NP2022_Wikidata and Knowledge Graphs in Practice: Using semantic SEO to create discoverable, accessible, machine-readable definitions of the people, places, and services in global information community institutions and organizations
presentation
posted on 2022-03-31, 18:14 authored by Doralyn RossmannSlides from “Wikidata and knowledge graphs in practice: Using semantic SEO to create discoverable, accessible, machine-readable definitions of the people, places, and services in global information community institutions and organizations"
Description:
As a part of the global information community, libraries provide content and education that expands the access and visibility of data and research in support of an informed public. And yet, search engines and indexing software agents have limited knowledge of the dynamic nature of libraries - the people who make the library happen, the services provided, and the resources procured, thus the very definition of libraries is static, outdated, and misleading.
At its core, this is a metadata problem; our solution is to introduce a web-scale cataloging model that redefines libraries for machine learning environments and search engines. Research by the MSU Library and LSE Library have recognized and approached this metadata problem in two unique ways: implementing local structured data in a knowledge graph model and “inside-out” definitions in Semantic Web endpoints like Wikidata. MSU Library has found that implementing a “Knowledge Graph” linked data model within HTML markup leads to improved discovery and interpretation by the bots and search engines that index and describe what libraries are, what they do, and their scholarly content. In contrast, LSE Library has found that contributing to a collaborative and global metadata source, like Wikidata, is a means to extend reach and engagement with libraries and how they are understood.
In this session, we’ll demonstrate how Wikidata can be used as a tool to push out data beyond organizational silos, the technical details of knowledge graph markup and semantic Search Engine Optimization (SEO), work through questions about how metadata can represent an institution/organization equitably, and explain how this work improves the accessibility and reach of global information communities
At its core, this is a metadata problem; our solution is to introduce a web-scale cataloging model that redefines libraries for machine learning environments and search engines. Research by the MSU Library and LSE Library have recognized and approached this metadata problem in two unique ways: implementing local structured data in a knowledge graph model and “inside-out” definitions in Semantic Web endpoints like Wikidata. MSU Library has found that implementing a “Knowledge Graph” linked data model within HTML markup leads to improved discovery and interpretation by the bots and search engines that index and describe what libraries are, what they do, and their scholarly content. In contrast, LSE Library has found that contributing to a collaborative and global metadata source, like Wikidata, is a means to extend reach and engagement with libraries and how they are understood.
In this session, we’ll demonstrate how Wikidata can be used as a tool to push out data beyond organizational silos, the technical details of knowledge graph markup and semantic Search Engine Optimization (SEO), work through questions about how metadata can represent an institution/organization equitably, and explain how this work improves the accessibility and reach of global information communities
