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History assessment about western civilization in Europe and a little bit in the US

History assessment about western civilization in Europe and a little bit in the US. I’m working on a History exercise and need support.

i will be posting a few assessment materials and each picture will show ID terms and that you should a talk about (who, what, where, when and why (why as in significance) you can chose any 8 of all those ID terms. Also in each of those pictures there are questions you can choose 2 of any of those questions and answer them in 2-3 paragraphs.
Lastly can you write a comprehensive essay on this : Discuss nationalism, the events that lead to it, and the role it plays in the events of early modern/modern Europe. Be sure to include at least 8 examples of how nationalism influences events, politics, and society of Europe. Use specific examples from throughout the time covered by this course (1600 to present) in crafting your essay..
so the format of the assessment will be
( mix from the ID terms i will send)
ID term 1 :(who, what, where, when and why (why as in significance)
ID term 2: (who, what, where, when and why (why as in significance)
ID term 3 (who, what, where, when and why (why as in significance)
ID term 4 (who, what, where, when and why (why as in significance)
ID term 5 (who, what, where, when and why (why as in significance)
ID term 6 (who, what, where, when and why (why as in significance)
ID term 7(who, what, where, when and why (why as in significance)
ID term 8(who, what, where, when and why (why as in significance)
Short answer questions
1) *question* 2/3 paragraphs
2) *question* 2/3 paragraphs
comprehensive essay
Discuss nationalism, the events that lead to it, and the role it plays in the events of early modern/modern Europe. Be sure to include at least 8 examples of how nationalism influences events, politics, and society of Europe. Use specific examples from throughout the time covered by this course (1600 to present) in crafting your essay..
History assessment about western civilization in Europe and a little bit in the US

