Biomedical search engine

  • The question 
For 3 years I was helping to develop a biomedical entity finder. The project consisted of, using the different biomedical databases and ontologies, developing text mining algorithms to detect these entities, index them and allow access through a search engine.  
  • The analysis
To achieve this in the backend, a relational database was used to store the entities, and XML files containing the texts from various sources. This was parsed using the Java framework Apache UIMA, annotated by perl algorithms using ontologies and finally indexed in Lucene.




  • The product
Here you can see a figure of the biomedical search engine, called novoseek


Comments

Post a Comment

Popular posts from this blog

Building a Powerful Chatbot with OpenAI and Elasticsearch Integration

 In the realm of modern technological advancements, harnessing the capabilities of artificial intelligence (AI) has become indispensable. One such manifestation of AI prowess lies in the creation of intelligent chatbots that not only understand user queries but also interact with data repositories to fetch relevant information. In this article, we'll delve into the process of developing a sophisticated chatbot using OpenAI's cutting-edge technology and seamlessly integrating it with Elasticsearch for efficient data retrieval. Understanding the Components: Before diving into the intricacies of building our chatbot, let's dissect the key components involved: OpenAI: OpenAI provides state-of-the-art natural language processing (NLP) capabilities, empowering developers to build conversational AI agents. By leveraging OpenAI's API, we can imbue our chatbot with the ability to comprehend and respond to user queries effectively. Elasticsearch: Elasticsearch serves as a powerfu...
Read more

Legal Text Analysis Project: Leveraging NLP for Enhanced Understanding and Insight

Image
 A key point in the development of NLP solutions is the consideration of language proficiency. The terms and jargon used in specialized sectors, such as the medical sector, differ greatly from those used in the financial sector, which in turn are not comparable to those in the legal sector. The legal texts contain a series of specifications such as: The breadth of the domain in terms of textual typology. The variety of target groups. The linguistic features of the domain. Furthermore, in the latter case, not only is the legal terminology of the domain covered, but it also tends to co-occur with terminologies from all areas. The limited number of NLP resources and tools tailored to the overall domain. The predominance of English, since most of the resources and tools available are developed for processing texts in English. A slowed adoption of smart technologies in the legal and administrative sector compared to other sectors such as the biomedical or financial sector. The heterogen...
Read more

Streamlining Log Analysis with Logstash, Filebeat, and Elasticsearch

Image
 In the realm of IT infrastructure management, effectively monitoring and analyzing application logs is crucial for maintaining system reliability and performance. To address this imperative, I've devised a robust system harnessing the capabilities of Logstash, Filebeat, and Elasticsearch. At its core, this system is designed to seamlessly ingest, process, and index application logs into Elasticsearch for comprehensive analysis and visualization. Let's delve into the key components and functionality of this integrated solution: Log Ingestion with Filebeat: Filebeat serves as the lightweight shipper responsible for tailing application log files and forwarding them to Logstash for processing. Its efficient design ensures minimal resource overhead while guaranteeing real-time log collection from diverse sources. Data Processing with Logstash: Logstash acts as the central processing engine, facilitating data transformation, enrichment, and filtering before indexing it into Elastics...
Read more