OpenIE‑Based Knowledge Graph Construction for Vertical Domains

The presentation begins with an overview of the current state of enterprise knowledge management, highlighting common pain points such as massive paper archives, tightly coupled ERP systems, and data silos that hinder efficient knowledge retrieval and reasoning.

It then introduces knowledge graphs as an effective model for representing and reasoning over structured knowledge, especially in vertical domains where they can support search engines, intelligent Q&A, knowledge mining, and decision‑making.

The next section outlines various knowledge extraction techniques. Traditional rule‑based and template methods are combined with model‑based approaches. For semi‑structured data, wrapper‑style extractors are used, while relational data can be transformed via D2R methods. The discussion distinguishes between OpenIE (open‑domain) and CloseIE (closed‑domain) extraction, noting that OpenIE often suffers from low precision in industrial settings.

Following this, the article details the SPO (Subject‑Predicate‑Object) extraction pipeline. It defines SPO components, reviews Baidu’s competition solution that treats extraction as a span‑labeling task, and describes how to handle both predefined predicates (closed‑domain) and predicates that appear in the text (open‑domain) using joint models that may incorporate reading‑comprehension and entity‑recognition techniques.

Finally, several real‑world applications of the constructed knowledge graph are presented: chatbot integration for multi‑turn conversational AI, knowledge‑driven search that goes beyond keyword matching, and intelligent Q&A systems that combine NLU, graph reasoning, and BI analytics to deliver richer, domain‑agnostic answers.