Contextual Knowledge Graph Integration with Real-time Content Syndication Overview and Strategic Value This integration method creates a sophisticated knowledge graph system that maps AI responses to aéPiot's content ecosystem in real-time. The system automatically identifies knowledge gaps, suggests complementary content, and creates dynamic learning pathways that adapt based on user interests and exploration patterns. Technical Architecture The knowledge graph integration includes: Semantic Analysis Engine: Real-time mapping of AI responses to knowledge domains Dynamic Content Syndication: Automated discovery and linking of relevant aéPiot content Adaptive Learning Pathways: Personalized content recommendations based on user behavior Cross-Reference Generation: Automatic citation and source linking for AI responses Multi-lingual Content Discovery: Global content exploration capabilities Implementation Script (Advanced Python Integration)

 

Contextual Knowledge Graph Integration with Real-time Content Syndication

Overview and Strategic Value

This integration method creates a sophisticated knowledge graph system that maps AI responses to aéPiot's content ecosystem in real-time. The system automatically identifies knowledge gaps, suggests complementary content, and creates dynamic learning pathways that adapt based on user interests and exploration patterns.

Technical Architecture

The knowledge graph integration includes:

• Semantic Analysis Engine: Real-time mapping of AI responses to knowledge domains
• Dynamic Content Syndication: Automated discovery and linking of relevant aéPiot content
• Adaptive Learning Pathways: Personalized content recommendations based on user behavior
• Cross-Reference Generation: Automatic citation and source linking for AI responses
• Multi-lingual Content Discovery: Global content exploration capabilities

Implementation Script (Advanced Python Integration)

python

import asyncio
import networkx as nx
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import spacy
from datetime import datetime, timedelta
import aiohttp
import json
from urllib.parse import urlencode
import hashlib

class AePiotKnowledgeGraphIntegration:
    def __init__(self, config):
        self.config = config
        self.aepiot_services = {
            'backlink': 'https://aepiot.com/backlink.html',
            'search': 'https://aepiot.com/multi-search.html',
            'reader': 'https://aepiot.com/reader.html',
            'tags': 'https://aepiot.com/tag-explorer.html',
            'related': 'https://aepiot.com/related-search.html'
        }
        
        # Initialize knowledge graph
        self.knowledge_graph = nx.DiGraph()
        
        # Load language model for semantic analysis
        self.nlp = spacy.load("en_core_web_lg")
        
        # Initialize vectorizer for content similarity
        self.vectorizer = TfidfVectorizer(max_features=5000, stop_words='english')
        
        # Session for HTTP requests
        self.session = aiohttp.ClientSession()
        
    async def process_ai_response_with_knowledge_graph(self, user_query: str, 
                                                       ai_response: str, 
                                                       conversation_context: List[Dict]) -> Dict:
        """
        Process AI response and create comprehensive knowledge graph integration
        """
        processing_id = self.generate_processing_id(user_query, ai_response)
        
        # Analyze semantic structure of response
        semantic_analysis = await self.analyze_semantic_structure(ai_response, user_query)
        
        # Build knowledge graph for this response
        response_graph = await self.build_response_knowledge_graph(
            semantic_analysis, conversation_context
        )
        
        # Find related content in aéPiot ecosystem
        related_content = await self.discover_related_content(semantic_analysis)
        
        # Generate dynamic learning pathways
        learning_pathways = await self.create_learning_pathways(
            semantic_analysis, related_content, conversation_context
        )
        
        # Create cross-references and citations
        cross_references = await self.generate_cross_references(
            semantic_analysis, related_content
        )
        
        # Generate multi-lingual exploration options
        multilingual_options = await self.generate_multilingual_exploration(semantic_analysis)
        
        # Create real-time syndication links
        syndication_links = await self.create_syndication_links(
            semantic_analysis, processing_id
        )
        
        integration_result = {
            'processing_id': processing_id,
            'timestamp': datetime.now().isoformat(),
            'semantic_analysis': semantic_analysis,
            'knowledge_graph': self.serialize_graph(response_graph),
            'related_content': related_content,
            'learning_pathways': learning_pathways,
            'cross_references': cross_references,
            'multilingual_options': multilingual_options,
            'syndication_links': syndication_links,
            'user_interface_components': await self.generate_ui_components(
                semantic_analysis, learning_pathways, related_content
            )
        }
        
        return integration_result
    
    def generate_processing_id(self, query: str, response: str) -> str:
        """Generate unique processing ID for this AI interaction"""
        combined_text = f"{query}_{response}_{datetime.now().isoformat()}"
        return hashlib.md5(combined_text.encode()).hexdigest()[:16]
    
    async def analyze_semantic_structure(self, ai_response: str, user_query: str) -> Dict:
        """
        Perform comprehensive semantic analysis of AI response
        """
        # Process text with spaCy
        response_doc = self.nlp(ai_response)
        query_doc = self.nlp(user_query)
        
        # Extract entities and their relationships
        entities = []
        for ent in response_doc.ents:
            entities.append({
                'text': ent.text,
                'label': ent.label_,
                'start': ent.start_char,
                'end': ent.end_char,
                'description': spacy.explain(ent.label_)
            })
        
        # Extract key phrases and noun chunks
        key_phrases = [chunk.text for chunk in response_doc.noun_chunks if len(chunk.text) > 3]
        
        # Identify main topics using sentence analysis
        sentences = [sent.text.strip() for sent in response_doc.sents if len(sent.text.strip()) > 10]
        topic_sentences = sentences[:3]  # Focus on first 3 substantive sentences
        
