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:  Ontology Building 
1. Abstract of Cited Paper (BIB001):  Wikis are established means for the collaborative authoring, versioning and publishing of textual articles. The Wikipedia project, for example, succeeded in creating the by far largest encyclopedia just on the basis of a wiki. Recently, several approaches have been proposed on how to extend wikis to allow the creation of structured and semantically enriched content. However, the means for creating semantically enriched structured content are already available and are, although unconsciously, even used by Wikipedia authors. In this article, we present a method for revealing this structured content by extracting information from template instances. We suggest ways to efficiently query the vast amount of extracted information (e.g. more than 8 million RDF statements for the English Wikipedia version alone), leading to astonishing query answering possibilities (such as for the title question). We analyze the quality of the extracted content, and propose strategies for quality improvements with just minor modifications of the wiki systems being currently used. 
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. 
3. Abstract of Cited Paper (BIB003):  As an important step towards bootstrapping the Semantic Web, many efforts have been made to extract triples from Wikipedia because of its wide coverage, good organization and rich knowledge. One kind of important triples is about Wikipedia articles and their non-isa properties, e.g. (Beijing, country, China). Previous work has tried to extract such triples from Wikipedia infoboxes, article text and categories. The infobox-based and text-based extraction methods depend on the infoboxes and suffer from a low article coverage. In contrast, the category-based extraction methods exploit the widespread categories. However, they rely on predefined properties, which is too effort-consuming and explores only very limited knowledge in the categories. This paper automatically extracts properties and triples from the less explored Wikipedia categories so as to achieve a wider article coverage with less manual effort. We manage to realize this goal by utilizing the syntax and semantics brought by super-sub category pairs in Wikipedia. Our prototype implementation outputs about 10M triples with a 12-level confidence ranging from 47.0% to 96.4%, which cover 78.2% of Wikipedia articles. Among them, 1.27M triples have confidence of 96.4%. Applications can on demand use the triples with suitable confidence.

Literature Review:
Research in the area of ontology building is vast and several studies have investigated many methods to extract concepts and relations for building ontologies. As one of the early attempts that used pattern matching for ontology building, Herbelot and Copestake  used Robust Minimal Recursion Semantics (RMRS) parser to extract Ontologies from biological articles in Wikipedia. RMRS is used to derive a semantic representation of the articles' text. It outputs the sentences into small semantic trees where the roots are the lemmas in each sentence. After parsing, each sentence is systematically processed for pattern matching to extract -isA‖ relations. The system is evaluated manually and automatically. The initial experiment that extracts the patterns manually resulted in 16% ‗rough' recall, 14% manual recall and 92% precision. Herbelot and Copestake have also discussed some factors that might yield the low recall achieved. Further experimentation by extracting the patterns automatically increased the recall to 37% while the precision decreases to 65%. Another attempt was brought by a project named DBpedia by Auer et. al.  BIB001 . DBpedia extracted unstructured knowledge from Wikipedia and converted it into more structured one represented as RDF triples. DBpedia contains about 103 million RDF triples that can be used in several Semantic Web applications. To build its dataset, DBpedia extracted information from Wikipedia's articles infoboxes and stored them as triples, where an article's title was considered as a subject and each attribute became a predicate of the triple while their values were considered as the objects. One of the main obstacles DBpedia has faced was the lack of availability of infoboxes in many Wikipedia articles. Auer et. al. used DBpedia mainly to build a querying system based on the stored triples to answer questions like: What have Innsbruck and Leipzig in common?. On the other hand, no evaluation results were provided to justify their method. In general, the main drawback of DBpedia is that it is poorly structured because there are no semantic links between its content. To overcome this problem, they produce the DBpedia ontology in 2008, which was manually created. Ponzetto and Strube BIB002 proposed a method to build a large taxonomy from Wikipedia using its categories hierarchy which is more like a conceptual network without the identification of the semantic relations between its nodes. They improved the semantic aspect of this hierarchy by classifying relation types to be either isA or not isA. They used syntax based methods to match categories' names and label's relations of matched categories as isA. Two types of syntax matching were used: head matching (isA) and modifier matching (not isA). The second method was based upon the connectivity characteristics of the network which had been used to define instanceOf relation between Wikipedia categories using their corresponding article as isA relation if their lemmas match. The final step was to label the rest of unidentified relations to either isA or not-isA relations using lexico syntactic based method that applied 13 predefined patterns to Wikipedia category titles. POS tagger and a SVMbased chunker were needed to identify noun phrases (NP). To evaluate the quality and coverage of the generated taxonomy, a comparison with ResearchCyc yielded recall of 89.1%, precision of 86.6% and F-measure of 87.9%. Suchanek et. al. in  aimed to extend the highly structured WordNet hierarchy by adding concepts and relations from Wikipedia through its categories. The project named YAGO (Yet Another Great Ontology) and it had successfully extracted 1.5 million entities and 5 million facts, some of the facts are represented as isA relation, SubClassOF (hyponymy) relation and the rest are predefined ones (bornInYear, diedInYear, establishedIn, locatedIn, writtenInYear, politicianOf, and hasWonPrize). Another type of relations is Means relation which had been extracted from Wikipedia