You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Semantic Relationship Extraction and Ontology Building using Wikipedia: A Comprehensive Survey 
Chapter Title:  DISCUSSION 
1. Abstract of Cited Paper (BIB001):  Extracting semantic relations is of great importance for the creation of the Semantic Web content. It is of great benefit to semi-automatically extract relations from the free text of Wikipedia using the structured content readily available in it. Pattern matching methods that employ information redundancy cannot work well since there is not much redundancy information in Wikipedia, compared to the Web. Multi-class classification methods are not reasonable since no classification of relation types is available in Wikipedia. In this paper, we propose PORE (Positive-Only Relation Extraction), for relation extraction from Wikipedia text. The core algorithm B-POL extends a state-of-the-art positive-only learning algorithm using bootstrapping, strong negative identification, and transductive inference to work with fewer positive training examples. We conducted experiments on several relations with different amount of training data. The experimental results show that B-POL can work effectively given only a small amount of positive training examples and it significantly out-performs the original positive learning approaches and a multi-class SVM. Furthermore, although PORE is applied in the context of Wikipedia, the core algorithm B-POL is a general approach for Ontology Population and can be adapted to other domains. 
2. Abstract of Cited Paper (BIB002):  This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO's precision at 95%-as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO's data.

Literature Review:
The problem of semantic extraction has been addressed using different techniques and throughout the previous sections we presented different studies that employed them. Table 1 shows that most studies have combined one or more methods to accomplish the required task. Also, by combining methods the overall coverage of semantics across Wikipedia articles will be increased accordingly. Although, Wikipedia categories hierarchy contains duplication and sometimes it is inconsistent compared to other manually created hierarchies e.g. Cyc and WordNet, yet, it has been used in many approaches  Compared to infoboxes, category hierarchy covers a wider area of concepts available within Wikipedia. In fact, the methods that are dependent on infoboxes only e.g. DBpedia BIB001 [15] perform poorly for many reasons, 1) there might be several templates for the same type of articles. 2) Sometimes some infoboexes attributes have no values. 3) Redundancy and inconsistency. Therefore, it is highly preferable to use infoboxes them combined with one or more methods. To the contrary, KYLIN BIB002 [8] used the information available on Wikipedia to complete and create infoboxes that belongs to the same class and sometimes it benefitted from the attributes in the infoboxes of some articles to add more attributes to other articles' infoboxes within the same class. As mentioned in BIB003 44.2% of Wikipedia articles have infoboxes while categories covered nearly 81% of Wikipedia information. Hyperlinks were not used widely in the previous methods, and obviously it is due to the ambiguity that they might lead to. Because, it cannot be guaranteed that the links appeared within Wikipedia's articles may indicate semantic relations between concepts. One reasonable use of hyperlinks was proposed by Chernov et al.  . They measured the strength of the semantic relations between categories by counting the number of pages that have hyperlinks to pages in other category. Parsing and pattern matching methods were used effectively in many approaches. Usually the results achieved were promising. The reason behind this promising result is that when applying patterns to the free text the number of extracted semantic relations would be very large, but for sure the selection of patterns should be done with caution because they should be general enough so that they can yield better performance. Another important issue when dealing with patterns is that the used language has a great impact on the ability of defining patterns and generalizing them. Also, some approaches used machine learning techniques mainly to train their systems to decide upon relationship types. To conclude, two characteristics can affect the usability of any relation extraction/ontology building approach: 1) The number of the correctly extracted concepts and relations it produces (the coverage). 2) The quality of the extracted semantic relationships between its extracted concepts (Quality of structure).