In this paper we present a survey of web information extraction systems and semantic annotation platforms. The survey is concentrated on the problem of employment of these tools in the process of web semantization. We compare the approaches with our own solutions and propose some future directions in the development of the web semantization idea.

Several problems exist with traditional HMM based approaches for Web information extraction (IE) due to the lack of consideration on Web-specific features. To address this issue we present a Generalized Hidden Markov Model (GHMM) that extends HMMs by making use of Web-specific information for Web IE. In GHMM based approach, Web content blocks instead of terms are used as basic extraction unit. ...

Web texts typically undergo the open-ended growth of new relations. Traditional relation extraction methods lack automatic annotation and perform poorly on tasks. We propose an open-domain system (ORES) based distant supervision few-shot learning to solve this problem. More specifically, we utilize tBERT design instance selector 1, implementing labeling in data mining component. Meanwhile, exam...

The approach towards Semantic Web Information Extraction (IE) presented here is implemented in KIM – a platform for semantic indexing, annotation, and retrieval. It combines IE based on the mature text engineering platform (GATE1) with Semantic Web-compliant knowledge representation and management. The cornerstone is automatic generation of named-entity (NE) annotations with class and instance ...