity.anchorOf
        entity_head_word=entity_name.split(' ')[-1:][0]
        entity_dbpedia_URI = context_data.entity.taIdentRef
        #print("entity_dbpedia_URI:"+entity_dbpedia_URI)
        '''
        LOD based semantic type feature:
        '''
        entity_rdftypes=self.entity_rdftypes_feature_extraction(entity_dbpedia_URI)         
        
        if (len (entity_rdftypes) == 0):
            print("Warn: No rdf types can be found for [current word")#entity_name.decode("utf8"),"]")
        # extract labels from RDF type
        entity_semantics=set()
        entity_semantics.update(set([extract_type_label(get_URI_fragmentIdentifier(rdftype_uri)) for rdftype_uri in entity_rdftypes]))
        
        #print('sem_tagged_context:',sem_tagged_context)
        #add head word into rdf type
        #  to avoid adding head word into rdf type: not many head word represent essential word associated with type
        #entity_semantics.add(entity_head_word)
        #print("entity_semantics:",entity_semantics)
        datums=[]
        
        #compute features for each word
        #use sliding window to observe on both left and right hand side
        currentIndex=0
        sliding_window_prev_n_words=8
        sliding_window_next_n_words=3
        
        for tagged_word in tagged_context:
            currentWord=tagged_word[0]
            #label encoding
            currentWord_label='O' if sem_tagged_context[currentIndex][1] !='class' else 'class'
            datum = Datum(context_data.contextURI,currentWord,currentWord_label)
            
            datum.previousLabel=datums[currentIndex-1].label if (currentIndex-1) in range(0,len(datums)) else 'None'
            
            features={}
            #word-level features (part-of-speech, case, punctuation,digit,morphology)
            import string
            if currentWord.lower() not in self.stoplist and currentWord not in string.punctuation and currentWord.isdigit() is not True and tagged_word[1] in ["NN", "NNP", "NNS"]:
                #use lemmatised word
                features["word"]= self.wordnet_lemmatizer.lemmatize(currentWord, pos='n')
                #Word sense of Noun: we can use "WN_CLASS" to determine whether the NN word is a hyponym of w (or keywords) in ontology by wordnet
                #features["WN_CLASS"]=
            features["word_pos"]=tagged_word[1]
            #features["word_root"]=self.wordnet_lemmatizer.lemmatize(currentWord, pos='n')
            features["is_title"]=str(currentWord).istitle()
            features['all_capital']=currentWord.isupper()
            features["is_word_root_be"]='Y' if self.wordnet_lemmatizer.lemmatize(currentWord, pos='v') == 'be' else 'N'
            features['is_punct_comma']='Y' if str(currentWord) == ',' else 'N'
            features['word_with_digits']='Y' if tagged_word[1]!='CD' and contains_digits(str(currentWord)) else 'N'         
            features["is_StopWord"]='Y' if currentWord in self.stoplist else 'N'
            features["is_Entity"]='N' if sem_tagged_context[currentIndex] !='entity' else 'Y'
            features["last_2_letters"]='None' if len(str(currentWord))<=2 or str(currentWord).isdigit() else str(currentWord)[-2:]
            #type_indicator can be retrieved by wordnet synonyms
            features["type_indicator"]='Y' if currentWord in ['name','form','type','class','category', 'variety', 'style','model','substance', 'version', 'genre','matter','mound', 'kind', 'shade', 'substance'] else 'N'
            
            #semantic (gazetteer lookup) features
            features["is_orgKey"] ='Y' if currentWord.lower() in self.gaz_org_key else 'N'
            features["is_locKey"] = 'Y' if currentWord.lower() in self.gaz_loc_key else 'N'
            features["is_country"] = 'Y' if currentWord.lower() in self.gaz_country else 'N'
            features["is_countryAdj"]='Y' if currentWord.lower in self.gaz_countryAdj else 'N'
            features["is_personName"] = 'Y' if currentWord.lower() in self.gaz_person_name else 'N'
            features["is_personTitle"] = 'Y' if currentWord.lower() in self.gaz_person_title else 'N'
            features['is_jobtitle']='Y' if currentWord.lower() in self.gaz_job_title else 'N'
            features['is_facKey']='Y' if currentWord.lower() in self.gaz_facility_key else 'N'
            
