What Are Knowledge Graphs?
Researchers are always trying to find better ways to organise, interpret, and interlink information. One method that, over the years, has been embraced across a variety of domains is the knowledge graph—a framework for analysing data based on the relationship between concepts. But what is a knowledge graph?
Summary: A knowledge graph is represented knowledge that connects entities (people, places, concepts, etc.) by relationships. Unlike traditional databases that require data to be represented in a structured format, knowledge graphs can be represented in a network-like structure, making it easier to model complex relationships.
Fundamentally, a knowledge graph is made up of:
Nodes (entities): Denote real-world phenomena, like ‘climate change’ or ‘carbon emissions’ or ‘policy impact’.
Edges (relationships): What connects entities together describing how they interact (e.g., “influences,” “caused by,” “is part of”).
Attributes (or metadata): Additional information about entities (e.g., publication year, author, or location).
The structure allows researchers to see relationships, find hidden patterns, and more important information that may not be readily found in traditional methods of analysis.
Why Knowledge Graphs Are Beneficial to Researchers
Better Literature Reviews:
Academic research must build on what has been done before, and it can benefit from using knowledge graphs to map how ideas, theories, and citations connect with one another. Instead of having to manually sift through papers, a knowledge graph can show influential works, track the evolution of concepts over time, and identify gaps in research.
Improved Data Integration:
Many researchers use datasets that come from a range of sources, including journal articles, surveys, and public databases. Knowledge graphs are a general framework for the integration of heterogeneous data. This form of data representation makes relationships from different domains easier to analyse.
More Accurate Text Analysis:
That being said, Natural Language Processing (NLP) tools are frequently challenged by the ambiguity within language itself. Personalisation using knowledge from large datasets results in improved text analysis as it provides more context to AI-driven tools that assist in better understanding of the context, terms, synonyms, and relationships. Especially in qualitative research and bibliometric analysis, this is of great help.
Facilitating Reproducibility and Transparency:
Organising research data in a knowledge graph helps fellow researchers follow your methodologies, validate your results, and create on top of the work done before. This is consistent with the increased focus on open science and transparent research practices.
How Interdisciplinary Research Will Drive Change
Many of today’s greatest research problems (climate change, public health, artificial intelligence) can’t be solved within one discipline. Because knowledge graphs enable researchers to connect knowledge from different domains, they encourage cross-disciplinary work.
How Do Knowledge Graph Systems Work in Practice?
Knowledge graphs are already used in academia & industry. For example, Google’s search engine leverages a knowledge graph to enhance search results by contextualising connections between concepts, rather than simply matching keywords. In research, knowledge graphs superpower academic databases by enriching the search and enabling citation analysis tools.
Tools like Leximancer provide similar advantages for qualitative researchers, detecting and mapping relationships in textual data to generate concept maps that reveal thematic structures. This makes it easier to identify relationships between themes and concepts, using text data from a single dataset.
Knowledge graphs provide a new way to capture, analyse, and visualise complex relationships within data. From augmenting literature reviews, aggregating disparate datasets, to refining text analytics, they serve as an asset for academics using them to glean meaningful insights from their output.
As knowledge graphs have the potential to shape the future of academic inquiry, research is becoming more and more data-driven. Research will need to evolve into understanding and harnessing this technology to stay a few steps ahead.
