Woter AI detection.Hurry - ends Jul 20th

New Year Sales :up to 80% OFF

AI Humanize AI Translator Bypass AI AI Rewriter AI Detector

PRICING

TRY FOR FREE

What is multimodal reasoning based on knowledge graph?

Written by

Iris Vance

Updated on:June-22nd-2025

1. Multimodal reasoning foundation: Let AI learn to "see, hear, and think"

1. What is multimodal reasoning? ‌

Multimodal reasoning refers to the process by which machines derive implicit conclusions by integrating information from multiple sensory modalities (such as text, images, audio, video, etc.) and combining logical analysis with semantic understanding. Just as humans infer that it is about to rain when they see dark clouds, AI can also predict weather changes by analyzing dark clouds in images and data from wind speed sensors.

2. Multimodal reasoning vs unimodal reasoning

Dimensions	Multimodal Reasoning	Unimodal Reasoning
Input Source	Integrate text, images, audio, etc.	A single source of data (such as plain text)
Advantages	Information complementarity and strong anti-interference ability	Simple calculation and fast response
limitation	Data alignment is difficult	Susceptible to information loss
Typical Cases	Autonomous driving (LiDAR + Camera)	Text Sentiment Analysis

3. Three characteristics of multimodal reasoning

Complementarity: Different modal information complements each other (e.g. action + voice commentary in a video)

‌Semantic Relevance‌: Semantic alignment across modalities (e.g. text description of “cat” with pictures of cats)

‌Dynamic‌: Real-time integration of streaming data (such as blackboard writing + voice explanation in educational live broadcasts)

‌4. Common modal combination cases

Combination form	Application Scenario
Image + Text	Medical imaging diagnostic report generation
Audio + Video	Real-time minutes of intelligent meetings
Sensor Data + Maps	Logistics robot path planning

2. What is a knowledge graph?

1. Definition of Knowledge Graph‌

The knowledge graph is a structured database with entity-relationship-attribute triples as its core. It is essentially a huge semantic network. For example, in the medical field, "aspirin-treatment-headache" constitutes a triple.

2. Components of the knowledge graph‌

Entity: a real-world object (e.g. "The Palace Museum")

‌Relationship‌: The connection between entities (e.g. "located in - Beijing")

‌Attributes‌: Characteristics of an entity (e.g. "Year Built — 1420")

3. Structured representation

Visual expression is achieved through RDF (Resource Description Framework) or graph databases (such as Neo4j) to form a spider-web-like association network.

3. When multimodal reasoning meets knowledge graph

1. How to build a multimodal knowledge graph? ‌

‌Multi-source data collection‌:

Text: textbooks, papers, online encyclopedias
Visual: teaching video, experimental process video
Audio: Classroom recordings, audio Q&A
Sensor: Laboratory temperature/pressure data

‌Cross-modal alignment‌:

Use models such as CLIP to align image and text semantics
Establish a mapping between "physics experiment video frames" and "formula derivation steps"

‌Knowledge Fusion and Storage‌:

Storing vectorized data in a graph database
Define cross-modal relationships (e.g. “5:30 in the video → Verification of Newton’s third law”)

‌Dynamic update mechanism‌:

Real-time access to student interaction data on online education platforms
Automatically expand newly discovered causal relationships (such as "operational error → experimental phenomenon abnormality")

2. Advantages of Combination‌

Enhanced understanding: When seeing a shadow in an X-ray, retrieve medication records for similar cases simultaneously

‌Supports complex reasoning‌: Combine weather data + road surveillance video to predict traffic accident risks

‌Dynamic knowledge update‌: When new species appear in the live broadcast, the knowledge graph will be automatically expanded

3. Typical application scenarios in the Internet IT industry‌

‌3.1 Intelligent Code Review System

Traditional code review relies on manual line-by-line inspection, which is time-consuming and prone to missing multi-module collaboration issues.

Multimodal data integration:

Code text (development documents/commit records)
System log (operation error information timestamp)
Screen operation video (developer debugging process)

‌Knowledge Graph Applications‌:

Build a code security rule map (CWE vulnerability library + enterprise coding standards)
Related historical accident cases (such as "concurrent lock not released → system deadlock" event chain)

‌Smart Output‌:

Automatically annotate risky code snippets (such as unencrypted API keys)
Generate a 3D visual call chain diagram
Push association fix solution (including Stack Overflow high vote answer)

3.2 Operation and maintenance of fault self-healing system

The average time to locate a data center fault is more than 45 minutes, and the MTTR (mean time to recovery) remains high.

