How Search Engines use Graph Concepts

An overview of various Graph techniques that enhance the performance of Search Engines

Search engines are the backbone of the internet, enabling us to find information and navigate the vast web of interconnected pages and resources. But with so much data available, how do search engines manage to organize and retrieve all this information? The answer lies in graphs — specifically, the use of graph theory and graph databases to model relationships between entities, index web pages, and provide relevant search results. In this blog post, we will explore how graphs are used in search engines, covering topics such as entity linking, semantic search, link analysis, personalization and knowledge graphs.

Graph Databases

One of the key ways that search engines use graphs is through the use of graph databases. These are databases that store data as nodes (representing entities or objects) and edges (representing relationships between those entities). By representing data in this way, search engines can easily traverse relationships between entities, allowing for more efficient querying and retrieval of relevant information.

To understand the concept of graph databases better, let us take an example of recommending Sushi restaurants for a person based on the relationships connecting people, location, cuisines, and restaurants.

Thus, the person in consideration gets 2 recommendations. A search engine could use a graph database to quickly find all the restaurants in the city, and then filter those results based on factors such as location, cuisine, and ratings. Without a graph database, this type of query would be much slower and less precise.

Entity Linking

Another way that graphs are used in search engines is through entity linking. This involves identifying entities (such as people, places, and organizations) within web pages and linking them to their corresponding nodes in a knowledge graph. By doing so, search engines can better understand the relationships between entities and provide more relevant search results.

For example, if a web page mentions “Donald Trump”, an entity linking algorithm could identify this as the president of the United States and link it to the corresponding node in a knowledge graph. This would allow the search engine to provide more accurate search results related to Donald Trump, such as news articles, biographical information, and related topics.

When we pluralize the term “President”, it connects multiple individuals as entities who are associated with the position of the President. Consequently, a search for the plural term will display all of these individuals.

Link Analysis

PageRank, an algorithm employed by Google to rank web pages based on their links, is one of the earliest and most renowned applications of graph theory in search engines. This algorithm is founded on the principle that a web page is deemed more significant if it is linked to by other important web pages. By examining the interconnections between web pages, PageRank can evaluate their importance and relevance.

But PageRank is just one example of the many link analysis algorithms that are used in modern search engines. Other algorithms, such as HITS and SALSA, use more sophisticated approaches to analyzing links and providing relevant search results.

Knowledge Graphs

Perhaps the most exciting application of graphs in search engines is the use of knowledge graphs. Knowledge graphs are large-scale, structured databases that model the relationships between entities, concepts, and facts. By building a knowledge graph, search engines can provide a more comprehensive and accurate representation of the world, allowing for more sophisticated search queries and recommendations.

For example, Google’s knowledge graph includes over 500 billion facts about 5 billion entities, ranging from famous people and places to historical events and scientific concepts. By using a knowledge graph, Google can answer complex queries such as “Who is the CEO of Microsoft?” or “What is the capital of France?” with ease.

Semantic Search and Knowledge Graph

Semantic search is an advanced technology that improves the precision of our internet searches by using various techniques to retrieve knowledge from diverse and structured data sources. By transforming both structured and unstructured data into a more responsive and intuitive knowledge paradigm called a “knowledge graph,” semantic search provides highly contextual and personalized search results.

The knowledge graph is a key component of semantic search that enables it to generate more significant results. It combines a variety of data related to entities and concepts within a particular domain, along with their interrelationships, into a single, integrated system. As a result, the knowledge graph creates an extensive network of interconnected facts that can be explored in a multitude of ways.

To enhance free-text search capabilities, knowledge graphs must be complemented with text analysis. This involves using semantic annotation and indexing techniques to identify the specific concepts from the knowledge graph that are referenced in the text. Instead of relying solely on ambiguous strings, documents are indexed based on properly identified entities and concepts, allowing for more accurate and precise search results.

Through the analysis of the concepts within a query and their interrelationships, semantic search can generate relevant results even if the wording of the query is not an exact match with the search results.

Personalization

Personalization in search engines refers to the process of tailoring search results to an individual user’s preferences and interests. Graphs play a critical role in achieving personalization in search engines.

Graphs are utilized to represent the connections between various entities, such as web pages, users, queries, and interests. Search engines leverage this data to construct a user profile that includes their search history, interests, and social relationships. Using this profile, the search engine can deliver personalized search results that cater to the user’s preferences.

For example, if a user frequently shares posts about hiking on social media, search engines may prioritize hiking-related search results for that user.

Conclusion

You can to play around with graphs and graph databases. Neo4j is a cloud database platform where you can create your own graph database and work with it using queries.

This is an example of a graph database created in Neo4j. In this example you can see the connection between movies (in green) and their cast (in brown).

To conclude, graphs are an essential tool for search engines, enabling them to model relationships between entities, understand the semantics of search queries, personalize search results, and provide more relevant recommendations. Graph databases, entity linking, semantic search, personalization, link analysis and knowledge graphs are just a few examples of the many ways that graphs are used in search engines. As search engines continue to evolve and become more sophisticated, we can expect to see even more exciting applications of graphs in the future.

Authors: Siddhi Patil, Mrunmayee Phadke, Rahul Sundkar, Rajkumar Dongre, Atharva Raut.