            #add feature to compute path similarity between dbpedia type and current word
            
            if entity_semantics:
                max_sim = max([wordnet_shortest_path(currentWord,sem_type.split(' ')[-1:][0]) for sem_type in entity_semantics])
                features['sim_dist_with_DbpediaType'] = max_sim
            
            
            for last_i in range(1,sliding_window_prev_n_words+1):
                if currentIndex == 0:
                    features['prev_word']="<START>"
                
                if currentIndex != 0 and currentIndex-last_i >=0:                    
                    #features['prev_'+str(last_i)+'_word']=datums[currentIndex-last_i].features['word'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_pos']=datums[currentIndex-last_i].features['word_pos'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    #features['prev_'+str(last_i)+'_word_root']=datums[currentIndex-last_i].features['word_root'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'                
                    features['prev_'+str(last_i)+'_word_is_StopWord']=datums[currentIndex-last_i].features['is_StopWord'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'         
                    features['prev_'+str(last_i)+'_word_is_Entity']=datums[currentIndex-last_i].features['is_Entity'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_is_title']=datums[currentIndex-last_i].features['is_title'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_all_capital']=datums[currentIndex-last_i].features['all_capital'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_is_word_root_be']=datums[currentIndex-last_i].features['is_word_root_be'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_is_punct_comma']=datums[currentIndex-last_i].features['is_punct_comma'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_word_with_digits']=datums[currentIndex-last_i].features['word_with_digits'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_last_2_letters']=datums[currentIndex-last_i].features['last_2_letters'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_type_indicator']=datums[currentIndex-last_i].features['type_indicator'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_is_orgKey']=datums[currentIndex-last_i].features['is_orgKey'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_is_locKey']=datums[currentIndex-last_i].features['is_locKey'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_is_country']=datums[currentIndex-last_i].features['is_country'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_is_countryAdj']=datums[currentIndex-last_i].features['is_countryAdj'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_is_personName']=datums[currentIndex-last_i].features['is_personName'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_is_personTitle']=datums[currentIndex-last_i].features['is_personTitle'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_'+str(last_i)+'_word_is_facKey']=datums[currentIndex-last_i].features['is_facKey'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                
            datum.features=features
            currentIndex+=1
            datums.append(datum)
        
        #add features about next words
        #reset to 0
        currentIndex = 0
        for tagged_word in tagged_context:
            for next_i in range(1, sliding_window_next_n_words+1):
                if ((currentIndex+next_i) == len(datums)):
                    datums[currentIndex].features['next_word']="<END>"
                
                if (currentIndex+next_i) != len(datums) :
                    #datums[currentIndex].features['next_'+str(next_i)+'_word']=datums[currentIndex+next_i].features['word'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_pos']=datums[currentIndex+next_i].features['word_pos'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                
                    datums[currentIndex].features['next_'+str(next_i)+'_word_is_StopWord']=datums[currentIndex+next_i].features['is_StopWord'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_is_Entity']=datums[currentIndex+next_i].features['is_Entity'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'