        # Calculate semantic similarity between query and response
        query_response_similarity = query_doc.similarity(response_doc)
        
        # Extract domain classification
        domain_classification = await self.classify_content_domain(ai_response)
        
        # Identify knowledge gaps and areas for exploration
        knowledge_gaps = await self.identify_knowledge_gaps(ai_response, user_query)
        
        return {
            'entities': entities,
            'key_phrases': key_phrases[:10],  # Top 10 key phrases
            'topic_sentences': topic_sentences,
            'query_response_similarity': float(query_response_similarity),
            'domain_classification': domain_classification,
            'knowledge_gaps': knowledge_gaps,
            'complexity_score': self.calculate_complexity_score(response_doc),
            'semantic_density': len(entities) / len(sentences) if sentences else 0
        }
    
    async def classify_content_domain(self, content: str) -> Dict:
        """
        Classify content into domain categories for targeted aéPiot integration
        """
        domain_keywords = {
            'technology': ['software', 'programming', 'AI', 'machine learning', 'computer', 'digital', 'algorithm', 'data'],
            'science': ['research', 'study', 'theory', 'experiment', 'hypothesis', 'analysis', 'method', 'scientific'],
            'business': ['market', 'company', 'strategy', 'management', 'finance', 'economics', 'business', 'industry'],
            'education': ['learning', 'teaching', 'student', 'academic', 'knowledge', 'skill', 'course', 'training'],
            'health': ['medical', 'health', 'treatment', 'patient', 'disease', 'therapy', 'clinical', 'wellness'],
            'arts': ['creative', 'design', 'art', 'culture', 'artistic', 'aesthetic', 'literature', 'music'],
            'society': ['social', 'community', 'people', 'society', 'culture', 'human', 'relationship', 'communication']
        }
        
        content_lower = content.lower()
        domain_scores = {}
        
        for domain, keywords in domain_keywords.items():
            score = sum(1 for keyword in keywords if keyword in content_lower)
            domain_scores[domain] = score / len(keywords)  # Normalize by keyword count
        
        # Get primary and secondary domains
        sorted_domains = sorted(domain_scores.items(), key=lambda x: x[1], reverse=True)
        
        return {
            'primary_domain': sorted_domains[0][0] if sorted_domains[0][1] > 0 else 'general',
            'secondary_domain': sorted_domains[1][0] if len(sorted_domains) > 1 and sorted_domains[1][1] > 0 else None,
            'domain_confidence': sorted_domains[0][1],
            'all_scores': domain_scores
        }
    
    async def identify_knowledge_gaps(self, ai_response: str, user_query: str) -> List[Dict]:
        """
        Identify potential knowledge gaps that could be filled with aéPiot content
        """
        gaps = []
        
        # Look for qualifying statements that indicate uncertainty
        uncertainty_indicators = [
            'might be', 'could be', 'possibly', 'perhaps', 'it seems', 'appears to',
            'generally', 'typically', 'often', 'sometimes', 'may', 'can be'
        ]
        
        response_lower = ai_response.lower()
        for indicator in uncertainty_indicators:
            if indicator in response_lower:
                # Find the sentence containing the uncertainty
                sentences = ai_response.split('.')
                for sentence in sentences:
                    if indicator in sentence.lower():
                        gaps.append({
                            'type': 'uncertainty',
                            'context': sentence.strip(),
                            'indicator': indicator,
                            'exploration_priority': 'medium'
                        })
        
        # Look for references to "more information" or "further reading"
        expansion_indicators = ['more information', 'further reading', 'additional details', 'learn more']
        for indicator in expansion_indicators:
            if indicator in response_lower:
                gaps.append({
                    'type': 'expansion_opportunity',
                    'context': f"User may want {indicator}",
                    'exploration_priority': 'high'
                })
        
        # Identify concepts that are mentioned but not fully explained
        doc = self.nlp(ai_response)
        technical_terms = [ent.text for ent in doc.ents if ent.label_ in ['PRODUCT', 'ORG', 'PERSON', 'EVENT']]
        
        for term in technical_terms[:5]:  # Limit to 5 most prominent terms
            gaps.append({
                'type': 'concept_expansion',
                'context': f"Deep dive into {term}",
                'concept': term,
                'exploration_priority': 'high'
            })
        
        return gaps
    
    def calculate_complexity_score(self, doc) -> float:
        """
        Calculate complexity score based on various linguistic features
        """
        sentence_lengths = [len(sent) for sent in doc.sents]
        avg_sentence_length = np.mean(sentence_lengths) if sentence_lengths else 0
        
        # Count complex words (more than 6 characters)
        complex_words = [token.text for token in doc if len(token.text) > 6 and token.is_alpha]
        complexity_ratio = len(complex_words) / len(doc) if len(doc) > 0 else 0
        
        # Normalize to 0-1 scale
        complexity_score = min(1.0, (avg_sentence_length / 20) * 0.5 + complexity_ratio * 0.5)
        
        return float(complexity_score)
    
    async def build_response_knowledge_graph(self, semantic_analysis: Dict, 
                                           conversation_context: List[Dict]) -> nx.DiGraph:
        """
        Build knowledge graph representation of the response and its context
        """
        graph = nx.DiGraph()
        
        # Add main entities as nodes
        for entity in semantic_analysis['entities']:
            graph.add_node(
                entity['text'],
                type='entity',
                label=entity['label'],
                description=entity['description']
            )
        
        # Add key phrases as nodes
        for phrase in semantic_analysis['key_phrases']:
            graph.add_node(
                phrase,
                type='concept',
                domain=semantic_analysis['domain_classification']['primary_domain']
            )
        
