  Despite widespread applications of knowledge graphs (KGs) in various tasks
such as question answering and intelligent conversational systems, existing KGs
face two major challenges: information granularity and deficiency in
timeliness. These hinder considerably the retrieval and analysis of in-context,
fine-grained, and up-to-date knowledge from KGs, particularly in highly
specialized themes (e.g., specialized scientific research) and rapidly evolving
contexts (e.g., breaking news or disaster tracking). To tackle such challenges,
we propose a theme-specific knowledge graph (i.e., ThemeKG), a KG constructed
from a theme-specific corpus, and design an unsupervised framework for ThemeKG
construction (named TKGCon). The framework takes raw theme-specific corpus and
generates a high-quality KG that includes salient entities and relations under
the theme. Specifically, we start with an entity ontology of the theme from
Wikipedia, based on which we then generate candidate relations by Large
Language Models (LLMs) to construct a relation ontology. To parse the documents
from the theme corpus, we first map the extracted entity pairs to the ontology
and retrieve the candidate relations. Finally, we incorporate the context and
ontology to consolidate the relations for entity pairs. We observe that
directly prompting GPT-4 for theme-specific KG leads to inaccurate entities
(such as "two main types" as one entity in the query result) and unclear (such
as "is", "has") or wrong relations (such as "have due to", "to start"). In
contrast, by constructing the theme-specific KG step by step, our model
outperforms GPT-4 and could consistently identify accurate entities and
relations. Experimental results also show that our framework excels in
evaluations compared with various KG construction baselines.