Fair Value Practice: Suitability in Accounting. Introduction The issue of the use of fair value as a model for financial standards and reporting has been subjected to significant debate and argument since the IASB[1] Framework was first introduced in 1989. As can be seen from a number of accounting industry responses, such as that of Peter Willams (2005), the use of fair value is becoming increasing contentious and could pose difficulties for the ISAB. Some fear that if this issue is not addressed to the satisfaction of all parties, it could affect the power and influence of the ISAB. The intention within this paper is to discuss the theoretical concept of “fair value” and to assess its suitability of use for accounting reporting purposes. The paper will also look at the practical application of the “fair value” measurement as determined by the IASB within their current international reporting and accounting standards. The Concept of Fair Value The concept of “fair value” is to enable recognition of the reliable economic future value of certain assets and expenses, the latter of which is intended to ensure the correct level of increase or decrease of balance sheet assets or liabilities. The result of this method is to create a defined link between income and expense to reflect the movement in the value of assets and liabilities. For those who promote the concept of fair value, or what is sometimes known as fair “market” value, it is the sale price achieved for an asset offered on the market at the time of the statement, based upon the reasonable opinion of a professional evaluator (A.M. King 2006, 45). Fair value at present has no specific and identifiable measurement definition within current international accounting standards. It is currently determined through an amalgamation of a number of different and diverse accounting measurements used by corporations in accounting and financial reporting, although these models all have their disadvantages. For example, in the case of the historical cost measurement basis, fair value is deemed to be at the measured at the date of purchase, as this reflects market value at that time. Although this model is seen as one of the least volatile methods of value measurements, it is perceived to have shortcomings. The main issues are that cost dates are earlier than sale date leading to a potential for profit overstatement, and that it is not the ideal model on which to based future business decisions. In fact some commentators see that the current moves on fair value, although they may signify a move away from the less volatile performance of the previously used historical cost method, produce a measurement that is more in line with the real volatility of life and business activity generally (Mary Barth 2006, p.324). An alternative measurement, which uses a price index system such as the RPI[2], and is still based on transactions, is current purchasing power. The fair value determination here is set to reflect the capital of the business in relation to the general price trends. The difficulty with this model is that it assumes all prices move in line with the index, which is clearly not the case and thus can create an artificial monetary unit. The replacement cost and net realisable value model (NBV) use a fair value system based upon market entry and exit costs respectively. The former has the advantage of being able to calculate current values on a realistic basis, and can therefore identify gains in operating and other business areas, thus preserving the capability of the business. However, its subjectivity is aggravated by the speed of technological development and the fact that this leads to the possibility of no similar asset being available to compare values. The NBV model is clearer as it is based upon the probable selling price of the asset. It also does away with the estimation of depreciation as that selling price already reflects this. However, NBV does not take into account that the majority of assets are not disposed of, but utilised within the business. The problem with this calculation of fair value can threaten the concept of the business being a going concern. The ISAB intend to move towards a definitive fair value model, which supporters see as a positive action, the cost of which will not “be significantly higher than the cost of trying to implement the mixed measurement system” (Langendijk 2003, p.292). Mary Barth (2006), a member of the IASB, agrees with this statement, adding that a more definitive “fair value” model will assist in the elimination of some of the perceived volatility presently in existence. However, the opponents are equally vocal in their objections. A.M. King (2006, p.45) poses the question whether “all assets on a balance sheet [should] be shown at Fair Value?,” continuing to comment that the ability to achieve a particular model does not necessarily mean that it should be implemented. De Vries (quoted in Langendijk 2003, p.174) also questions whether it is a move in the right direction for financial reporting, and others fear that it will lead to less, rather than more reliance upon financial statements by investors and other stakeholders (Peter Williams, 2005). In the author’s opinion it appears that, whilst professional preparers of financial statements understand the concept of the “fair value” model being sought, those who utilize the statements as a basis for making investment and other business decisions, including stakeholders of all sizes, find difficulty equating the results with other factual information. In addition, the term fair value will only be valid at the date of preparation of the statement and, as a result, itself becomes historic from that moment. Thus, there is an argument for maintaining its use with the commonly used historical cost model. Use of Fair Value in accounting and reporting standards The term “fair value” is liberally spread throughout the international accounting and reporting standards. It is referred to in four of the IFRS[3]’s and at least fourteen of the international accounting standards, as shown in the summaries of the IAS (2006). The context of fair value within IFRS relates to treatment of the initial adoption of the standards, business combinations, insurance contracts and non-current assets and discontinued operations. In terms of the initial adoption, IFRS grants exemption of some non-current assets from the fair value model. The intention of the inclusion of fair value here is to ensure that the movement in the market value of an asset or liability, in other words the increase or decrease in value, is reflected within the financial statements at the prevailing date of those statements, identifying if this is different from actual cost. With the movements being recognised within the profit and loss, the anticipated result is to enable, a more accurate reflection of the capital (or share) value of the business at the given date (Antill and Kenneth 2005). In addition, IFRS demand that these fair value measurements be performed at each subsequent financial and accounting statement date, thus endeavouring to provide for the organisation’s Balance Sheet to reflect the impact of market conditions at all times. The inclusion of fair value within the international accounting standards is concentrated mainly within the areas of assets and liabilities, and in relation to specific business sectors, such as banks and similar financial organisations (IAS 30), Investment property (IAS 40) and agriculture (IAS 41). Two of the IAS’s do relate specifically to non balance sheet items. IAS 18 deals with fair value within the context of revenue. In this respect, it deals refers to the treatment of deferred income, where the fair value is achieved by the discounting of future receipts. The intention here is to take into account the change in revenue value by deferring the time of receipt, for example, how a rise in RPI[4] might influence the income in real terms. In IAS 21, which deals with foreign exchange transactions, the presenter of the financial statement is required to determine a fair value in the foreign currency in question before converting at the exchange rate applicable at the determination date. When dealing with the treatment of assets, impairment of assets and liabilities, as in IAS 16, 17 and 19, the fair value model intends the financial statements to include a valuation that accurately reflects the realisable worth in the marketplace of that asset or liability at the date of the valuation, notwithstanding whether the intention is to retain or dispose of that asset. In this respect fair value differs from historical cost accounting, which records the value of such items as at the date of purchase and, in many cases applies a depreciation content to the items, irrespective of their worth to a prospective purchaser. The historical cost result is twofold. Firstly, the financial statement recognition of any gain or loss against the real market value of an item may be delayed by several years and secondly, the statements will therefore not portray an accurate and fair view of the real value of the business at the date of the statements. The fair value model aim is to accurately align the varying fortunes of the business and its capital worth with the market forces of the date, allocating gains and losses within the period of time that they actually occur, rather than, as is the case with the historical cost model, creating an unrealistic movement in value within the space of one accounting period. A simple example of this in action is where, in the historical system, depreciation is attached to an asset at a predetermined annual rate, annually reducing the asset value. In reality, the sale of that asset would often achieve greater value than the statements showed, leading to a sudden annual increase in profits and growth in capital. Fair value proponents’ state that, by reassessing the market value on an annual basis, the real annual growth achieved by a business entity is more accurately defined, and that this provides investors with statements from which they can make more realistic judgments and use of as comparisons against other organisations, which is of benefit in their investment decision making process. Conclusion The core intention in the adoption of a fair value model as the most appropriate method of measurement for financial and accounting statement is to create a balance sheet and capital value of an organisation that accurately reflects the real market position of that organisation at the date of the statement. One difficulty and concern with this is the inherent problem in the evaluation and establishment of the fair value in respect of all of the items included within the statements. Langendijk et. al. 2003, p.52). At the time of this paper, the IASB has entered into further discussions with the various parties involved with, and affected by the fair value model. This is an attempt to arrive at a clearer definition of the model itself, and to seek a position on fair value, which is more acceptable for the future. References Antill, Nick and Lee, Kenneth (2005). Company Valuation Under IFRS: Interpreting and Forecasting Accounts Using International Financial Reporting Standards. Harriman House Publishing. UK Barth, Mary (2006). Fair Values and Financial Statement Volatility. International Accounting Standards Board, UK. ISAB Framework (2001). Framework for the preparation and presentation of Financial Statements. International King, A.M (2006). Fair Value for Financial Reporting: Meeting the New FASB Requirements. John WileyFair Value Practice: Suitability in Accounting
Walden Internship Programs Business Students Professional & Personal DEV Discussion.

