Traditional Search Methods
Keyword-Based Search:
- Relies on matching user queries with text in web documents.
- The limitations of this is that it struggles with understanding context, synonyms, and non-textual data.
Boolean Search:
- Uses operators like AND, OR, NOT to refine searches.
- The limitations of this is that it requires precise query formulation and doesn't handle natural language well.
Semantic Search Methods
Ontology-Based Search: Utilizes ontologies to understand relationships between concepts.
Searching for "heart attack" also returns results for "myocardial infarction" due to their semantic equivalence.
Natural Language Processing (NLP): Analyzes user queries to understand intent and context.
Interprets "best Italian restaurants near me" by identifying the type of cuisine and location context.
Linked Data and RDF: Uses RDF triples to connect related data across the web.
A search for "Barack Obama" retrieves information from multiple sources linked by RDF.
Searching Non-Text-Based Files
Feature Analysis: Extracts features from multimedia files (e.g., color, shape, texture) for indexing and retrieval.
Image search engines use feature vectors to find visually similar images.
Metadata Tagging: Associates descriptive metadata with multimedia files.
Audio files tagged with genre, artist, and mood for easier retrieval.
Content-Based Retrieval: Analyzes the actual content of files rather than relying on metadata.
Video search engines analyze scenes and objects within videos to match user queries.
Challenges and Considerations
- Data Integration: Combining data from heterogeneous sources with varying formats and standards.
- Scalability: Handling the vast amount of data on the web while maintaining search efficiency.
- Privacy and Security: Ensuring user data is protected during personalized search processes.
- Accuracy and Relevance: Balancing precision and recall to provide the most relevant results without overwhelming users.