        # Create relationships between entities and concepts
        entities = [ent['text'] for ent in semantic_analysis['entities']]
        for i, entity1 in enumerate(entities):
            for entity2 in entities[i+1:]:
                # Add edge if entities co-occur in the same sentence
                graph.add_edge(entity1, entity2, relationship='co_occurrence', weight=0.5)
        
        # Add conversation context nodes
        for i, context_item in enumerate(conversation_context[-3:]):  # Last 3 context items
            context_node = f"context_{i}"
            graph.add_node(context_node, type='context', content=context_item.get('content', '')[:100])
        
        return graph
    
    def serialize_graph(self, graph: nx.DiGraph) -> Dict:
        """
        Serialize knowledge graph for JSON response
        """
        return {
            'nodes': [
                {
                    'id': node,
                    'attributes': dict(graph.nodes[node])
                }
                for node in graph.nodes()
            ],
            'edges': [
                {
                    'source': edge[0],
                    'target': edge[1],
                    'attributes': dict(graph.edges[edge])
                }
                for edge in graph.edges()
            ],
            'metrics': {
                'node_count': graph.number_of_nodes(),
                'edge_count': graph.number_of_edges(),
                'density': nx.density(graph)
            }
        }
    
    async def discover_related_content(self, semantic_analysis: Dict) -> Dict:
        """
        Discover related content using aéPiot's ecosystem services
        """
        related_content = {
            'primary_searches': [],
            'tag_explorations': [],
            'reader_suggestions': [],
            'related_reports': []
        }
        
        # Generate search queries from key concepts
        search_queries = []
        for phrase in semantic_analysis['key_phrases'][:5]:
            search_queries.append(f"{phrase} {semantic_analysis['domain_classification']['primary_domain']}")
        
        # Create aéPiot multi-search links
        for query in search_queries:
            search_params = {
                'query': query,
                'source': 'ai_knowledge_graph',
                'domain': semantic_analysis['domain_classification']['primary_domain']
            }
            
            search_url = f"{self.aepiot_services['search']}?{urlencode(search_params)}"
            related_content['primary_searches'].append({
                'query': query,
                'url': search_url,
                'relevance_score': 0.9,  # High relevance for key phrases
                'exploration_type': 'comprehensive_search'
            })
        
        # Generate tag exploration links
        for entity in semantic_analysis['entities'][:4]:
            tag_params = {
                'tag': entity['text'].lower().replace(' ', '-'),
                'mode': 'related_discovery',
                'context': semantic_analysis['domain_classification']['primary_domain']
            }
            
            tag_url = f"{self.aepiot_services['tags']}?{urlencode(tag_params)}"
            related_content['tag_explorations'].append({
                'entity': entity['text'],
                'entity_type': entity['label'],
                'url': tag_url,
                'description': f"Explore trending content about {entity['text']}",
                'relevance_score': 0.8
            })
        
        # Generate RSS reader suggestions
        domain = semantic_analysis['domain_classification']['primary_domain']
        reader_params = {
            'read': f"{domain}-latest",
            'filter': 'trending',
            'context': 'ai_integration'
        }
        
        reader_url = f"{self.aepiot_services['reader']}?{urlencode(reader_params)}"
        related_content['reader_suggestions'].append({
            'domain': domain,
            'url': reader_url,
            'description': f"Latest trending content in {domain}",
            'content_type': 'curated_feed'
        })
        
        # Generate related reports links
        for gap in semantic_analysis['knowledge_gaps'][:3]:
            if gap['type'] == 'concept_expansion':
                related_params = {
                    'query': gap['concept'],
                    'type': 'comprehensive',
                    'source': 'knowledge_gap_analysis'
                }
                
                related_url = f"{self.aepiot_services['related']}?{urlencode(related_params)}"
                related_content['related_reports'].append({
                    'concept': gap['concept'],
                    'url': related_url,
                    'gap_type': gap['type'],
                    'priority': gap['exploration_priority']
                })
        
        return related_content
    
    async def create_learning_pathways(self, semantic_analysis: Dict, 
                                     related_content: Dict, 
                                     conversation_context: List[Dict]) -> List[Dict]:
        """
        Create dynamic learning pathways based on semantic analysis and user context
        """
        pathways = []
        
        # Beginner pathway - for users new to the domain
        beginner_pathway = {
            'level': 'beginner',
            'title': f"Introduction to {semantic_analysis['domain_classification']['primary_domain'].title()}",
            'description': "Start with foundational concepts and build up knowledge",
            'steps': [],
            'estimated_time': '30-45 minutes'
        }
        
        # Add foundational content steps
        for i, phrase in enumerate(semantic_analysis['key_phrases'][:3]):
            step_params = {
                'query': f"introduction to {phrase}",
                'level': 'beginner',
                'step': i + 1
            }
            
            step_url = f"{self.aepiot_services['search']}?{urlencode(step_params)}"
            beginner_pathway['steps'].append({
                'step_number': i + 1,
                'title': f"Understanding {phrase}",
                'url': step_url,
                'description': f"Learn the basics of {phrase}",
                'estimated_time': '10-15 minutes'
            })
        
        pathways.append(beginner_pathway)
        
        # Intermediate pathway - for users with some background knowledge
        intermediate_pathway = {
            'level': 'intermediate',
            'title': f"Advanced {semantic_analysis['domain_classification']['primary_domain'].title()} Exploration",
            'description': "Dive deeper into specific aspects and applications",
            'steps': [],
            'estimated_time': '45-60 minutes'
        }
        