This discussion must be based on Reading: Sweitzer & King – CHAPTER 2In order to answer those questions, you have to read chapt 2 attachment I send here. The attachment on the part 1 introduction I send you only if you want some information because I do not want in part two you out of the subject.*Please cite past courses in this text for reflections as part of your discussion. A minimum of five citations/sources should be from other books/text citations from previous courses. TOPIC: The Anticipation Stage – Venturing ForthReading: Sweitzer & King – CHAPTER 2Chapter 2 Questions:Create your own list of anxieties about the internship experience. What are you finding most effective in managing them? Which ones do you think will diminish over time – without you intentionally doing anything about them? Why do you think that will happen?What hopes do you have for this internship, and what has happened so far that informs you of the likelihood of those being realized? What are your next steps now that you have a better sense of your hopes being realized? Think about the changes- personally and professionally- that you are experiencing even this early on in the internship. What did you do to bring about these changes? What did you not do that in turn allowed the changes to occur?(IN 1000+ WORDS IN APA DOUBLE SPACED WORD)
Walden Internship Programs Business Students Professional & Personal DEV Discussion

HSM 310 ATC Wk 6 Death by Measles Major Problems and Secondary Issues Case Study

HSM 310 ATC Wk 6 Death by Measles Major Problems and Secondary Issues Case Study.

I’m working on a health & medical question and need an explanation to help me learn.

For this assignment, select any one of the case studies presented in Chapter 18 & page 507 of your textbook. Conduct a thorough case study analysis of the case, following the guidelines presented at the beginning of Chapter 18 (for our online course, you will be completing this analysis individually, not on a team). And then prepare a case study write-up as described in your textbook, including a background statement, major problems and secondary issues, your role, organizational strengths and weaknesses, alternatives and recommended solutions, and evaluation. Hi there, I have attached the reference material read the intro part choose one case the follow the format provided to do the write up. The paper should have 0% similarity and follow APA guidelines.Thank you.
HSM 310 ATC Wk 6 Death by Measles Major Problems and Secondary Issues Case Study

Summarize the COSO Risk Management Framework and COSO’s ERM process.

essay writing help Summarize the COSO Risk Management Framework and COSO’s ERM process.. I don’t understand this Computer Science question and need help to study.

The following material may be useful for the completion of this assignment. You may refer to the documents titled “Embracing Enterprise Risk Management: Practical Approaches for Getting Started” and “Developing Key Risk Indicators to Strengthen Enterprise Risk Management”, located at Imagine you are an Information Technology Manager employed by a business that needs you to develop a plan for an effective Enterprise Risk Management (ERM) program. In the past, ERM has not been a priority for the organization. Failed corporate security audits, data breaches, and recent news stories have convinced the Board of Directors that they must address these weaknesses. As a result, the CEO has tasked you to create a brief overview of ERM and provide recommendations for establishing an effective ERM program that will be used as a basis to address this area moving forward. Write a three to four (3-4) page paper in which you:

Summarize the COSO Risk Management Framework and COSO’s ERM process.
Recommend to management the approach that they need to take to implement an effective ERM program. Include the issues and organizational impact they might encounter if they do not implement an effective ERM program.
Analyze the methods for establishing key risk indicators (KRIs).
Suggest the approach that the organization needs to take in order to link the KRIs with the organization’s strategic initiatives.
Use at least three (3) quality resources in this assignment (in addition to and that support the documents from the COSO Website referenced in this assignment). Note: Wikipedia and similar Websites do not qualify as quality resources.

Your assignment must follow these formatting requirements:

Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.

The specific course learning outcomes associated with this assignment are:

Describe the COSO enterprise risk management framework.
Describe the process of performing effective information technology audits and general controls.
Use technology and information resources to research issues in information technology audit and control.
Write clearly and concisely about topics related to information technology audit and control using proper writing mechanics and technical style conventions.

Summarize the COSO Risk Management Framework and COSO’s ERM process.

Monroe College Week 11 Why It Is Important Not to Plagiarize the Work of Others Discussion

Monroe College Week 11 Why It Is Important Not to Plagiarize the Work of Others Discussion.

