Relevant Words Extraction Method for Recommendation System

Nowadays, E-commerce is very popular because of information explosion. Text mining is also important for information extraction. Users are more preferable to use the convenience system from many sources such as through web pages, email, social network and so on. This system proposed the relevant words extraction method for car recommendation system from user email. In relevant words extraction, this system proposed the Rule-based approach in Compiling Technique. Context- free grammar is the most suitable for relevant words extraction. Recommendation System (RS) is a most popular tool that helps users to recommend according to their interests. This system implements efficient recommendation system by using proposed key extraction algorithm, Content-based Filtering (CBF) method and Jaccard Coefficient that will help the users who want to buy the car by providing relevant car information

Relevant Words Extraction Method for Recommendation System

Nowadays, E-commerce is very popular because of information explosion. Text mining is also important for information extraction.  Users are more preferable to use the convenience system from many sources such as through web pages, email, social network and so on. This system proposed the relevant words extraction method for car recommendation system from user email. In relevant words extraction, this system proposed the Rule-based approach in Compiling Technique. Context- free grammar is the most suitable for relevant words extraction. Recommendation System (RS) is a most popular tool that helps users to recommend according to their interests. This system implements efficient recommendation system by using proposed key extraction algorithm, Content-based Filtering (CBF) method and Jaccard Coefficient that will help the users who want to buy the car by providing relevant car information

Knowledge Discovery and Management within Service Centers

These days, most enterprise service centers deploy Knowledge Discovery and Management (KDM) systems to address the challenge of timely delivery of a resourceful service request resolution while efficiently utilizing the huge amount of data. These KDM systems facilitate prompt response to the critical service requests and if possible then try to prevent the service requests getting triggered in the first place. Nevertheless, in most cases, information required for a request resolution is dispersed and suppressed under the mountain of irrelevant information over the Internet in unstructured and heterogeneous formats. These heterogeneous data sources and formats complicate the access to reusable knowledge and increase the response time required to reach a resolution. Moreover, the state-of-the art methods neither support effective integration of domain knowledge with the KDM systems nor promote the assimilation of reusable knowledge or Intellectual Capital (IC). With the goal of providing an improved service request resolution within the shortest possible time, this research proposes an IC Management System. The proposed tool efficiently utilizes domain knowledge in the form of semantic web technology to extract the most valuable information from those raw unstructured data and uses that knowledge to formulate service resolution model as a combination of efficient data search, classification, clustering, and recommendation methods. Our proposed solution also handles the technology categorization of a service request which is very crucial in the request resolution process. The system has been extensively evaluated with several experiments and has been used in a real enterprise customer service center

Ontology-Based Clinical Information Extraction Using SNOMED CT

Extracting and encoding clinical information captured in unstructured clinical documents with standard medical terminologies is vital to enable secondary use of clinical data from practice. SNOMED CT is the most comprehensive medical ontology with broad types of concepts and detailed relationships and it has been widely used for many clinical applications. However, few studies have investigated the use of SNOMED CT in clinical information extraction. In this dissertation research, we developed a fine-grained information model based on the SNOMED CT and built novel information extraction systems to recognize clinical entities and identify their relations, as well as to encode them to SNOMED CT concepts. Our evaluation shows that such ontology-based information extraction systems using SNOMED CT could achieve state-of-the-art performance, indicating its potential in clinical natural language processing

Research on Medical Question Answering System Based on Knowledge Graph

To meet the high-efficiency question answering needs of existing patients and doctors, this system integrates medical professional knowledge, knowledge graphs, and question answering systems that conduct man-machine dialogue through natural language. This system locates the medical field, uses crawler technology to use vertical medical websites as data sources, and uses diseases as the core entity to construct a knowledge graph containing 44,000 knowledge entities of 7 types and 300,000 entities of 11 kinds. It is stored in the Neo4j graph database, using rule-based matching methods and string-matching algorithms to construct a domain lexicon to classify and query questions. This system has specific practical value in the medical field knowledge graph and question answering system

Measuring Semantic Similarity of Documents by Using Named Entity Recognition Methods

The work presented in this thesis was born from the desire to map documents with similar semantic concepts between them. We decided to address this problem as a named entity recognition task, where we have identified key concepts in the texts we use, and we have categorized them. So, we can apply named entity recognition techniques and automatically recognize these key concepts inside other documents. However, we propose the use of a classification method based on the recognition of named entities or key phrases, where the method can detect similarities between key concepts of the texts to be analyzed, and through the use of Poincaré embeddings, the model can associate the existing relationship between these concepts. Thanks to the Poincaré Embeddings’ ability to capture relationships between words, we were able to implement this feature in our classifier. Consequently for each word in a text we check if there are words close to it that are also close to the words that make up the key phrases that we use as Gold Standard. Therefore when detecting potential close words that make up a named entity, the classifier then applies a series of characteristics to classify it. The methodology used performed better than when we only considered the POS structure of the named entities and their n-grams. However, determining the POS structure and the n-grams were important to improve the recognition of named entities in our research. By improving time to recognize similar key phrases between documents, some common tasks in large companies can have a notorious benefit. An important example is the evaluation of resumes, to determine the best professional for a specific position. This task is characterized by consuming a lot of time to find the best profiles for a position, but our contribution in this research work considerably reduces that time, finding the best profiles for a job. Here the experiments are shown considering job descriptions and real resumes, and the methodology used to determine the representation of each of these documents through their key phrases is explained