Background: Automatic recognition of medical concepts in unstructured text is an important component of many clinical and research applications, and its accuracy has a large impact on electronic health record analysis. The mining of medical concepts is complicated by the broad use of synonyms and nonstandard terms in medical documents. Objective: We present a machine learning model for concept recognition in large unstructured text, which optimizes the use of ontological structures and can identify previously unobserved synonyms for concepts in the ontology. Methods: We present a neural dictionary model that can be used to predict if a phrase is synonymous to a concept in a reference ontology. Our model, called the Neural Concept Recognizer (NCR), uses a convolutional neural network to encode input phrases and then rank medical concepts based on the similarity in that space.
Notwithstanding the differences displayed by drama criticism for what concerns the theoretical stance and the aims, the key role played by emotions seems to be a common aspect to most definitions of drama. Although the field of computational drama has acknowledged this trend, we argue that the role of emotions in character design deserves more attention, as it is the key to establish a distinction between autonomous agents and drama characters. In this paper, we analyze a fragment of drama, taken from Shakespeare's Hamlet, to show how characters can be construed from agents as an outcome of drama, through the manipulation of their emotional qualities.
We discuss the problems associated with versioning ontologies in distributed environments. This is an important issue because ontologies can be of great use in structuring and querying intemet information, but many of the Intemet's characteristics, such as distributed ownership, rapid evolution, and heterogeneity, make ontology management difficult. We present SHOE, a web-based knowledge representation language that supports multiple versions of ontologies. We then discuss the features of SHOE that address ontology versioning, the affects of ontology revision on SHOE web pages, and methods for implementing ontology integration using SHOE's extension and version mechanisms. 1. Introduction As the use of ontologies becomes more prevalent, there is a more pressing need for good ontology management schemes. This is especially true once an ontology has been used to structure data, since changing it can be very expensive. Often the solution is to "get it right the first time", however, in long term applications, there is always the chance that new information will be discovered or that different features of the domain will become important. Therefore, we must think of ontology development as an ongoing process. In a centralized environment, it may be possible to coordinate ontology revisions with corresponding revisions to the data that was structured using the ontology. However, as the volume of data increases this become more difficult.
Largest free, formal ontology available, with 25,000 terms and 80,000 axioms when all domain ontologies are combined. These consist of SUMO itself, the MId-Level Ontology (MILO), and ontologies of communications, countries and regions, distributed computing and user interfaces, economy, finance, automobiles and engineering components, Food, Dining, Sports, Shopping catalogs and Hotels, geography, government and Justice, language taxonomy, media and Music, Military (general, devices, processes, people), North American Industrial Classification System, people and their Emotions, physical elements, transnational issues, transportation and its Details, viruses, world airports A-K, world airports L-Z, weapons of mass destruction. See also a large amount of instance content from DBPedia about people and the YAGO, project which includes millions of facts from Wikipedia merged with SUMO, and an initial merge of the Mondial geographical data with SUMO. The Open Biomedical Ontologies are lightly mapped to SUMO. Additional ontologies of terrorism are available on request.