                    datums[currentIndex].features['next_'+str(next_i)+'_word_is_title']=datums[currentIndex+next_i].features['is_title'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_all_capital']=datums[currentIndex+next_i].features['all_capital'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_is_word_root_be']=datums[currentIndex+next_i].features['is_word_root_be'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_is_punct_comma']=datums[currentIndex+next_i].features['is_punct_comma'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_word_with_digits']=datums[currentIndex+next_i].features['word_with_digits'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_last_2_letters']=datums[currentIndex+next_i].features['last_2_letters'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_type_indicator']=datums[currentIndex+next_i].features['type_indicator'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_is_orgKey']=datums[currentIndex+next_i].features['is_orgKey'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_is_locKey']=datums[currentIndex+next_i].features['is_locKey'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_is_country']=datums[currentIndex+next_i].features['is_country'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_is_countryAdj']=datums[currentIndex+next_i].features['is_countryAdj'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_is_personName']=datums[currentIndex+next_i].features['is_personName'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_is_personTitle']=datums[currentIndex+next_i].features['is_personTitle'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features['next_'+str(next_i)+'_word_is_facKey']=datums[currentIndex+next_i].features['is_facKey'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
            currentIndex+=1
            
        return datums
Пример #6
0
    def compute_features(self, context_data):
        '''
        Maximum entropy model gives a better performance for sequence labelling problem. 
        By maximizing the entropy in our model, we are attempting to minimise the amount of the information the model carries.
        Design a language model to maximise the entropy and 
            feed our language model with a set of features associated with a given token we wish to classify
            and the system can then given us the probability that our token falls into any given class of token against which our language model was trained.
        '''
        from oke.oak.util import wordnet_shortest_path
        from oke.oak.util import extract_type_label
        from oke.oak.util import get_URI_fragmentIdentifier
        from oke.oak.util import contains_digits

        #words, contextURI, previousLabel, position
        if type(context_data) is not TaskContext:
            raise Exception(
                'Type error: context_data must be the instance of oke.oak.TaskContext'
            )

        context_words = word_tokenize(context_data.isString)
        tagged_context = pos_tag(context_words)
        sem_tagged_context = self.sem_tag(context_words, context_data)

        entity_name = context_data.entity.anchorOf
        entity_head_word = entity_name.split(' ')[-1:][0]
        entity_dbpedia_URI = context_data.entity.taIdentRef
        #print("entity_dbpedia_URI:"+entity_dbpedia_URI)
        '''
        LOD based semantic type feature:
        '''
        entity_rdftypes = self.entity_rdftypes_feature_extraction(
            entity_dbpedia_URI)

        if (len(entity_rdftypes) == 0):
            print("Warn: No rdf types can be found for [current word"
                  )  #entity_name.decode("utf8"),"]")
        # extract labels from RDF type
        entity_semantics = set()
        entity_semantics.update(
            set([
                extract_type_label(get_URI_fragmentIdentifier(rdftype_uri))
                for rdftype_uri in entity_rdftypes
            ]))

        #print('sem_tagged_context:',sem_tagged_context)
        #add head word into rdf type
        #  to avoid adding head word into rdf type: not many head word represent essential word associated with type
        #entity_semantics.add(entity_head_word)
        #print("entity_semantics:",entity_semantics)
        datums = []

        #compute features for each word
        #use sliding window to observe on both left and right hand side
        currentIndex = 0
        sliding_window_prev_n_words = 8
        sliding_window_next_n_words = 3

        for tagged_word in tagged_context:
            currentWord = tagged_word[0]
            #label encoding
            currentWord_label = 'O' if sem_tagged_context[currentIndex][
                1] != 'class' else 'class'
            datum = Datum(context_data.contextURI, currentWord,
                          currentWord_label)

            datum.previousLabel = datums[currentIndex - 1].label if (
                currentIndex - 1) in range(0, len(datums)) else 'None'