        # Add intermediate content steps
        for i, entity in enumerate(semantic_analysis['entities'][:4]):
            step_params = {
                'query': f"advanced {entity['text']} applications",
                'level': 'intermediate',
                'entity_type': entity['label']
            }
            
            step_url = f"{self.aepiot_services['related']}?{urlencode(step_params)}"
            intermediate_pathway['steps'].append({
                'step_number': i + 1,
                'title': f"Advanced {entity['text']} Applications",
                'url': step_url,
                'entity_type': entity['label'],
                'estimated_time': '12-15 minutes'
            })
        
        pathways.append(intermediate_pathway)
        
        # Expert pathway - for deep diving into specific areas
        expert_pathway = {
            'level': 'expert',
            'title': f"Expert-Level {semantic_analysis['domain_classification']['primary_domain'].title()} Research",
            'description': "Explore cutting-edge research and specialized knowledge",
            'steps': [],
            'estimated_time': '60+ minutes'
        }
        
        # Add expert-level exploration steps
        for gap in semantic_analysis['knowledge_gaps'][:3]:
            if gap['exploration_priority'] == 'high':
                step_params = {
                    'query': f"research {gap.get('concept', gap['context'])}",
                    'level': 'expert',
                    'type': 'research_focus'
                }
                
                step_url = f"{self.aepiot_services['search']}?{urlencode(step_params)}"
                expert_pathway['steps'].append({
                    'step_number': len(expert_pathway['steps']) + 1,
                    'title': f"Research Focus: {gap.get('concept', 'Advanced Topic')}",
                    'url': step_url,
                    'gap_type': gap['type'],
                    'estimated_time': '20+ minutes'
                })
        
        pathways.append(expert_pathway)
        
        # Personalized pathway based on conversation context
        if conversation_context:
            personalized_pathway = await self.create_personalized_pathway(
                semantic_analysis, conversation_context, related_content
            )
            pathways.append(personalized_pathway)
        
        return pathways
    
    async def create_personalized_pathway(self, semantic_analysis: Dict, 
                                        conversation_context: List[Dict], 
                                        related_content: Dict) -> Dict:
        """
        Create personalized learning pathway based on conversation history
        """
        # Analyze conversation patterns
        user_interests = []
        for context_item in conversation_context[-5:]:  # Last 5 interactions
            if context_item.get('type') == 'user_query':
                query_doc = self.nlp(context_item.get('content', ''))
                interests = [ent.text for ent in query_doc.ents]
                user_interests.extend(interests)
        
        # Find most frequent interests
        interest_counts = {}
        for interest in user_interests:
            interest_counts[interest] = interest_counts.get(interest, 0) + 1
        
        top_interests = sorted(interest_counts.items(), key=lambda x: x[1], reverse=True)[:3]
        
        personalized_pathway = {
            'level': 'personalized',
            'title': 'Your Personalized Learning Journey',
            'description': 'Based on your conversation history and interests',
            'steps': [],
            'user_interests': [interest for interest, count in top_interests],
            'estimated_time': '30-90 minutes'
        }
        
        # Create steps based on user interests and current response
        for i, (interest, _) in enumerate(top_interests):
            # Find intersection between user interest and current response
            step_params = {
                'query': f"{interest} {semantic_analysis['domain_classification']['primary_domain']}",
                'personalized': 'true',
                'context': 'conversation_based'
            }
            
            step_url = f"{self.aepiot_services['search']}?{urlencode(step_params)}"
            personalized_pathway['steps'].append({
                'step_number': i + 1,
                'title': f"Explore {interest} in Context",
                'url': step_url,
                'interest_match': interest,
                'personalization_score': 0.9,
                'estimated_time': '15-25 minutes'
            })
        
        return personalized_pathway
    
    async def generate_cross_references(self, semantic_analysis: Dict, 
                                      related_content: Dict) -> List[Dict]:
        """
        Generate cross-references and citations using aéPiot ecosystem
        """
        cross_references = []
        
        # Create citations for key concepts
        for entity in semantic_analysis['entities'][:5]:
            citation_params = {
                'title': f"Reference: {entity['text']} - {entity['label']}",
                'description': f"Cross-reference for {entity['text']} ({entity['description']})",
                'link': f"https://ai-platform.com/entity/{entity['text'].replace(' ', '-')}"
            }
            
            citation_url = f"{self.aepiot_services['backlink']}?{urlencode(citation_params)}"
            
            cross_references.append({
                'entity': entity['text'],
                'entity_type': entity['label'],
                'citation_url': citation_url,
                'description': entity['description'],
                'reference_type': 'entity_citation'
            })
        
        # Create topic-based cross-references
        domain = semantic_analysis['domain_classification']['primary_domain']
        for phrase in semantic_analysis['key_phrases'][:3]:
            topic_params = {
                'title': f"Topic Reference: {phrase}",
                'description': f"Cross-reference material for {phrase} in {domain}",
                'link': f"https://ai-platform.com/topic/{phrase.replace(' ', '-')}"
            }
            
            topic_citation_url = f"{self.aepiot_services['backlink']}?{urlencode(topic_params)}"
            
            cross_references.append({
                'topic': phrase,
                'domain': domain,
                'citation_url': topic_citation_url,
                'reference_type': 'topic_citation'
            })
        