In academic writing, plagiarism (using other people’s work without acknowledging their contribution) is a serious issue on many levels. You are writing a final paper based on the knowledge attained in pursuit of your degree. To that end, this week discuss your thoughts on plagiarism and how it should be addressed from an academic perspective. Consider why it is important not to plagiarize the work of others. Although we have discussed plagiarism in a previous week, this week the question is presented from a different viewpoint being academic integrity and consequences.
Monroe College Week 11 Why It Is Important Not to Plagiarize the Work of Others Discussion

Constructing Social Knowledge Graph from Twitter Data

Yue Han Loke 1.1 Introduction The current era of technology allows its users to post and share their thoughts, images, and content via networks through different forms of applications and websites such as Twitter, Facebook and Instagram. With the emerging of social media in our daily lives and it is becoming a norm for the current generation to share data, researchers are starting to perform studies on the data that could be collected from social media [1] [2].The context of this research will be solely dedicated to Twitter data due to its publicly available wealth of data and its public Stream API. Twitter’s tweets can be used to discover new knowledge, such as recommendations, and relationships for data analysis. Tweets in general are short microblogs consisting of maximum 140 characters that can consists of normal sentences to hashtags and tags with “@”, other short abbreviation of words (gtg, 2night), and different form of a word (yup, nope). Observing how tweets are posted shows the noisy and short lexical nature of these texts. This presents a challenge to the flexibility of Twitter data analysis. On the other hand, the availability of existing research conducted on entity extraction and entity linking has decreased the gap between entities extracted and the relationships that could be discovered. Since 2014, the introduction of the Named Entity rEcognition and Linking (NEEL) Challenge [3] has proved the significance of automated entity extraction, entity linking and classification appearing in different event streams of English tweets in the research and commercial communities to design and develop systems that could solve the challenging nature in tweets and to mine semantics from them. 1.2 Project Aim The focus of this research aims to construct a social knowledge graph (Knowledge Base) from Twitter data. A knowledge graph is a technique to analyse social media networks using the method of mapping and measurement for both relationships and information flows among group, organizations, and other connected entities in social networks [4]. A few tasks are required to successfully create a knowledge graph based on Twitter data A method to aid in the construction of knowledge graph is by extracting named entitiessuch as persons, organizations, locations, or brands from the tweets [5]. In the domain of this research, the named entity to be referenced in the tweet is defined as a proper noun or acronym if it is found in the NEEL Taxonomy in the Appendix A of [3], and is linked to an English DBpedia [6] referent and a NIL referent. The second component in creating a social knowledge graph is to utilize those extracted entities and link them to their respective entities in a knowledge base. For example, Tweet: The ITEE department is organizing a pizza gettogether at UQ. #awesome ITEE refers to an organization and UQ refers to an organization as well. The annotation for this is [ITEE, organization, NIL1], where NIL1 refers to the unique NIL referent describing the real-world entity “ITEE” that does not have the equivalent entry in DBpedia and [UQ, Organization, dbp:University_of_Queensland] which represents the RDF triple (subject, predicate, object). 1.3 Project Goals Firstly, getting the Twitter tweets. This can be achieved by crawling Twitter data using Public Stream API[1] available in the Twitter developer website. The Public Stream API allows extraction of Twitter data in real time. Next, entity extraction and typing with the aid of a specifically chosen information extraction pipeline called TwitIE[2] – open-source and specific to social media and has been tested most extensively on microblog sentences. This pipeline receives the tweets as input and recognises the entities in the same tweet. The third task is to link those entities mined from tweets to the entities in the available knowledge base. The knowledge base that has been selected for the context of this project is DBpedia. If there is a referent in DBpedia, the entity extracted will be linked to that referent. Thus, the entity type is retrieved based on the category received from the knowledge base. In the event of the unavailability of a referent, a NIL identifier is given as shown in section 1.2. The selection of an entity linking system with the appropriate entity disambiguation and candidate entity generation that receives the extracted entities from the same Tweet and produce a list with all the candidate entities in the knowledge base. The task is to accurately link the correct entity extracted to one of the candidates. The social knowledge graph is an entity-entity graph combining two extracted sources of entities. The first is the analysis of the co-occurrence of those entities in same tweet or same sentence. Besides that, the existing relationships or categories extracted from DBpedia. Thus, the project aims to combine the extraction of co-occurrence of extracted entities and the extracted relationships to create a social knowledge graph to unlock new knowledge from the fusion of the two data sources. Named Entity Recognition (NER), Information Extraction (IE) are generally well researched in the domain of longer text such as newswire. However, overall, microblogs are possibly the hardest kind of content to process. For Twitter, some methods have been proposed by the research community such as [7] that uses a pipeline approach to perform the first tokenisation and POS tagging and topic models were used to find named entities. [8] propose a gradient-descent graph-based method for doing joint text normalisation and recognition, reaching 83.6% F1 measure. Besides that, entity linking in knowledge graphs have been studied in [9] using graph-based method by