They’re all about what data means in terms of its intent and context with the help of annotations so that better integration and reuse is possible. Knowledge graphs are interpersonal by definition. In that sense, an “interpersonal knowledge graph” is from the Department of Redundancy Department. It’s an evolution of Web 1.0 designed to encourage sharing, discoverability and reuse out of the gate. Then sharing is a next step.īut a semantic standards-based knowledge graph architecture assumes web scalable collaboration by design. A first step obviously targets individual use. That distinction is essential when referring to or designing tools. It’s sometimes helpful to make a distinction between “personal” (designed originally just for individual use) and interpersonal (designed for sharing). Truly semantic graphs have this ease of sharability because they are intended for and designed to use standard means of abstraction, synthesis, disambiguation and identification. In other words, they can’t be easily shared. Keep in mind that many people use the term “knowledge graph” in reference to graphs that can’t deliver large-scale integration, comparison or interoperation. But with enough machine readable context to empower higher volumes of the instance data, generalizable intelligence becomes possible. If you don’t have clear context, you can’t really be sure of anything. Just to be clear on the key things a knowledge graph does, here’s a definition in contextual intelligence terms:Ī knowledge graph facilitates a contextual web through large-scale integration and interoperation, including: Making KG-friendly annotation a regular habit when notetaking and saving relevant links is another goal of the project.
One of the goals of the project is to publish the content as a knowledge graph. George Anadiotis and Ivo Velichkov are the editors of a forthcoming book described at. Others simply want to make it easier for everyday users to create and publish RDF (standard subject-verb-object triplets), ontologies and make more use of linked data. Gyuri Lagos, for example, recommends for taking existing text and adding annotation to it. The members of our working group have different preferences when it comes to their own personal notetaking and annotation. Family history research is an example of the type of research that could benefit from image-friendly knowledge graphs.
Margaret Warren’s ImageSnippets is designed for that purpose.
For them, the need is to annotate the links and associated text (in a simple and ideally machine assisted way from research sources so that machines can help retrieve the right links later on and discover (or rediscover) related links. For others, making images more easily connectable, discoverable and retrievable is key. Lately, it’s functioned as a discussion and demo evaluation group for new technologies and how they might be used in a knowledge graph context.ĭifferent individuals want to annotate different kinds of data. I help lead a working group focused on personal knowledge graphs (PKGs).
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