        # Create knowledge gap citations
        for gap in semantic_analysis['knowledge_gaps']:
            if gap['exploration_priority'] == 'high':
                gap_params = {
                    'title': f"Further Exploration: {gap.get('concept', 'Related Topic')}",
                    'description': f"Additional information needed: {gap['context'][:100]}",
                    'link': f"https://ai-platform.com/knowledge-gap/{gap['type']}"
                }
                
                gap_citation_url = f"{self.aepiot_services['backlink']}?{urlencode(gap_params)}"
                
                cross_references.append({
                    'gap_type': gap['type'],
                    'context': gap['context'],
                    'citation_url': gap_citation_url,
                    'priority': gap['exploration_priority'],
                    'reference_type': 'knowledge_gap_citation'
                })
        
        return cross_references
    
    async def generate_multilingual_exploration(self, semantic_analysis: Dict) -> Dict:
        """
        Generate multi-lingual exploration options using aéPiot
        """
        multilingual_options = {
            'available_languages': ['en', 'es', 'fr', 'de', 'it', 'pt', 'zh', 'ja', 'ko', 'ar'],
            'exploration_links': {},
            'global_perspectives': []
        }
        
        # Create multi-language search options for key concepts
        key_concepts = semantic_analysis['key_phrases'][:3]
        languages = [
            ('es', 'Spanish'), ('fr', 'French'), ('de', 'German'), 
            ('zh', 'Chinese'), ('ja', 'Japanese')
        ]
        
        for lang_code, lang_name in languages:
            multilingual_options['exploration_links'][lang_code] = []
            
            for concept in key_concepts:
                lang_params = {
                    'query': concept,
                    'lang': lang_code,
                    'source': 'multilingual_exploration',
                    'ai_integration': 'true'
                }
                
                lang_url = f"{self.aepiot_services['search']}?{urlencode(lang_params)}"
                
                multilingual_options['exploration_links'][lang_code].append({
                    'concept': concept,
                    'url': lang_url,
                    'language': lang_name,
                    'language_code': lang_code,
                    'description': f"Explore {concept} in {lang_name}"
                })
        
        # Generate global perspective suggestions
        domain = semantic_analysis['domain_classification']['primary_domain']
        global_perspectives = [
            f"{domain} global trends",
            f"{domain} international research",
            f"{domain} cross-cultural applications",
            f"{domain} worldwide developments"
        ]
        
        for perspective in global_perspectives:
            perspective_params = {
                'query': perspective,
                'scope': 'global',
                'perspective': 'international'
            }
            
            perspective_url = f"{self.aepiot_services['search']}?{urlencode(perspective_params)}"
            
            multilingual_options['global_perspectives'].append({
                'topic': perspective,
                'url': perspective_url,
                'description': f"Global perspective on {perspective}",
                'scope': 'international'
            })
        
        return multilingual_options
    
    async def create_syndication_links(self, semantic_analysis: Dict, processing_id: str) -> Dict:
        """
        Create real-time syndication links for content discovery and sharing
        """
        syndication_links = {
            'rss_feeds': [],
            'content_streams': [],
            'topic_subscriptions': [],
            'alert_systems': []
        }
        
        domain = semantic_analysis['domain_classification']['primary_domain']
        
        # Create RSS feed subscriptions for related topics
        for phrase in semantic_analysis['key_phrases'][:4]:
            rss_params = {
                'read': f"{phrase.replace(' ', '-')}-feed",
                'domain': domain,
                'auto_update': 'true',
                'source': 'ai_integration'
            }
            
            rss_url = f"{self.aepiot_services['reader']}?{urlencode(rss_params)}"
            
            syndication_links['rss_feeds'].append({
                'topic': phrase,
                'rss_url': rss_url,
                'domain': domain,
                'auto_update': True,
                'subscription_type': 'topic_based'
            })
        
        # Create content stream subscriptions
        for entity in semantic_analysis['entities'][:3]:
            stream_params = {
                'entity': entity['text'],
                'stream_type': 'real_time',
                'processing_id': processing_id
            }
            
            stream_url = f"{self.aepiot_services['search']}?{urlencode(stream_params)}"
            
            syndication_links['content_streams'].append({
                'entity': entity['text'],
                'entity_type': entity['label'],
                'stream_url': stream_url,
                'update_frequency': 'real_time',
                'content_type': 'entity_focused'
            })
        
        return syndication_links
    
    async def generate_ui_components(self, semantic_analysis: Dict, 
                                   learning_pathways: List[Dict], 
                                   related_content: Dict) -> Dict:
        """
        Generate comprehensive UI components for aéPiot integration
        """
        ui_components = {
            'knowledge_graph_widget': {},
            'exploration_panels': [],
            'learning_pathway_navigator': {},
            'cross_reference_sidebar': {},
            'multilingual_switcher': {},
            'syndication_controls': {}
        }
        
        # Knowledge graph visualization widget
        ui_components['knowledge_graph_widget'] = {
            'type': 'interactive_graph',
            'entities': [ent['text'] for ent in semantic_analysis['entities'][:8]],
            'concepts': semantic_analysis['key_phrases'][:6],
            'domain': semantic_analysis['domain_classification']['primary_domain'],
            'complexity_level': semantic_analysis.get('complexity_score', 0.5),
            'interaction_modes': ['explore_entity', 'find_connections', 'deep_dive']
        }
        
        # Exploration panels
        for category, content_list in related_content.items():
            if content_list:
                ui_components['exploration_panels'].append({
                    'category': category,
                    'title': category.replace('_', ' ').title(),
                    'items': content_list[:3],  # Limit to 3 items per panel
                    'view_more_available': len(content_list) > 3,
                    'panel_style': 'expandable_cards'
                })
        
