## 2 State-of-the-Art
### Creating an Expert Profile Using External Sources  
Creating a comprehensive and accurate expert profile involves various technologies and methodologies. These methods gather, verify, and synthesize data from multiple external platforms. The following papers mainly focused on the LinkedIn platform, as it is one of the most popular platforms for professional endeavors and career related networking. In [8], the authors present how LinkedIn data can be mined in order to create expert profiles using LinkedIn API. The data available on LinkedIn contain redundancies because users enter their information in various ways. For example, in the \"Education\" section, the name of a university might appear in different formats across profiles, even though all refer to the same institution. Because of this, the paper presents how the collected data are normalized before being used so that redundancies are removed. A novel approach to extracting expert information from personal homepages using deep learning techniques is described in [9]. A key contribution is the segmentation of web pages into smaller text units, enabling the model to focus on relevant information more effectively. Integrating prior knowledge, such as named entity recognition and regular expressions, significantly enhances the model's ability to accurately identify and extract crucial data. The authors propose using a long short-term memory (LSTM) network to capture the contextual dependencies within text units. This allows a more precise classification of words and segments, leading to improved overall extraction performance. The experimental results presented in the paper demonstrate the model's superior accuracy in generating comprehensive expert profiles compared to traditional methods. While the model presented in the paper handles diverse web page formats, there are still challenges related to varying page structures. The paper highlights the potential for future work to address these issues and further enhance the model's capabilities. In [10], the authors present a data-driven approach for profile extraction based on Resumes. In the article, the authors leverage natural language processing (NLP) techniques like keyword extraction and document representation to create profiles. While the paper focuses on resumes, the underlying techniques used can be similarly applied for creating expert profiles from LinkedIn profiles. Another paper related to profile extraction is [11], in which the authors aim to create a comprehensive database of college alumni by scraping data from LinkedIn profiles. To do this, web scraping techniques are leveraged to collect and organize details from the user's profiles, such as employment history, educational background, skills, and professional connections. The methodology presented involves using Python programming and web scraping libraries to extract information from public LinkedIn profiles. This is carried out while ensuring compliance with LinkedIn's terms of service and privacy policies. After collecting the data, data cleaning and structuring techniques are necessary to ensure the accuracy and consistency in the collected data. The paper demonstrates that web scraping techniques can collect data from LinkedIn profiles. However, a limitation is that it does not address interpreting unstructured data such as descriptions or comments. In order to overcome this issue NLP, techniques can be used to extract the essential information from these unstructured data sources. In [12], the authors aim to extract content-based user profiles from the data available on LinkedIn to have an image of the users' interests that can be used to recommend interesting academic research papers. In order to do so, an extractor system is developed, which processes the information extracted from LinkedIn for building the researcher profile. A novel idea presented in the paper was to utilize both the professional data of the researcher and that of their social graph connections. This provides a more accurate picture of the researcher's interests.