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:  Relation Extraction 
1. Abstract of Cited Paper (BIB001):  Proceedings of the First Workshop on Semantic Wikis - From Wiki to Semantics co-located with the ESWC2006 Budva, Montenegro, June 12, 2006. 
2. Abstract of Cited Paper (BIB002):  This paper describes an automatic approach to identify lexical patterns that represent semantic relationships between concepts in an on-line encyclopedia. Next, these patterns can be applied to extend existing ontologies or semantic networks with new relations. The experiments have been performed with the Simple English Wikipedia and WordNet 1.7. A new algorithm has been devised for automatically generalising the lexical patterns found in the encyclopedia entries. We have found general patterns for the hyperonymy, hyponymy, holonymy and meronymy relations and, using them, we have extracted more than 2600 new relationships that did not appear in WordNet originally. The precision of these relationships depends on the degree of generality chosen for the patterns and the type of relation, being around 60-70% for the best combinations proposed. 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  Berners-Lee's compelling vision of a Semantic Web is hindered by a chicken-and-egg problem, which can be best solved by a bootstrapping method - creating enough structured data to motivate the development of applications. This paper argues that autonomously "Semantifying Wikipedia" is the best way to solve the problem. We choose Wikipedia as an initial data source, because it is comprehensive, not too large, high-quality, and contains enough manually-derived structure to bootstrap an autonomous, self-supervised process. We identify several types of structures which can be automatically enhanced in Wikipedia (e.g., link structure, taxonomic data, infoboxes, etc.), and we describea prototype implementation of a self-supervised, machine learning system which realizes our vision. Preliminary experiments demonstrate the high precision of our system's extracted data - in one case equaling that of humans. 
5. Abstract of Cited Paper (BIB005):  The exponential growth of Wikipedia recently attracts the attention of a large number of researchers and practitioners. One of the current challenge on Wikipedia is to make the encyclopedia processable for machines. In this paper, we deal with the problem of extracting relations between entities from Wikipedia's En- glish articles, which can straightforwardly be transformed into Semantic Web meta data. We propose a method to exploit syntactic and semantic information for relation extraction. In addition, our method can utilize the nature of Wikipedia to automatically obtain training data. The preliminary results of our experiments strongly support our hyperthesis that using information in higher level of description is better for relation extraction on Wikipedia and show that our method is promising for text understanding. 
6. Abstract of Cited Paper (BIB006):  Not only is Wikipedia a comprehensive source of quality information, it has several kinds of internal structure (e.g., relational summaries known as infoboxes), which enable self-supervised information extraction. While previous efforts at extraction from Wikipedia achieve high precision and recall on well-populated classes of articles, they fail in a larger number of cases, largely because incomplete articles and infrequent use of infoboxes lead to insufficient training data. This paper presents three novel techniques for increasing recall from Wikipedia's long tail of sparse classes: (1) shrinkage over an automatically-learned subsumption taxonomy, (2) a retraining technique for improving the training data, and (3) supplementing results by extracting from the broader Web. Our experiments compare design variations and show that, used in concert, these techniques increase recall by a factor of 1.76 to 8.71 while maintaining or increasing precision.

Literature Review:
As one of the first attempts to extract relations from Wikipedia Ruiz et. al. BIB001 BIB003 propose an automatic approach to extend the semantic network of WordNet using relations extracted from Wikipedia. So, for two semantically related WordNet synsets, this method defines lexical patterns from their corresponding Wikipedia articles, in order to extract the missing relations between them in WordNet. Their method successfully extracted 1224 new relations from Wikipedia with a precision ranged from 61%-69% for three different types of relations (Hyponymy, Holonymy and Meronymy). Ruiz. et. al BIB002 have also extend their previous work in BIB001 , by proposing an automated method to extract other types of relations besides Hyponymy (isA), Hyperonymy (not-isA) , Holonymy (has part) and Meronymy. In their approach they used several NLP tools such as (Tokenizer, POS Tagger, Stemmer, Named Entity Recognition, Chunker) to extract the lexical patterns from Wikipedia. The approach achieved a human judgement precision that varied from 90% (for death-year relation) to 7.75% (for player-team relation) this is related to the types of pattern used to extract such relations. In their discussion, they mentioned possible future use of the method to build Ontologies. Hyperlinks over categories were another major technique used to extract relationships from Wikipedia. For instance, Chernov et. al.  discussed the degree in which semantic relations extracted from Wikipedia are correlated. To do so they propose two measures to estimate the strength of the semantic relation between two Wikipedia categories. The first measure is the number of hyperlinks in the pages contained within the categories. The other measure is called Connectivity Ratio which represents a normalization of the number of hyperlinks according to the category size. The authors' main argument is that if there is a