5 Reasons Knowledge Graph will never bloom
Knowledge Graph (KG) is used to organize knowledge as a graph with nodes and edges (called triples). KG has been one of the most popular technologies in the AI community and has attracted increasing attention in recent years. However, KG is definitely not a new technology; this article will detail five reasons why KG will never be thriving (or at least will not be in the next two foreseeable decades) by tracing its past and analyzing the present.
Starting from the semantic web, proposed in around 1989 for more than two decades, the key technology has been descended the same (or at least similar), although the naming has been evolved from semantic technology, knowledge base, linked data to the current knowledge graph. Despite the massive effort for more than 20 years with these different versions, they all failed to thrive. With the latest version — KG, it has set up a smaller goal (i.e., as a way of knowledge organization) than its ancestor semantic web (i.e., leading to a whole web3.0 revolution). The question is would KG be able to thrive in the next few decades? To answer this question, we track the KG technology from past to president thoroughly in a comprehensive manner. We admit there is a remarkable development of KG with the knowledge representation learning in recent years. However, the existing crucial barriers for the last two decades are still there without being eliminated or even mitigated. This is alarming because hardly can we see any successful and easy-to-use KG applications surrounding us while some researchers embrace unrealistic expectations on it, i.e., to achieve cognitive intelligence or advanced intelligence.
We will first illustrate the problems that have failed the semantic web, which has been descended without being solved. Then, we depict five problems from the KG angle but not limited to: entity generalization challenge (including granularity and ambiguity), relation splurging, triple knowledge complexity, hard access, and knowledge acquisition latency. We also detail knowledge representation learning would not mitigate those problems either; on the contrary, it might exaggerate those. Therefore, KG would not be capable of leading to any breakthrough in the seeable future either.
At the end of the article, we also bring some profound suggestions to the table with our best R&D knowledge. We believe there need to be radical changes that could drive us away from whirlpools and navigate us to a real-world land that could genuinely benefit us.
The initial grand plan for the Web is to evolve the whole Web era from 2.0 to 3.0. We can summarize the web development with three eras chronologically: web1.0, web2.0 and the targeting web3.0.
Specifically, web 1.0 is a single direction service mode where few editors maintain the servers that serve the massive users, e.g., yahoo, Sohu, etc. Web 2.0 is a dual direction service mode with no boundary between users and editors, e.g., social networks, blogs, tweets, youtube, Instagram, etc. With web2.0, the editors of the servers become the users themselves, resulting in a vast amount of data. Web 3.0 involves the machine as triangle relations (machine, users, editors) called the machine-readable/understandable, or Semantic Web.
It has ever been a myth for a long time when the semantic web was first proposed two decades ago because it is often related to the terms of machine-readable, semantics, reasoning, or whatever fancy terms you might have seen somewhere. However, the semantic web is so simple that we can show that no more than one word can explain everything — ‘unifying’. Its only purpose is to unify the data form as much as possible, and RDF triple form is a unified solution for global knowledge representation. Let us see how those fancy terms can be interpreted as the word — ‘Unifying’:
If someone was talking about “standardized, formalized, semantics, machine-readable, reasoning, etc.”, you can follow up: “I gotcha, you are talking about unifying”.
