Today, it has become a really tedious task to manage and find the correct content from the vast digital video libraries with the rising trend of AI in media search. However, these AI models may not deliver optimal performances.
One such important issue is “AI hallucination,” where the output generated by the AI can be misleading or factually incorrect due to lack of real-world context. In this point, it becomes necessary for knowledge graphs to enhance the ability of AI in retrieving actual media content with minimized errors.
In this blog, we’ll discuss the ways in which knowledge graphs have changed the face of advanced media search, especially in Media Asset Management (MAM), to make possible such structured and contextualized information to enrich input with search relevancy.
What is a Knowledge Graph?
A knowledge graph refers to a system that contains the real-world identity and relationships between it. Such a kind of arrangement links data points in a significant manner, showing the contextual message over isolated blocks.
For example, in the instance of a movie, a knowledge graph would link information about actors, characters, production details, genres, locations, and so on, with the AI “understanding” how the things correlate.
These have been used by giants like Google and Facebook to develop their search engines or with content recommendation engines. In a lot of ways, these graphs can also be put to good use and significantly refined when used with media searches, as they allow linking and associating concepts such as linking a given scene from a video to involved characters, underlying themes, or even the spoken dialogue.
The Role of Knowledge Graphs in Media Asset Management Search ?
At present, the challenge posed by very large video libraries in Media Asset Management (MAM) lies significantly in retrieval and search accuracy. Traditional artificial intelligence search algorithms mainly depend upon text data or metadata, which has high chances of going wrong if the metadata is incomplete or in cases where visual content is misinterpreted by this artificial intelligence.
The combination of knowledge with AI will be such that it helps the system walk through understanding richer contexts about the content in the media database.
Here is how knowledge graphs enhance media searching:
1. Contextual Understanding:
These AI models can do nothing to extract video content on descriptions if they do not know the relationships between the entities beforehand, and therefore, just that won’t help achieve results. Knowledge graphs place a fourth dimension on this understanding and make all the difference when it comes to search term relevance and content.
Specifically, if you search “football” in the knowledge graph, it helps to identify an associated article in sports and a movie in which the character’s name is “Football.”
2. Entity Recognition and Linking:
These use AI to identify entities within that video (such as actors, or scenes/events). Now connect an entity like “Actor A” to the relationship of “Scene B. This forms a complete web of connections that the AI can use to pull relevant media results based on associations of entities.
3. Search Results Disambiguation:
One problem that has vexed all models is AI hallucination, in which a search result can be conflated by information. Knowledge graphs, in contrast, can refine results by checking if the search aligns with structured data.
Where an AI does not also know who’s in what scene, the knowledge graph will cross-verify via location, context, or dialogue, which may also be contained within the database as attributes.
4. Dynamic Updates and Continuous Learning:
Knowledge graphs cannot be just finished and forgotten technology. Due to the dynamic nature of this technology, with the newly added media content into the MAM system, the knowledge graph will be updated, which, in turn, will improve future search results. The main idea is that the AI behind those graphs learns from the media being added, and the developing graph is becoming more and more accurate for further searches.
5. Combining AI with knowledge graphs:
Despite this AI model’s use, knowledge graphs, as complementary systems, ensure that the search makes sense because it is based on the reality around us. The good thing about AI is that it can perceive any data from an image, audio, or video. However, the knowledge graph connects such data following factual associations. Therefore, by working together, the media searches will become extremely user-friendly and, at the same time, will ensure the least amount of hallucination accuracy.
Reducing AI Hallucinations
AI hallucinations happen when the AI over-interprets or makes up information regarding content because it has not been grounded in reality. By using knowledge graphs, we ensure that the AI search engine cross-references search results with real-world facts and relationships. This reduces the chance of errors and improves the quality of search results.
How Knowledge Graphs Function in Advanced Media Search.
To explain how knowledge graphs work in a media search environment, let’s start with breaking down the workflow that generally follows:
- Video Library Integration: The AI models use descriptive metadata from analyzing the media library, for example, character names, objects, or themes.
- Entity Extraction: Key entities like actors, events, or locations are identified and mapped to a knowledge graph from video content.
- Relationship mapping: Entities are connected using meaningful relationships, for instance, an actor playing a character at a particular scene
- Query processing: When the search query is submitted, it utilizes both the metadata produced from AI and the knowledge graph in producing the desired output.
- Verification of the outcome: Knowledge graph verifies whether or not the result fits actual realities. This prevents possible instances of hallucination and misinformation.
Advantages of Using Knowledge Graphs in Media Search.
- AI and knowledge graphs improve search accuracy by adding context to isolated data points.
- Knowledge graphs enrich media search by providing results that are closer to the user’s intent.
- Since there are fewer AI hallucinations, media search becomes reliable.
- As media libraries grow, knowledge graphs keep updating, thus improving the system’s search capabilities over time.
Conclusion:
As media libraries grow and the demand for intelligent media search increases, knowledge graphs will become an inevitable tool in ensuring that not only is the search fast, but it is also factually correct, thereby reducing the potential for AI hallucinations.
To learn more about how knowledge graphs and AI can enhance your media search experience, visit our website at Gyrus AI and explore the cutting-edge solutions we offer.
