Today, Artificial Intelligence (AI) is being increasingly used to find, crawl, aggregate, and analyze both structured and unstructured data. However, when it comes to filtering and processing data of a particular domain, this plain vanilla approach might not work. For computers to identify key terms and concepts and extract the most relevant data regarding the search query, it needs to know the language of the domain. According to Silke Otte, Chief Marketing Officer of Innoplexus, using a domain-specific ontology in combination with AI-driven tools is efficient in crawling for relevant data. Furthermore, the combination can uncover opportunities for discovering unexplored connections.
Every domain has its own language, frequently referred to as jargon, argot, idiom, legalese, geeks peek, etc. This language, which is specific to a domain, will be discernible to those in another domain and may be indiscernible to others. For example, the words and terms used in daily language are far from what scientists, advocates, or any domain experts speak.
In fact, many terminologies have completely distinct meaning when used in a domain specific context. Take, for example, the word hedgehog. Experts in the human genetics or molecular biology domain will directly associate the word hedgehog with cancer. However, others who are not aware that a protein critical to cell division is also referred to as hedgehog, would definitely associate the word with the hedgehog, the spiny mammal.
Now, suppose computers are tasked with the job of extracting information about the hedgehog the protein and not the animal. There is no way computers can discern the difference and bring up relevant answers. Here is where a domain specific ontology can help.
A domain-specific ontology contains concepts and relations about everything in the domain. Consequently, it can enable computers, understand domain specific terminologies and help the computers find, crawl, and aggregate the most relevant data. Furthermore, ontologies can help improve the data analysis process by leveraging context based tagging to recommend similar resources of information such as articles, searches, results and concepts.
For instance, an ontology specific to Life Sciences can map discoverable concepts from all significant sources, connect observations, and learn unseen concepts. This can help researchers, academicians, and scientists generate associations between disease, gene, drug targets, molecules, MOAs, etc. In sum, ontologies are used to apply consistent language to a domain, which will help both humans and computers understand the language specific to a domain. In addition, they are effective in removing word-sense disambiguation.
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