redirect pages in which it connects synonyms together. YAGO was made with the capabilities of future extension so it had its own data model, which is an extension of the RDF (YAGO Data Model) that was used to represent concepts and relationships between them. They argued that the reason behind defining such model is that the existing Web Ontology Language (OWL and its variants before OWL 2) at that time are either undecidable or lacks in relations expression e.g. unable to represent transitivity. YAGO attached the leaf nodes from Wikipedia categories hierarchy (classes) to the ontology derived from WordNet where each synset is considered as a class in YAGO. All of the Wikipedia articles belong to the same category in Wikipedia (class) that had been mapped into WordNet are added to YAGO ontology as new entities. Many experiments had been conducted to evaluate the accuracy of the ontology generated by YAGO. They manually evaluated the facts produced by the system. The accuracy varied according to the type of relation being evaluated it ranged from 98.72 % for diedInYear relation to 90.84% for establishedIn relation. The average accuracy of the system was 95 %. In their later work Suchanek et. al.  extended their work in  by adding new heuristics to extract new entities and facts from Wikipedia and emphasizing on generating a high quality ontology using some quality control mechanisms. The heuristics used yielded a large number of relations which have been evaluated by 13 human judges to compute their precision. 74 heuristics achieved a precision of 95 % or higher and the average precision of YAGO was 95%. YAGO has been employed as a backbone in many other applications including querying, semantic search engines and even in ontologies constructions e.g. Freebase, UMBEL and SUMO. Pattern matching methods have been exploited in extracting semantic relations from Wikipedia. An example of such usage was introduced by Liu et. al. BIB003 . In this paper, the main goal was to extract triples from Wikipedia assuring a wide coverage across it with a minimum effort. Wikipedia categories hierarchy played a major role in the proposed method, so for each category the method's job was to extract the property and value that are common among all the articles belonging to a given category. Two types of category pairs were investigated. The first one has explicit property and value e.g. -Songs by artist‖ and -The Beatles songs‖, where -artist‖ is the property and -The Beatles‖ is the value. The second type has explicit value and implicit property, e.g. -Rock songs‖ and -British rock songs‖, where -British‖ is the value and the property is not provided. Four patterns were used to extract property-contained category names (PCCN) and value-contained category names (VCCN) from category names. OpenNLP were employed to extract part-ofspeech tags for the category names. At the final step, triples are generated for each article where the subject is the article's title. To evaluate the proposed method, 500 Catriple (Category Triples) triples were selected randomly to be judged by human judges, this yielded a precision ranged from 47.0% to 96.4%. Cyc project has been also utilized in ontology building. As an example of such usage Medelyan and Legg BIB004 benefited from ResearchCyc by integrating it with Wikipedia in order to generate a folksonomy that combines both Wikipedia and ResearchCyc Their approach mapped Cyc concepts into Wikipedia articles' titles. Two types of mapping were investigated: 1) Exact mapping in which Cyc concepts are mapped directly to Wikipedia article titles or to redirect links within an article as one to one. In case of no matched article found they checked whether its Cyc synonym is available. 2) Ambiguous mapping, this arise when one Cyc concepts can be mapped to many Wikipedia articles. Each Cyc concept would have a list of candidates collected from disambiguation pages. At the end 52,690 Cyc concepts were mapped to Wikipedia articles. To evaluate the method, two datasets were used. The first one was a list of 9,333 manually mapped Cyc synonymous; the precision of the comparison was 93.9%. The second one consists of 100 random pairs of Cyc concepts that were mapped to Wikipedia articles to be judged by six human judges which resulted in a precision of 93%. The methodology used by Medelyan and Legg BIB004 has been further improved by the work of Sarjant et. al.  . Mappings' methods were improved by adding more conditions, for example in exact mapping they removed -The‖ from titles. In the evaluation, they evaluated both mapping process and extension process by 22 human judges who evaluated both old (mapping used by BIB004 ) and the current mapping. The newly computed precision for the old mapping was 83% while the new one achieved 91% precision. De Silva and Jayaratne  introduced WikiOnto which extract and model ontology from Wikipedia XML corpus using Natural Language Processing (NLP) and machine learning techniques. WikiOnto composed of three phases, in the first phase concepts and candidate relationships (sub-conceptOf) with each other were extracted using the provided XML document structure. In the next step, a vector-space is constructed for each document which contains the keywords (most relevant concepts) by measuring TFIDF (Term Frequency Inverse Document Frequency) of each concept. After that documents were clustered according to the similarities (cosine similarity) of their corresponding vector-spaces using k-means algorithm. After clustering, ontologies were modeled where each concept is linked to the other concepts that belong to its cluster. To improve the performance of their system they added a syntactic processing method that can extract hyponymic relations between concepts. It is noted that no evaluation results were provided but they mentioned that their project is an ongoing one. Wikipedia is a multilingual online encyclopedia that is available in many languages. As an example of using other versions of Wikipedia, Farhoodi et. al.  used the Persian Wikipedia to build an ontology that facilitated their proposed query expansion method. To generate ontology, a process of three phases was employed. Firstly, a Wikipedia parser extracted the title, keywords, existing links, ‗see also' links and list of categories. Then the relationships between the extracted data are defined as -IsRelatedTo‖ which occur between article's title and keywords article's title and related links, and finally between an article's title and hyperlinks. No specific evaluation results about ontology building were provided.