Amanda Hicks

Mining Twitter to Assess the Public Perception of the "Internet of Things".

Social media analysis has shown tremendous potential to understand publics opinion on a wide variety of topics. In this paper, we have mined Twitter to understand the publics perception of the Internet of Things (IoT). We first generated the discussion trends of the IoT from multiple Twitter data sources and validated these trends with Google Trends. We then performed sentiment analysis to gain insights of the public’s attitude towards the IoT. As anticipated, our analysis indicates that the publics perception of the IoT is predominantly positive. Further, through topic modeling, we learned that public tweets discussing the IoT were often focused on business and technology. However, the public has great concerns about privacy and security issues toward the IoT based on the frequent appearance of related terms. Nevertheless, no unexpected perceptions were identified through our analysis. Our analysis was challenged by the limited fraction of tweets relevant to our study. Also, the user demographics of Twitter users may not be strongly representative of the population of the general public.

Towards an obesity-cancer knowledge base: Biomedical entity identification and relation detection

Obesity is associated with increased risks of various types of cancer, as well as a wide range of other chronic diseases. On the other hand, access to health information activates patient participation, and improve their health outcomes. However, existing online information on obesity and its relationship to cancer is heterogeneous ranging from pre-clinical models and case studies to mere hypothesis-based scientific arguments. A formal knowledge representation (i.e., a semantic knowledge base) would help better organizing and delivering quality health information related to obesity and cancer that consumers need. Nevertheless, current ontologies describing obesity, cancer and related entities are not designed to guide automatic knowledge base construction from heterogeneous information sources. Thus, in this paper, we present methods for named-entity recognition (NER) to extract biomedical entities from scholarly articles and for detecting if two biomedical entities are related, with the long term goal of building a obesity-cancer knowledge base. We leverage both linguistic and statistical approaches in the NER task, which supersedes the state-of-the-art results. Further, based on statistical features extracted from the sentences, our method for relation detection obtains an accuracy of 99.3% and a f-measure of 0.993.

Cross-Lingual Evaluation of Ontologies with Rudify

Rudify is a set of tools used for automatically annotating concepts in an ontology with the ontological meta-properties employed by OntoClean [1]. While OntoClean provides a methodology for evaluating ontological hierarchies based on ontological meta-properties of the concepts in the hierarchy, it does not provide a method for determining the meta-properties of a given concept within an ontology. Rudify has been developed to help bridge this gap, and has been used in the KYOTO project to facilitate ontology development. The general idea behind Rudify is the assumption that a preferred set of linguistic expressions is used when talking about ontological meta-properties. Thus, one can deduce a concept’s meta-properties from the usage of the concept’s lexical representation (LR) in natural language. This paper describes the theory behind Rudify, the development of Rudify, and evaluates Rudify’s output for the rigidity of base concepts in English, Dutch, and Spanish. Our overall conclusion is that the decisive output for English is useable data, while the procedure currently exploited by Rudify does not easily carry over to Spanish and Dutch.

The ontology of medically related social entities: recent developments.

BackgroundThe Ontology of Medically Related Social Entities (OMRSE) was initially developed in 2011 to provide a framework for modeling demographic data in Resource Description Framework/Web Ontology Language. It is built upon the Basic Formal Ontology and conforms to Open Biomedical Ontologies Foundry’s best practices.DescriptionWe report recent development of OMRSE which includes representations of organizations, roles, facilities, demographic data, enrollment in insurance plans, and data about socio-economic indicators.ConclusionsOMRSE’s coverage has been expanding in recent years to include a wide variety of classes and has been useful in several biomedical applications.

Therapeutic indications and other use-case-driven updates in the drug ontology: anti-malarials, anti-hypertensives, opioid analgesics, and a large term request

BackgroundThe Drug Ontology (DrOn) is an OWL2-based representation of drug products and their ingredients, mechanisms of action, strengths, and dose forms. We originally created DrOn for use cases in comparative effectiveness research, primarily to identify historically complete sets of United States National Drug Codes (NDCs) that represent packaged drug products, by the ingredient(s), mechanism(s) of action, and so on contained in those products. Although we had designed DrOn from the outset to carefully distinguish those entities that have a therapeutic indication from those entities that have a molecular mechanism of action, we had not previously represented in DrOn any particular therapeutic indication.ResultsIn this work, we add therapeutic indications for three research use cases: resistant hypertension, malaria, and opioid abuse research. We also added mechanisms of action for opioid analgesics and added 108 classes representing drug products in response to a large term request from the Program for Resistance, Immunology, Surveillance and Modeling of Malaria in Uganda (PRISM) project. The net result is a new version of DrOn, current to May 2016, that represents three major therapeutic classes of drugs and six new mechanisms of action.ConclusionsA therapeutic indication of a drug product is represented as a therapeutic function in DrOn. Adverse effects of drug products, as well as other therapeutic uses for which the drug product was not designed are dispositions. Our work provides a framework for representing additional therapeutic indications, adverse effects, and uses of drug products beyond their design. Our work also validated our past modeling decisions for specific types of mechanisms of action, namely effects mediated via receptor and/or enzyme binding. DrOn is available at: http://purl.obolibrary.org/obo/dron.owl. A smaller version without NDCs is available at: http://purl.obolibrary.org/obo/dron/dron-lite.owl

The Colloquial WordNet: Extending Princeton WordNet with Neologisms

Princeton WordNet is one of the most important resources for natural language processing, but has not been updated for over ten years and is not suitable for analyzing the fast moving language as used on social media. We propose an extension to WordNet, with new terms that have been found from Twitter and Reddit, and cover language usage that is emergent or vulgar. In addition to our methodology for extraction, we analyze new terms to provide information about how new words are entering the English language. Finally, we discuss publishing this resource both as linguistic linked open data and as part of the Global WordNet Association’s Interlingual Index.