Multimodal perception matrix:

Computer room surveillance video (equipment indicator light status)
Log text (ERROR/WARNING keywords)
Sensor data (CPU temperature/network latency)
Voice recording (communication information between on-duty personnel)

‌Knowledge Graph Empowerment‌:

Establish a fault mode library (e.g. "hard drive red light flashes → RAID5 array degraded")
Topology diagram (physical server → virtual machine → container → microservice)

‌Smart Response‌:

Real-time warning: "An abnormal temperature of the A3 cabinet is detected, and the traffic of the associated B2 switch surges"
Automatically execute the plan: isolate abnormal Pods → trigger elastic expansion → notify relevant persons in charge by email
Generate fault tracing report (including timeline and root cause analysis)

3.3 Interconnection of knowledge graphs across systems

Enterprise-level systems have information islands, and CRM/ERP/SCM data are difficult to coordinate

‌Multimodal Access‌:

Structured data (database table/API interface)
Unstructured data (meeting minutes/email exchanges)
Visual data (business process diagram/architecture design diagram)
Behavioral data (user click stream/permission change records)

‌Graph Construction‌:

Entity alignment: unify the naming differences of "customer ID" in different systems
Relationship mining: Discovering the implicit association of "delayed purchase order → production line shutdown"
Dynamic Update: Real-time synchronization of JIRA task status and Jenkins build log

‌Smart Application‌:

Impact analysis of demand changes: Modify the payment interface → Alert involves 12 microservices
Smart Q&A: "Show all suppliers and their contacts with abnormal purchases in the last three months"
Business process mining: Automatically generate ITIL service desk optimization suggestions (based on 5000+ event logs)

3‌.4. AI training data governance platform‌

The quality of training data for machine learning models varies greatly, and annotation costs account for more than 60% of the total budget

‌Multimodal Quality Inspection‌:

Image data (detection of annotation box offset/occlusion issues)
Text data (identifying inconsistent NER annotations)
Audio data (to verify the accuracy of speech transcription alignment)
Video data (tracking action annotation continuity)

‌Knowledge Graph Support‌:

Build a data lineage map (original data → enhanced version → model version)
Labeling specification knowledge base (labeling rule trees in different scenarios)

‌Intelligent Efficiency‌:

Automatically fix common errors: correct 15% of mislabeled bounding boxes
Intelligent augmentation: Generate scarce samples based on scene graphs (such as "nighttime rainy and foggy weather" traffic sign images)
Cost prediction: Recommend the best labeling solution based on task complexity (manual vs semi-automatic)

3‌.5. Automated Collaborative Knowledge Hub‌

The efficiency of knowledge transfer in remote teams decreases, and new employees need an average of 3 months to familiarize themselves with the system architecture.

‌Multimodal knowledge accumulation‌:

Code annotation map (function → call relationship → design intent)
Conference video key frame extraction (architecture diagram modification process)
Instant messaging semantic analysis (extracting key points of technical decision-making)
Document version difference comparison (requirements change track)

‌Intelligent Services‌:

Newbie guide: 3D decomposition animation of the core modules of the playback system
Smart search: "Show the decision records of the last three reconstructions of the gateway authentication module"
Knowledge recommendation: push related design pattern cases based on current tasks

Through the deep integration of multimodal reasoning and knowledge graphs, the IT industry is evolving from "manual operation and maintenance" to "cognitive operation and maintenance", building an intelligent system organism with self-repair and self-optimization capabilities.

4. Reasoning Method

1. Comparison of mainstream reasoning methods

Reasoning Type	Features	Applicable scenarios
Reasoning by analogy	Deriving conclusions through similarity	Legal case matching and product recommendations
Inductive reasoning	Derivation from the specific to the general	Discovery of scientific research rules and analysis of user behavior
Abductive reasoning	Infer the cause based on the result	Medical diagnosis, equipment troubleshooting

2. Three factors in choosing a reasoning method

‌ Data characteristics‌: Structured data is suitable for deductive reasoning, while unstructured data requires multimodal analysis

‌Task Objective‌: Precise answers require deterministic reasoning, open questions are suitable for probabilistic reasoning
‌Real-time requirements‌: Fast reasoning algorithms are preferred in emergency scenarios

5. Knowledge Graph Empowers Test Development

Are you experiencing these testing woes? ‌

❌Facing complex business systems, manually writing test cases is time-consuming and labor-intensive

❌Automated test scripts have high maintenance costs and require reconstruction when business changes occur

❌Defect prediction relies on experience and cannot accurately locate related modules

❌Analyzing performance test results is like looking for a needle in a haystack, and it is difficult to find the deep bottleneck

?‌Industry's first "knowledge graph + test development" in-depth integration course [AI test development training camp]

What can you do after learning?

✅‌Intelligent use case generation‌: Automatically derive test scenarios based on business graphs (reduce 70% of repetitive work)

✅‌Defect root cause analysis‌: Locate the source of the problem in seconds by calling the chain graph

✅‌Test asset reuse‌: Build an enterprise-level test knowledge base (increase new employee efficiency by 65%)

✅‌Performance bottleneck prediction‌: Use resource dependency graphs to predict system weaknesses