Bookmarking and Beyond: Building the Pointer to Quality Knowledge
Many of us have experienced the disheartening time after losing an online article or blog which we discovered earlier. We may not remember the path of encountering the article: clicked through layers of blogs, recommended by a friend, or discovered in the comment of a domain expert. We may roughly remember the topic of the article and try to search for it. However, the author of the article usually focuses on providing thoughtful ideas but using SEO (Search Engine Optimization) takes a back seat. Thus, it is possible for the valuable article to be unfortunately outranked by hundreds of web pages hosted on popular websites in search engine results. The video Dan North — Decisions, Decisions is such an example, which bakes in lots of interesting thoughts on choices in software engineering, but neither its keywords nor its description makes it right for search engines to highlight it.
Search engines are not designed to understand the content of links as humans do. Instead, they infer web page importance and quality by the number of links to it and the popularity of the link sources amongst other parameters. They help the most when the content creator knows how to align the description of a web page with the indexing and ranking mechanisms of a search engine. For exploratory searches where users performing the search have little knowledge of what they are looking for and where they are aiming to learn from the results, the indirect inference of the quality of the knowledge by link statistics may not work well. It is not rare that we need to skim through multiple pages of search results for exploratory searches such as “best software design practices” and find many highly ranked results not containing high-quality knowledge useful to us. We may wonder whether our peers, friends, or experts in a field can provide insights into the quality of the content and give us suggestions on learning. More generally, could members from the computing community help each other to identify and learn high-quality knowledge?
Academic articles published in conferences, journals, magazines, etc. don’t see this problem as our established publishing system with the peer-review process provides a good view of the quality of knowledge, and they are well indexed in online libraries such as ACM DL. However, learning should not be limited to such heavy knowledge, which brings novel and comprehensive information to humankind and takes a lot of effort to compose and publish. Unlike heavy knowledge, computing professionals publish and consume light knowledge in their daily life, such as blogs, wiki, and videos, which focuses on simplicity, explainability, timeliness, but not necessarily novelty and precision. For example, software engineers read blogs to learn new technologies; students go to project wikis, articles, and videos to learn new concepts and languages; professors follow news and whitepapers to find research problems.
While we consume light knowledge every day to understand technologies and try out new ideas in different computing disciplines, we do not have a good way to search for quality light knowledge.
We cannot solve this problem by formalizing the publishing procedure of light knowledge, which will make it heavier and kill its value.
To address this challenge, our team at ACM Future of Computing Academy (FCA) is introducing the ACM Pointer project — a community vetted, quality knowledge bookmarking, and discovery service for technical content. We have planned the project carefully to preserve the essence of light knowledge regarding its free composition and publishing process. The goal of reasoning about the quality of light knowledge is achieved by providing an additional bookmarking layer between light knowledge and end-users. This additional layer is responsible for collecting users’ opinions on published light knowledge and processing the understanding of light knowledge to foster knowledge rating and discovery for the community.
Besides the basic functionality of personal bookmark management in the cloud, ACM Pointer aggregates metadata provided by users such as keywords and likes, which provides insights into the quality of the light knowledge, thereby providing a high-quality knowledge discovery service to the community. A user can retrieve her saved bookmarks, search for highly voted bookmarks about a topic, or follow her friends or domain experts to check out the latest valuable content bookmarked by them. Users can comment and discuss on a bookmarked resource, and a user can message another user about an exciting piece of light knowledge in a bookmark. In the future development, we plan to expand the power of knowledge recommendation and discovery like editors’ choice and trend analysis, which can be accessed in the portal or through news feed subscription.
The ACM Pointer project aims to fill the gap of discovering quality light knowledge. To realize its power, our team at ACM FCA is working on a prototype system. We are actively reaching out to the computing community for feedback and will be happy to join hands with more volunteers to grow the project. Stay tuned for more updates and early-access previews. Please reach out to our team members listed below if you are interested in exploring more about the ACM Pointer project.
— The ACM Pointer Team
Xiaokui Shu (Email, Homepage)
Noopur Gupta (Twitter, LinkedIn)
Darshit Patel (Twitter, LinkedIn)
Cheng Li (Email, Homepage)