collectively gather the referent entities of all named entities in the same document and by modelling and exploiting the global interdependence between Entity Linking decisions. However, the combination of NER, and Entity Linking in Twitter tweets is still a new area of research since the NEEL challenge was first established in 2013. Based on the evaluation conducted in [10] on the NEEL challenge, lexical similarity mention detection strategy that exploit the popularity of the entities and apply a distance similarity functions to rank entities efficiently, and n-gram [11] features are used. Besides that, Conditional Random Forest (CRF) [12] is another mentioned entity extraction strategy. In the entity detection context, graph distances and various ranking features were used. 2.1. Twitter crawling [13] defined the public “Twitter Streaming API” provides the ability of collecting a sample of user tweets. Using the statuses/filter API provides a constant stream of public Tweets. Multiple optional parameters may be specified such as language and locations. Applying the method CreateStreamingConnection,a POST request to the API has the capability of returning the public statuses as a stream. The rate limit of the Streaming API allows each application to submit up to 5,000 Twitter. [13] Based on the documentation, Twitter currently allows the public to retrieve at most a 1% sample of their data posted on Twitter at a specific time. Twitter will begin to return the sample data to the user when the number of tweets reaches 1% of all tweets on Twitter. According to [14] research comparing Twitter Streaming API and Twitter Firehouse, the final results of the Streaming API depends strongly on the coverage and the type of analysis that the researcher wishes to perform. For example, the researchers found that if given a set of parameters and the number of tweets matching them increases, the coverage of the Streaming API is reduced. Thus, if the research is concerning a filtered content, the Twitter Firehose would be a better choice with regards to its drawback of restrictive cost. However, since our project requires random sampling of Twitter data without filters except for English language, Twitter Streaming API would be an appropriate choice since it is freely available. 2.2. Entity Extraction [15] suggested an open-source pipeline, called TwitIE which is solely dedicated for social media components in GATE [16]. TwitIE consists for 7 parts: tweet import, language identification, tokenisation, gazetteer, sentence splitter, normalisation, part-of-speech tagging, and named entity recogniser. Twitter data is delivered from the Twitter Streaming API in JSON format. TwitIE included a new Format_Twitter plugin in the most recent GATE codebase which converts the tweets in JSON format automatically into GATE documents. This converter is automatically associated with documents names that end in .json, if not text/x-json-twitter should be specified. The TwitIE system uses TextCat – a language processing and identification algorithm for its language identification. It has the capability to provide reliable tweet language identification for tweets written in English using the English POS tagger and named entity recogniser. Tokenisation oversees different characters, class sequence and rules. Since the TwitIE system is dealing with microblogs, it treats abbreviations and URL’s as one token each by following the Ritter’s tokenisation scheme. Hashtags and user mentions are considered as two tokens and is covered by a separate annotation hashtags. Normalisation in TwitIE system is divided into two task: the identification of orthographic errors and correction of the errors found. The TwitIE Normaliser is designed specific to social media. TwitIE reuses the ANNIE gazetteer lists which contain lists such as cities, organisations, days of the week, etc. TwiTie uses the adapted version of the Stanford Part-of speech tagger which is tweets tagged with Penn TreeBank(PTB) tagset trained. The results of using the combination of normalisation, gazetteer name lookup, and POS tagger, the performance was increased to 86.93%. It was further increased to 90.54% token accuracy when the PTB tagset was used. Named entity recognition in TwitIE has a “ 30% absolute precision and 20% absolute performance increase as compare to ANNIE, mainly respect to date, Organizations and Person”. [7] proposed an innovative approach to distant supervision using topic models that pulls large amount of entities gathered from Freebase, and large amount of unlabelled data. Using those entities gathered, the approach combines information about an entity’s context across its mentions. T-NER POS Tagging system called T-POS has added new tags for Twitter specific phenomenal retweets such as usernames, urls and hashtags. The system uses clustering to group together distributionally similar words for lexical variations and OOV words. T-POS utilizes the Brown Clusters and Conditional Random Fields. The combination of both features results in the ability to model strong dependencies between adjacent POS tags and make use of highly correlated features. The results of the T-POS are shown on a 4-fold cross validation over 800 tweets. It is proved that “T-POS outperforms the Standford tagger, obtaining a 26% reduction in error”. Besides that, when trained on 102K tokens, there is an error reduction of 41%. The system includes shallow parsing which can identify non-recursive phrases such as noun, verb and prepositional phrases in text. T-NER’s shallow parsing component called T-CHUNK, obtained a better performance at shallow parsing of tweets as compared against the off the shelf OpenNLP chunker. As reported, “a 22% reduction in error”. Another component of the T-NER is the capitalization classifier, T-CAP, which analyse a tweet to predict capitalization. Named entity recognition in T-NER is divided into two components: Named Entity Segmentation using T-SEG, and classifying named entities by applying LabeledLDA. T-SEG uses IOB encoding on sequence-labelling task to represent segmentations. Furthermore, Conditional Random Fields is used for learning and inference. Contextual, dictionary and orthographic features: a set of type lists is included in the in-house dictionaries gathered from Freebase. Additionally, outputs of T-POS, T-CHUNK and T-CAP, and the Brown clusters are used to generate features. The outcome of