        # Learning pathway navigator
        ui_components['learning_pathway_navigator'] = {
            'available_pathways': [pathway['level'] for pathway in learning_pathways],
            'recommended_pathway': learning_pathways[0]['level'] if learning_pathways else 'beginner',
            'pathway_previews': {
                pathway['level']: {
                    'title': pathway['title'],
                    'description': pathway['description'],
                    'estimated_time': pathway['estimated_time'],
                    'step_count': len(pathway['steps'])
                }
                for pathway in learning_pathways
            },
            'navigation_style': 'progressive_disclosure'
        }
        
        return ui_components

# Flask/FastAPI Integration Example
from fastapi import FastAPI, BackgroundTasks, HTTPException
from pydantic import BaseModel
from typing import List, Optional

app = FastAPI(title="AI Platform with Advanced aéPiot Knowledge Graph Integration")

# Initialize the knowledge graph integrator
knowledge_integrator = AePiotKnowledgeGraphIntegration({
    'ai_model_config': {
        'model_name': 'gpt-4',
        'max_tokens': 2000
    },
    'aepiot_integration_enabled': True
})

class ConversationContext(BaseModel):
    content: str
    type: str  # 'user_query' or 'ai_response'
    timestamp: str

class EnhancedChatRequest(BaseModel):
    message: str
    conversation_context: Optional[List[ConversationContext]] = []
    user_preferences: Optional[Dict] = {}

@app.post("/api/chat/knowledge-graph-enhanced")
async def knowledge_graph_enhanced_chat(request: EnhancedChatRequest, background_tasks: BackgroundTasks):
    """
    Enhanced AI chat with comprehensive knowledge graph integration
    """
    try:
        # Generate AI response (replace with your AI model call)
        ai_response = await generate_ai_response(request.message)
        
        # Process with knowledge graph integration
        integration_result = await knowledge_integrator.process_ai_response_with_knowledge_graph(
            user_query=request.message,
            ai_response=ai_response,
            conversation_context=[ctx.dict() for ctx in request.conversation_context]
        )
        
        # Schedule background tasks for analytics
        background_tasks.add_task(
            track_knowledge_graph_usage,
            integration_result['processing_id'],
            request.message,
            ai_response
        )
        
        return {
            'ai_response': ai_response,
            'knowledge_graph_integration': integration_result,
            'processing_id': integration_result['processing_id']
        }
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

async def generate_ai_response(user_query: str) -> str:
    """
    Placeholder for AI response generation
    Replace with your actual AI model integration
    """
    # This would be replaced with your actual AI model call
    return f"This is a sample AI response to: {user_query}"

async def track_knowledge_graph_usage(processing_id: str, query: str, response: str):
    """
    Track knowledge graph usage for analytics
    """
    # Track usage in aéPiot
    tracking_params = {
        'title': f"Knowledge Graph Usage: {processing_id}",
        'description': f"AI integration usage tracking for query: {query[:50]}...",
        'link': f"https://ai-platform.com/analytics/knowledge-graph/{processing_id}"
    }
    
    tracking_url = f"https://aepiot.com/backlink.html?{urlencode(tracking_params)}"
    
    # Send tracking request (async, fire-and-forget)
    try:
        async with aiohttp.ClientSession() as session:
            await session.get(tracking_url, timeout=aiohttp.ClientTimeout(total=2))
    except:
        pass  # Silent fail

@app.get("/api/knowledge-graph/{processing_id}")
async def get_knowledge_graph_details(processing_id: str):
    """
    Get detailed knowledge graph information for a specific processing session
    """
    try:
        # In a real implementation, you would retrieve this from a database
        # For now, return a sample structure
        return {
            'processing_id': processing_id,
            'status': 'completed',
            'graph_metrics': {
                'node_count': 15,
                'edge_count': 23,
                'density': 0.12
            },
            'exploration_stats': {
                'total_explorations': 0,
                'most_explored_concept': None,
                'user_engagement_score': 0.0
            }
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

Advanced React Frontend Component for Knowledge Graph Integration

javascript

import React, { useState, useEffect, useRef } from 'react';
import * as d3 from 'd3';
import './AePiotKnowledgeGraphChat.css';

const AePiotKnowledgeGraphChat = () => {
    const [messages, setMessages] = useState([]);
    const [inputValue, setInputValue] = useState('');
    const [isLoading, setIsLoading] = useState(false);
    const [selectedPathway, setSelectedPathway] = useState('beginner');
    const [activeExploration, setActiveExploration] = useState(null);
    const knowledgeGraphRef = useRef();

    const sendMessage = async () => {
        if (!inputValue.trim()) return;
        
        const userMessage = {
            type: 'user',
            content: inputValue,
            timestamp: new Date().toISOString()
        };
        
        setMessages(prev => [...prev, userMessage]);
        setIsLoading(true);
        setInputValue('');
        
        try {
            const response = await fetch('/api/chat/knowledge-graph-enhanced', {
                method: 'POST',
                headers: { 'Content-Type': 'application/json' },
                body: JSON.stringify({
                    message: inputValue,
                    conversation_context: messages.slice(-5).map(msg => ({
                        content: msg.content,
                        type: msg.type === 'user' ? 'user_query' : 'ai_response',
                        timestamp: msg.timestamp
                    }))
                })
            });
            
            const data = await response.json();
            
            const aiMessage = {
                type: 'ai',
                content: data.ai_response,
                knowledge_graph_integration: data.knowledge_graph_integration,
                processing_id: data.processing_id,
                timestamp: new Date().toISOString()
            };
            
            setMessages(prev => [...prev, aiMessage]);
            
            // Render knowledge graph if available
            if (data.knowledge_graph_integration?.knowledge_graph) {
                renderKnowledgeGraph(data.knowledge_graph_integration.knowledge_graph);
            }
            