strong semantic relation between any two categories then the number of hyperlinks between the pages under them will be large, otherwise, there is no regular semantic relation. In order to evaluate their method, Chenove et. al. introduced Semantic Connection Strength measure (SCS) with the values 0, 1 or 2, where 2 means strong semantic relation and 0 means weak semantic relation. In general, Connectivity Ratio results were better by 25%. Another widely used technique for relation extraction is infoboxes. Wang et. al. [6] proposed the Positive-Only Relation Extraction (PORE) framework to extract relations from Wikipedia. Instead of using pattern matching, their approach was completely independent of patterns and it was based on B-POL algorithm which is an extension of Positive-Only Learning algorithm, where negative examples are identified and then classified until they reach convergence. In the conducted experiments, they evaluated four types of relations (album-artist, film-director, university-city, band-member). These four relations were extracted from album_infobox, movie_infobox, university_infobox and band_infobox respectively. The evaluation was done by 3 human judges. Album-artist relation achieved the highest F-measure with 79.9% while band-member relation achieved the lowest F-measure that is equal to 47.1%. Similarly, Wu and Weld BIB004 proposed the KYLIN system, which automatically extracts information from Wikipedia articles that belongs to the same category in order to create and complete their infoboxes. KYLIN generates infoboxes using Infoboxes Generation Module which consists of three phases: 1) preprocessing, 2) classifying and 3) extracting. Moreover, it can be further used in automatic link generation. Four classes were evaluated during the experiments conducted to test KYLIN's performance: U.S. county, airline, actor, and university. For each one of these classes the authors manually compared KYLIN generated infobox with the one already exist within Wikipedia's articles. Recall results ranged from 60.5% for university, to 95.9% for county. The precision ranged from 87.2 % for airline to 97.3% for county. The authors argued that county achieved the highest recall and precision because it contains a lot of numerical data that can be extracted easily. Based on KYLIN BIB004 , Wu and Weld BIB005 proposed KOG (KYLIN Ontology Generator), which generally used to build Ontology that combines Wikipedia infoboxes and WordNet hierarchy. In KOG each infobox template was considered as a class, while the slots belong to the infobox were considered as attributes. KOG has three main components: 1) schema cleaner, 2) subsumption detector, and 3) schema mapper. Subsumption detecting was mainly used to identify isA relations between classes (infobox templates). They used machine learning techniques (SVM and Markov Logic Networks MLN) with a number of classification features to classify the relations between the classes while mapping them to WordNet nodes. Many experiments were conducted to evaluate the system as a whole and to evaluate the performance of each component based on 1269 selected infobox classes. In subsumption detection phase, SVM achieved a 97.2% precision and 88.6% recall. In MLN (basic) the precision decreased to 96.8% while the recall increased to 92.1%. Finally, MLN+ (fully-functional) had increased both, precision to 98.8% and recall to 92.5%. In their later work, Wu et. al.  , focused on the improvements in the recall of their system. KYLIN had successfully extracted attributes for many types of classes (infoboxes), unfortunately not all classes had infoboxes contained articles. KYLIN's extraction performance is poor for such classes due to the lack of the possible attributes to extract and the lack of sufficient training examples. Natural language techniques (NLP) were also utilized to extract relations from Wikipedia via syntactic parsing and tree mining. In this regard, Nguyen et. al. BIB007 [11] proposed a framework to extract relations from Wikipedia using a syntactic parser and several NLP techniques. The process starts by extracting the hyperlinks from each Wikipedia article, then, the whole text is processed by OpenNLP tool that contains a Sentence Splitter, a Tokenizer, and a Phrase Chunker to extract all of the different occurrences of an entity in an article. After that, from each detected sentence's keywords which indicate the type of relation that exist between principals are extracted using Keyword Extractor. Relation Extractor is used to construct a syntactic tree representation of each sentence; those trees are then generalized using machine learning techniques to be applied to other similar sentences. In their experiment they randomly selected 3300 entities (principal and secondary) and manually annotate their relations. 3100 entities are used for training and 200 for testing. The system achieved an average F-measure equals to 38%. Where recall has ranged from 50% to 55% and precision ranged from 20% to 30%. Cui et. al. BIB006 focused on extracting concepts from Wikipedia as a requirement in building ontologies. They used infoboxes information, category labels and the first sentence of each article. Definition sentences usually indicate hypernym relation (instanceOf). Verbs-to-be in a definition (e.g. is, are, was) are usually a good indication that the following noun is connected to the title noun with isA relation. A Part-Of-Speech (POS) tagger was used to syntactically parse the sentence. N-gram statistics were used to divide category names into smaller parts. They evaluated the correctness of 50,000 extracted concepts. They compared the three methods individually. Infobox method achieved 90.1% precision with 15% coverage. Definition sentences method achieved 76.7 % precision and 79.2% coverage. Finally, categories method achieved 75.3 % with 100% coverage.