the T-SEG as stated in the research paper, “Compared with the state-of-the-art news-trained Stanford Named Entity Recognizer. T-SEG obtains a 52% increase in F1 score”. To address the issues of lack of context in tweets to identify the types of entities they contain and excessive distinctive named entity types present in tweets, the research paper presented and assessed a distantly supervised approach based on LabeledLD. This approach utilizes modelling of every entity as a combination of types. This allows information about an entity’s distribution over types to be shared across mentions, naturally handling ambiguous entity strings whose mentions could refer to different types. Based on the empirical experiments conducted, there is a 25% increase in F1 score over the co-training approach to Named Entity Classification suggested by Collins and Singer (1999) when applied to Twitter. [17] proposed a Twitter adapted version of Kanopy called Kanopy4Tweets that uses the approach of interlinking text documents with a knowledge base by using the relations between concepts and their neighbouring graph structure. The system consists of four parts: Name Entity Recogniser (NER), Named Entity Linking (NEL), Named Entity Disambiguation(NED) and Nil Resources Clustering(NRC). The NER of Kanopy4Tweets uses a TwitIE – a Twitter information extraction pipeline mentioned above. For the Named Entity Linking. For NEL, a DBpedia index is build using a selection of datasets to search for suitable DBpedia resource candidates for each extracted entity. The datasets are store in a single binary file using HDT RDF format. This format has compact structures due to its binary representation of RDF data. It allows for faster search functionality without the need of decompression. The datasets can be quickly browse and scan through for a specific object, subject or predicate at glance. For each named entity found by NER component, a list of resource candidates retrieved from DBpedia can be obtain using the top-down strategy. One of the challenges found is the large volume of found resource candidates impacts negatively on the processing time for disambiguation process. However, this problem can be resolved by reducing the number of candidates using a ranking method. The proposed ranking method ranks the candidates according to the document score assigned by the indexing engine and selects the top-x elements. The NED takes an input of a list of named entities which are candidate DBpedia resources after the previous NEL process. The best candidate resource for each named entity is selected as output. A relatedness score is calculated based on the number of paths between the resources weighted by the exclusivity of the edges of these paths which is applied to candidates with respect to the candidate resources of all other entities. The input named entities are jointly disambiguated and linked to the candidate resources with the highest combined relatedness. NRC is a stage whereby if there are no resource in the knowledge base that can be linked to a named entity extracted. Using the Monge-Elkan similarity measure, the first NIL element is assign into a new cluster, then the next element is used to differentiate from the previous ones. An element is added to a cluster when the similarity between an element and the present clusters is above a fixed threshold, the element is added to that particular cluster, whereas a new cluster is formed if there are no current cluster with a similarity above the threshold is found. 2.3. Entity Extraction and Entity Linking [18]proposed a lexicon-based joint Entity Extraction and Entity Linking approach, where n-grams from tweets are mapped to DBpedia entities. A pre-processing stage cleans and classifies the part-of-speech tags, and normalises the initial tweets converting alphabetic, numeric, and symbolic Unicode characters to ASCII equivalents. Tokenisation is performed on non-characters except special characters joining compound words. The resulting list of tokens is fed into a shingle filter to construct token n-grams from the token stream. In the candidate mapping component, a gazetteer is used to map each token that is compiled from DBpedia redirect labels, disambiguation labels and entities’ labels that is linked to their own DBpedia entities. All labels are lowercase indexed and linked by exact matches only to the list of candidate entities in the form of tokens. The researcher used a method of prioritizing longer tokens than shorter ones to remove possible overlaps of tokens. For each entity candidate, it considers both local and context-related features via a pipeline of analysis scorers. Examples of local features included are string distance between the candidate labels and the n-gram, the origin of the label, its DBpedia type, the candidates link graph popularity, the level of uncertainty of the token, and the surface form that matches best. On the other hand, the relation between a candidate entity and other candidates with a given context is accessed by the context-related features. Examples of mentioned context-related features are “direct links to other context candidates in the DBpedia link graph, co-occurrence of other token’s surface forms in the corresponding Wikipedia article of the candidate under consideration, co-references in Wikipedia article, and further graph based feature of the link graph induced by all candidates of the context graph which includes “graph distance measurements, connected component analysis, or centrality and density observations”. Besides that, the candidates are sorted per their confidence score based on how an entity describes a mention. If the confidence score is lower than the threshold chosen, a NIL referent is annotated. [19] proposed a lexical based and n-grams features to look up resources in DBpedia. The role of the entity type was assigned by a Conditional Random Forest (CRF) classifier, that is specifically trained using DBpedia related feature (local features), word embedding (contextual features), temporal popularity knowledge of an entity extracted from Wikipedia page view data, string similarity measures to measure the similarity between the title of the entity and the mention (string distance), and linguistic features, with additional pruning stage to increase the precision of Entity Linking. The whole process of the system is split into five stages: pre-processing, mention