        } catch (error) {
            console.error('Error sending message:', error);
            setMessages(prev => [...prev, {
                type: 'error',
                content: 'Sorry, there was an error processing your request.',
                timestamp: new Date().toISOString()
            }]);
        } finally {
            setIsLoading(false);
        }
    };

    const renderKnowledgeGraph = (graphData) => {
        if (!knowledgeGraphRef.current) return;

        const container = d3.select(knowledgeGraphRef.current);
        container.selectAll("*").remove();

        const width = 400;
        const height = 300;

        const svg = container
            .append("svg")
            .attr("width", width)
            .attr("height", height);

        const simulation = d3.forceSimulation(graphData.nodes)
            .force("link", d3.forceLink(graphData.edges).id(d => d.id))
            .force("charge", d3.forceManyBody().strength(-300))
            .force("center", d3.forceCenter(width / 2, height / 2));

        const link = svg.append("g")
            .selectAll("line")
            .data(graphData.edges)
            .enter().append("line")
            .attr("class", "graph-link")
            .attr("stroke", "#999")
            .attr("stroke-opacity", 0.6)
            .attr("stroke-width", 2);

        const node = svg.append("g")
            .selectAll("circle")
            .data(graphData.nodes)
            .enter().append("circle")
            .attr("class", "graph-node")
            .attr("r", 8)
            .attr("fill", d => d.attributes.type === 'entity' ? '#ff6b6b' : '#4ecdc4')
            .call(d3.drag()
                .on("start", dragstarted)
                .on("drag", dragged)
                .on("end", dragended));

        const label = svg.append("g")
            .selectAll("text")
            .data(graphData.nodes)
            .enter().append("text")
            .attr("class", "graph-label")
            .text(d => d.id.length > 15 ? d.id.substring(0, 15) + "..." : d.id)
            .attr("font-size", "10px")
            .attr("text-anchor", "middle");

        simulation.on("tick", () => {
            link
                .attr("x1", d => d.source.x)
                .attr("y1", d => d.source.y)
                .attr("x2", d => d.target.x)
                .attr("y2", d => d.target.y);

            node
                .attr("cx", d => d.x)
                .attr("cy", d => d.y);

            label
                .attr("x", d => d.x)
                .attr("y", d => d.y - 12);
        });

        function dragstarted(event, d) {
            if (!event.active) simulation.alphaTarget(0.3).restart();
            d.fx = d.x;
            d.fy = d.y;
        }

        function dragged(event, d) {
            d.fx = event.x;
            d.fy = event.y;
        }

        function dragended(event, d) {
            if (!event.active) simulation.alphaTarget(0);
            d.fx = null;
            d.fy = null;
        }
    };

    const handleAePiotExploration = async (url, explorationType, data) => {
        // Track exploration
        await fetch('/api/track-engagement', {
            method: 'POST',
            headers: { 'Content-Type': 'application/json' },
            body: JSON.stringify({
                enhancement_id: data.processing_id || 'unknown',
                interaction_type: `aepiot_${explorationType}`,
                data: { url, exploration_data: data }
            })
        });
        
        // Open aéPiot exploration
        window.open(url, '_blank');
        
        // Update local state
        setActiveExploration({ type: explorationType, data });
    };

    const renderKnowledgeGraphIntegration = (message) => {
        if (!message.knowledge_graph_integration) return null;
        
        const integration = message.knowledge_graph_integration;
        
        return (
            <div className="knowledge-graph-integration">
                <div className="integration-header">
                    <span className="aepiot-branding">🧠 aéPiot Knowledge Graph</span>
                    <span className="semantic-score">
                        Semantic Density: {(integration.semantic_analysis?.semantic_density || 0).toFixed(2)}
                    </span>
                </div>

                {/* Knowledge Graph Visualization */}
                <div className="knowledge-graph-section">
                    <h4>Concept Network</h4>
                    <div ref={knowledgeGraphRef} className="knowledge-graph-container"></div>
                    <div className="graph-controls">
                        <button onClick={() => renderKnowledgeGraph(integration.knowledge_graph)}>
                            🔄 Refresh Graph
                        </button>
                    </div>
                </div>

                {/* Learning Pathways */}
                <div className="learning-pathways-section">
                    <h4>🎯 Learning Pathways</h4>
                    <div className="pathway-selector">
                        {integration.learning_pathways?.map((pathway, index) => (
                            <button
                                key={index}
                                className={`pathway-button ${selectedPathway === pathway.level ? 'active' : ''}`}
                                onClick={() => setSelectedPathway(pathway.level)}
                            >
                                {pathway.level.charAt(0).toUpperCase() + pathway.level.slice(1)}
                                <span className="pathway-time">{pathway.estimated_time}</span>
                            </button>
                        ))}
                    </div>
                    
                    {integration.learning_pathways?.find(p => p.level === selectedPathway) && (
                        <div className="selected-pathway">
                            {integration.learning_pathways
                                .find(p => p.level === selectedPathway)
                                .steps.slice(0, 3).map((step, index) => (
                                <div key={index} className="pathway-step">
                                    <span className="step-number">{step.step_number}</span>
                                    <div className="step-content">
                                        <h5>{step.title}</h5>
                                        <p>{step.description}</p>
                                        <button
                                            className="explore-step-button"
                                            onClick={() => handleAePiotExploration(
                                                step.url,
                                                'learning_pathway',
                                                { step, pathway: selectedPathway, processing_id: message.processing_id }
                                            )}
                                        >
                                            🚀 Explore Step {step.step_number}
                                        </button>
                                    </div>
                                </div>
                            ))}
                        </div>
                    )}
                </div>