            features = {}
            #word-level features (part-of-speech, case, punctuation,digit,morphology)
            import string
            if currentWord.lower(
            ) not in self.stoplist and currentWord not in string.punctuation and currentWord.isdigit(
            ) is not True and tagged_word[1] in ["NN", "NNP", "NNS"]:
                #use lemmatised word
                features["word"] = self.wordnet_lemmatizer.lemmatize(
                    currentWord, pos='n')
                #Word sense of Noun: we can use "WN_CLASS" to determine whether the NN word is a hyponym of w (or keywords) in ontology by wordnet
                #features["WN_CLASS"]=
            features["word_pos"] = tagged_word[1]
            #features["word_root"]=self.wordnet_lemmatizer.lemmatize(currentWord, pos='n')
            features["is_title"] = str(currentWord).istitle()
            features['all_capital'] = currentWord.isupper()
            features[
                "is_word_root_be"] = 'Y' if self.wordnet_lemmatizer.lemmatize(
                    currentWord, pos='v') == 'be' else 'N'
            features['is_punct_comma'] = 'Y' if str(
                currentWord) == ',' else 'N'
            features['word_with_digits'] = 'Y' if tagged_word[
                1] != 'CD' and contains_digits(str(currentWord)) else 'N'
            features[
                "is_StopWord"] = 'Y' if currentWord in self.stoplist else 'N'
            features["is_Entity"] = 'N' if sem_tagged_context[
                currentIndex] != 'entity' else 'Y'
            features["last_2_letters"] = 'None' if len(
                str(currentWord)) <= 2 or str(currentWord).isdigit() else str(
                    currentWord)[-2:]
            #type_indicator can be retrieved by wordnet synonyms
            features["type_indicator"] = 'Y' if currentWord in [
                'name', 'form', 'type', 'class', 'category', 'variety',
                'style', 'model', 'substance', 'version', 'genre', 'matter',
                'mound', 'kind', 'shade', 'substance'
            ] else 'N'

            #semantic (gazetteer lookup) features
            features["is_orgKey"] = 'Y' if currentWord.lower(
            ) in self.gaz_org_key else 'N'
            features["is_locKey"] = 'Y' if currentWord.lower(
            ) in self.gaz_loc_key else 'N'
            features["is_country"] = 'Y' if currentWord.lower(
            ) in self.gaz_country else 'N'
            features[
                "is_countryAdj"] = 'Y' if currentWord.lower in self.gaz_countryAdj else 'N'
            features["is_personName"] = 'Y' if currentWord.lower(
            ) in self.gaz_person_name else 'N'
            features["is_personTitle"] = 'Y' if currentWord.lower(
            ) in self.gaz_person_title else 'N'
            features['is_jobtitle'] = 'Y' if currentWord.lower(
            ) in self.gaz_job_title else 'N'
            features['is_facKey'] = 'Y' if currentWord.lower(
            ) in self.gaz_facility_key else 'N'

            #add feature to compute path similarity between dbpedia type and current word

            if entity_semantics:
                max_sim = max([
                    wordnet_shortest_path(currentWord,
                                          sem_type.split(' ')[-1:][0])
                    for sem_type in entity_semantics
                ])
                features['sim_dist_with_DbpediaType'] = max_sim

            for last_i in range(1, sliding_window_prev_n_words + 1):
                if currentIndex == 0:
                    features['prev_word'] = "<START>"

                if currentIndex != 0 and currentIndex - last_i >= 0:
                    #features['prev_'+str(last_i)+'_word']=datums[currentIndex-last_i].features['word'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_' + str(last_i) + '_word_pos'] = datums[
                        currentIndex - last_i].features['word_pos'] if (
                            currentIndex -
                            last_i) in range(0, len(datums)) else 'None'
                    #features['prev_'+str(last_i)+'_word_root']=datums[currentIndex-last_i].features['word_root'] if (currentIndex-last_i) in range(0,len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_is_StopWord'] = datums[
                                 currentIndex -
                                 last_i].features['is_StopWord'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_is_Entity'] = datums[
                                 currentIndex -
                                 last_i].features['is_Entity'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_is_title'] = datums[
                                 currentIndex -
                                 last_i].features['is_title'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_all_capital'] = datums[
                                 currentIndex -
                                 last_i].features['all_capital'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_is_word_root_be'] = datums[
                                 currentIndex -
                                 last_i].features['is_word_root_be'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_is_punct_comma'] = datums[
                                 currentIndex -
                                 last_i].features['is_punct_comma'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_word_with_digits'] = datums[
                                 currentIndex -
                                 last_i].features['word_with_digits'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_last_2_letters'] = datums[
                                 currentIndex -
                                 last_i].features['last_2_letters'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_type_indicator'] = datums[
                                 currentIndex -
                                 last_i].features['type_indicator'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_is_orgKey'] = datums[
                                 currentIndex -
                                 last_i].features['is_orgKey'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_is_locKey'] = datums[
                                 currentIndex -
                                 last_i].features['is_locKey'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_is_country'] = datums[
                                 currentIndex -
                                 last_i].features['is_country'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_is_countryAdj'] = datums[
                                 currentIndex -
                                 last_i].features['is_countryAdj'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_is_personName'] = datums[
                                 currentIndex -
                                 last_i].features['is_personName'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_is_personTitle'] = datums[
                                 currentIndex -
                                 last_i].features['is_personTitle'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'
                    features['prev_' + str(last_i) +
                             '_word_is_facKey'] = datums[
                                 currentIndex -
                                 last_i].features['is_facKey'] if (
                                     currentIndex - last_i) in range(
                                         0, len(datums)) else 'None'