candidate generation, mention detection and disambiguation (candidate selection), NIL detection and entity mention typing prediction. In the pre-processing stage, tweet tokenisation and part-of-speech tags were used based on ARK Twitter Part-of-Speech Tagger, together with the tweet timestamps extracted from tweet ID. The researchers used an in-house mention-entity dictionary of acronyms. This dictionary computes the n-grams (n<10) and a query to the dictionary is performed on each of the n-gram split mentions. There are four methods used to generate candidates such as exact search, fuzzy search, approximate search and acronym search. Learning-to-rank approach was applied on the candidate selection. A confidence score is computed for each mention and is regarded as the output of supervised learning approach using a random forest classifier. In the event of an overlap of entity mentions, the one with the highest score is selected based on an empirically defined threshold. The NIL detection is done using the random forest algorithm. The entity mention typing prediction stage is conducted as a supervised learning task. Two independent classifiers are built on entity and NIL mentions: a logistic regression classifier and a random forest. [20] research paper proposed an entity linking technique to link named entity mentions appearing in Web text with their corresponding entities in a knowledge base. The solution mentioned is by employing a knowledge base. Due to the vast knowledge shared among communities and the development of information extraction techniques, the existence of automated large scale knowledge bases has been ensured. Thus, this rich information about the world’s entities, their relationships, and their semantic classes which are all possibly populated into a knowledge base, the method of relation extraction techniques is vital to obtain those web data that promotes discovery of useful relationships between entities extracted from text and their extracted relation. Once possible way is to map those entities extracted and associated them to a knowledge base before it could be populated into a knowledge base. The goal of entity linking is to map ever textual entity mention m ∈ M to its corresponding entry e ∈ E in the knowledge base. In some cases, when the entity mentioned in text does not have its corresponding entity record in the given knowledge base, a NIL referent is given to indicate a special label of un-linkable. It is mentioned in the paper that named entity recognition and entity linking o be jointly perform for both processes to strengthen one another. A method proposed in this paper is candidate entity generation. The objective of the entity linking system is to filter out irrelevant entities in the knowledge base that for each entity extracted. A list of candidates which might be the possible entities that the extracted entity is referring to is retrieved. The paper suggested three techniques to handle this goal such as name based dictionary techniques – entity pages, redirect pages, disambiguation pages, bold phrases from the first paragraphs, and hyperlinks in Wikipedia articles. Another method proposed is the surface form expansion from the local document that consists of heuristics based methods and supervised learning methods, and methods based on search engine. In the context of candidate entity ranking method, five categories of methods are advised. The supervised ranking methods, unsupervised ranking methods, independent ranking methods, collective ranking methods and collaborative ranking methods. Lastly, the research paper mentioned ways to evaluate entity linking systems using precision, recall, F1-measure and accuracy. Despite all these methods used in the three main approaches is proposed to handle entity linking system, the paper clarified that it is still unclear which are the best techniques and systems. This is since different entity linking system react or perform differently according to datasets and domains. [21] proposed a new versatile algorithm based on multiple addictive regression trees called S-MART (Structured Multiple Additive Regression Trees) which emphasized on non-linear tree-based models and structured learning. The framework is a generalized Multiple Addictive Regression Trees (MART) but is adapted for structured learning. This proposed algorithm was tested on entity linking primarily focused on tweet entity linking. The evaluation of the algorithm is based on both IE and IR situations. It is shown that non-linear performs better than linear during IE. However, for the IR setting, the results are similar except for LambdaRank, a neural network based model. The adoption of polynomial kernel further improves the performance of entity linking by non-LINEAR SSVM. The paper proved that entity linking of tweets perform better using tree-based non-linear models rather than the alternative linear and non-linear methods in IE and IR driven evaluations. Based on the experiments conducted, the S-MART framework outperforms the current up-to-date entity linking systems. 2.4. Entity Linking and Knowledge Base Based on [22], an approach to free text relation extraction was proposed. The system was trained to extract the entities from the text from existing large scale knowledge base in a cooperatively manner. Furthermore, it utilizes the learning of low-dimensional embedding of words, entities and relationships from a knowledge base with regards to score functions. Built upon the norm of employing weakly labelled text mention data but with a modified version which extract triples from the existing knowledge bases. Thus, by generalizing from knowledge base, it can learn the plausibility of new triples (h, r, t); h is the left-hand side entity (or head), the right-hand side entity (or tail) and r the relationship linking them, even though this specific triple does not exist. By using all knowledge base triples rather than training only on (mention, relationship), the precision on relation extraction was proved to be significantly improved. [1] presented a novel system for named entity linking over microblog posts by leveraging the linked nature of DBpedia as knowledge base and using graph centrality scoring as disambiguation methods to overcome polysemy and synonymy problems. The motivation for the authors to create this method is because linked entities tend to appear in the same tweets because