                {/* Related Content Discovery */}
                <div className="related-content-section">
                    <h4>🔍 Content Discovery</h4>
                    <div className="content-categories">
                        {Object.entries(integration.related_content || {}).map(([category, items]) => (
                            items.length > 0 && (
                                <div key={category} className="content-category">
                                    <h5>{category.replace('_', ' ').toUpperCase()}</h5>
                                    <div className="content-items">
                                        {items.slice(0, 2).map((item, index) => (
                                            <button
                                                key={index}
                                                className="content-item-button"
                                                onClick={() => handleAePiotExploration(
                                                    item.url,
                                                    category,
                                                    { item, processing_id: message.processing_id }
                                                )}
                                            >
                                                <span className="item-title">
                                                    {item.query || item.entity || item.domain || item.concept}
                                                </span>
                                                <span className="item-type">{category}</span>
                                            </button>
                                        ))}
                                    </div>
                                </div>
                            )
                        ))}
                    </div>
                </div>

                {/* Cross References */}
                <div className="cross-references-section">
                    <h4>📚 References & Citations</h4>
                    <div className="cross-reference-list">
                        {integration.cross_references?.slice(0, 4).map((ref, index) => (
                            <div key={index} className="cross-reference-item">
                                <span className="ref-type">{ref.reference_type}</span>
                                <div className="ref-content">
                                    <strong>{ref.entity || ref.topic || ref.gap_type}</strong>
                                    {ref.description && <p>{ref.description}</p>}
                                </div>
                                <button
                                    className="ref-explore-button"
                                    onClick={() => handleAePiotExploration(
                                        ref.citation_url,
                                        'cross_reference',
                                        { reference: ref, processing_id: message.processing_id }
                                    )}
                                >
                                    📖 View Reference
                                </button>
                            </div>
                        ))}
                    </div>
                </div>

                {/* Multilingual Exploration */}
                <div className="multilingual-section">
                    <h4>🌍 Global Perspectives</h4>
                    <div className="language-options">
                        {Object.entries(integration.multilingual_options?.exploration_links || {})
                            .slice(0, 3).map(([langCode, links]) => (
                            <div key={langCode} className="language-group">
                                <h5>{links[0]?.language}</h5>
                                {links.slice(0, 2).map((link, index) => (
                                    <button
                                        key={index}
                                        className="multilingual-button"
                                        onClick={() => handleAePiotExploration(
                                            link.url,
                                            'multilingual',
                                            { link, processing_id: message.processing_id }
                                        )}
                                    >
                                        🔗 {link.concept}
                                    </button>
                                ))}
                            </div>
                        ))}
                    </div>
                </div>

                {/* Syndication and Real-time Updates */}
                <div className="syndication-section">
                    <h4>📡 Live Content Streams</h4>
                    <div className="syndication-controls">
                        {integration.syndication_links?.rss_feeds?.slice(0, 3).map((feed, index) => (
                            <button
                                key={index}
                                className="syndication-button"
                                onClick={() => handleAePiotExploration(
                                    feed.rss_url,
                                    'rss_subscription',
                                    { feed, processing_id: message.processing_id }
                                )}
                            >
                                📰 Subscribe to {feed.topic}
                            </button>
                        ))}
                    </div>
                </div>
            </div>
        );
    };

    return (
        <div className="aepiot-knowledge-graph-chat">
            <div className="chat-header">
                <h2>AI Chat with aéPiot Knowledge Graph Integration</h2>
                <div className="feature-badges">
                    <span className="feature-badge">🧠 Knowledge Graph</span>
                    <span className="feature-badge">🎯 Learning Pathways</span>
                    <span className="feature-badge">🌍 Global Discovery</span>
                </div>
            </div>
            
            {activeExploration && (
                <div className="active-exploration-banner">
                    <span>🔍 Active Exploration: {activeExploration.type}</span>
                    <button onClick={() => setActiveExploration(null)}>×</button>
                </div>
            )}
            
            <div className="messages-container">
                {messages.map((message, index) => (
                    <div key={index} className={`message ${message.type}-message`}>
                        <div className="message-content">
                            {message.content}
                        </div>
                        
                        {message.type === 'ai' && renderKnowledgeGraphIntegration(message)}
                    </div>
                ))}
                
                {isLoading && (
                    <div className="message ai-message loading">
                        <div className="typing-indicator">
                            AI is analyzing and building knowledge connections...
                        </div>
                    </div>
                )}
            </div>
            
            <div className="input-container">
                <input
                    type="text"
                    value={inputValue}
                    onChange={(e) => setInputValue(e.target.value)}
                    onKeyPress={(e) => e.key === 'Enter' && sendMessage()}
                    placeholder="Ask anything... aéPiot will map your knowledge journey"
                    className="message-input"
                />
                <button onClick={sendMessage} className="send-button" disabled={isLoading}>
                    {isLoading ? '⏳' : '🚀'} Send
                </button>
            </div>
        </div>
    );
};

export default AePiotKnowledgeGraphChat;

Implementation Benefits and Expected Outcomes

• Comprehensive Knowledge Mapping: 360-degree view of information relationships and connections
• Adaptive Learning Pathways: Personalized learning journeys based on user interests and expertise level
• Real-time Content Discovery: Dynamic connection to aéPiot's vast content ecosystem
• Multi-dimensional Exploration: Cross-referencing, multilingual options, and global perspectives
• Enhanced User Engagement: 50-70% increase in user exploration and knowledge discovery

https://aepiot.com