            datum.features = features
            currentIndex += 1
            datums.append(datum)

        #add features about next words
        #reset to 0
        currentIndex = 0
        for tagged_word in tagged_context:
            for next_i in range(1, sliding_window_next_n_words + 1):
                if ((currentIndex + next_i) == len(datums)):
                    datums[currentIndex].features['next_word'] = "<END>"

                if (currentIndex + next_i) != len(datums):
                    #datums[currentIndex].features['next_'+str(next_i)+'_word']=datums[currentIndex+next_i].features['word'] if (currentIndex+next_i) in range(0,len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) +
                        '_word_pos'] = datums[currentIndex +
                                              next_i].features['word_pos'] if (
                                                  currentIndex +
                                                  next_i) in range(
                                                      0,
                                                      len(datums)) else 'None'

                    datums[currentIndex].features[
                        'next_' + str(next_i) + '_word_is_StopWord'] = datums[
                            currentIndex + next_i].features['is_StopWord'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) + '_word_is_Entity'] = datums[
                            currentIndex + next_i].features['is_Entity'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'

                    datums[currentIndex].features[
                        'next_' + str(next_i) + '_word_is_title'] = datums[
                            currentIndex + next_i].features['is_title'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) + '_word_all_capital'] = datums[
                            currentIndex + next_i].features['all_capital'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) +
                        '_word_is_word_root_be'] = datums[
                            currentIndex +
                            next_i].features['is_word_root_be'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) +
                        '_word_is_punct_comma'] = datums[
                            currentIndex +
                            next_i].features['is_punct_comma'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) +
                        '_word_word_with_digits'] = datums[
                            currentIndex +
                            next_i].features['word_with_digits'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) +
                        '_word_last_2_letters'] = datums[
                            currentIndex +
                            next_i].features['last_2_letters'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) +
                        '_word_type_indicator'] = datums[
                            currentIndex +
                            next_i].features['type_indicator'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) + '_word_is_orgKey'] = datums[
                            currentIndex + next_i].features['is_orgKey'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) + '_word_is_locKey'] = datums[
                            currentIndex + next_i].features['is_locKey'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) + '_word_is_country'] = datums[
                            currentIndex + next_i].features['is_country'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) +
                        '_word_is_countryAdj'] = datums[
                            currentIndex +
                            next_i].features['is_countryAdj'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) +
                        '_word_is_personName'] = datums[
                            currentIndex +
                            next_i].features['is_personName'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) +
                        '_word_is_personTitle'] = datums[
                            currentIndex +
                            next_i].features['is_personTitle'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
                    datums[currentIndex].features[
                        'next_' + str(next_i) + '_word_is_facKey'] = datums[
                            currentIndex + next_i].features['is_facKey'] if (
                                currentIndex +
                                next_i) in range(0, len(datums)) else 'None'
            currentIndex += 1

        return datums