tweets are topic specific and together with the assumption since tweets are topic specific, related entities tend to appear in the same tweet. Since the system is tackling noisy tweets acronyms handling and Hashtags in the process of entity linking were integrated. The system was compared with TAGME, a state-of-the-art system for named entity linking designed for short text. The results shown that it outperformed TAGME in Precision, Recall and F1 metrics with 68.3%, 70.8% and 69.5%. [23] presented an automated method to populate a Web-scale probabilistic knowledge base called Knowledge Vault (KV) that uses the combination of extractions from the Web such as text documents (TXT), HTML trees (DOM), Html tables (TBL), and Human Annotated pages (ANO). By using RDF triples (subject, predicate, object) with association to a confidence score that represents the probability that KV believes the triple is correct. In addition, all 4 extractors are merged together to form one system called FUSED-EX by constructing a feature vector for each extracted triple. Next, a binary classifier is applied to compute the formula. The advantages of using this fusion extractor is that it can learn the relative reliabilities of each system as well as creating a model of the reliabilities. The benefits of combining multiple extractors include 7% higher confidence triples and a high AUC score (the higher probability that a classifier will choose a randomly chosen positive instance to be ranked) of 0.927. To overcome the unreliability of facts extracted from the Web, prior knowledge is used. In the domain of this paper, Freebase is used to fit the existing models. Two ways were proposed in the paper which are “Path ranking algorithm” with AUC scores of 0.884 and the “Neural network model” with a AUC score of 0.882. A fusion of both methods stated was conducted to increase performance with an increased AUC score of 0.911. With the evidence of the benefits of fusion quantitatively, the authors of the paper proposed another fusion of the prior methods and the extractors to gain additional performance boost. The result of the fusion is a generation of 271M high confidence facts with 33% new facts that are unavailable in Freebase. [24]proposed TremenRank, a graph based model to tackle the target entity disambiguation challenge, task of identifying target entities of the same domain. The motivation of this system is due to the challenges and unreliability of current methods that relies on knowledge resources, the shortness of the context which a target word occurs, and the large scale of the document collected. To overcome these challenges, first TremenRank was built upon the notion of collectively identity target entities in short texts. This reduces memory storage because the graph is constructed locally and is continuously scale-up linearly as per the number of target entities. This graph was created locally via inverted index technology. There are two types of indexes used: the document-to-word index and the word-to-document index. Next, the collection of documents (the shorts texts) are modelled as a multi-layer directed graph that holds various trust scores via propagation. This trust score provided an indication of the possibility of a true mention in a short text. A series of experiments was conducted on TremenRank and the model is more superior than the current advanced methods with a difference of 24.8% increase in accuracy and 15.2% increase in F1. [25]introduced a probabilistic fusion system called SIGMAKB that integrates strong, high precision knowledge base and weaker, and nosier knowledge bases into a single monolithic knowledge base. The system uses the Consensus Maximization Fusion algorithm to validate, aggregate, and ensemble knowledge extracted from web-scale knowledge bases such as YAGO and NELL and 69 Knowledge Base Population. The algorithm combines multiple supervised classifiers (high-quality and clean KBs), motivated by distant supervision and unsupervised classifiers (noisy KBs) Using this algorithm, a probabilistic interpretation of the results from complementary and conflicting data values can be shown in a singular response to its user. Thus, using a consensus maximization component, the supervised and unsupervised data collected from the method stated above produces a final combined probability for each triple. The standardization of string named entities and alignment of different ontologies is done in the pre-processing stage. Project plan Semester 1 Task Start End Duration(days) Milestone Research: 23/03/2017 Twitter Call 27/02/2017 02/03/2017 4 Entity Recognition 27/02/2017 02/03/2017 4 Entity Extraction 02/03/2017 02/03/2017 7 Entity Linking 09/03/2017 16/03/2017 7 Knowledge Base Fusion 16/03/2017 23/03/2017 7 Proposal 27/02/2017 30/03/2017 30 30/03/2017 Crawling Twitter data using Public Stream API 31/03/2017 15/04/2017 15 15/04/2017 Collect Twitter data for training purp Cite This Work To export a reference to this article please select a referencing stye below: APA MLA MLA-7 Harvard Vancouver Wikipedia OSCOLA UKEssays. (November 2018). Constructing Social Knowledge Graph from Twitter Data. Retrieved from https://www./essays/information-technology/constructing-social-knowledge-graph-twitter-7806.php?vref=1 Copy to Clipboard Reference Copied to Clipboard. “Constructing Social Knowledge Graph from Twitter Data.” .com. 11 2018. UKEssays. 08 2021 . Copy to Clipboard Reference Copied to Clipboard. “Constructing Social Knowledge Graph from Twitter Data.” UKEssays., November 2018. Web. 24 August 2021. . Copy to Clipboard Reference Copied to Clipboard. UKEssays. November 2018. Constructing Social Knowledge Graph from Twitter Data. [online]. Available from: [Accessed 24 August 2021]. Copy to Clipboard Reference Copied to Clipboard. UKEssays. Constructing Social Knowledge Graph from Twitter Data [Internet]. November 2018. [Accessed 24 August 2021]; Available from: Copy to Clipboard Reference Copied to Clipboard. {{cite web|last=Answers |first=All |url= |title=Constructing Social Knowledge Graph from Twitter Data | |date=November 2018 |accessdate=24 August 2021 |location=Nottingham, UK}} Copy to Clipboard Reference Copied to Clipboard. All Answers ltd, ‘Constructing Social Knowledge Graph from Twitter Data’ (, August 2021) accessed 24 August 2021 Copy to Clipboard Reference Copied to Clipboard. 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