arXiv subjects

Deborah L. McGuinness

Publications and source records attributed to Deborah L. McGuinness.

25 records · Page 2Linked to original sources

arXiv2018

Semantically-aware population health risk analyses

One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition. Examples include finding lifestyle factors associated with chronic diseases and finding genetic mutations associated with diseases in precision health. We develop a combined semantic and machine learning system that uses a health risk ontology and knowledge graph (KG) to dynamically discover risk factors and their associated subpopulations. Semantics and the novel supervised cadre model make our system explainable. Future population health studies are easily performed and documented with provenance by specifying additional input and output KG cartridges.

cs.LG↗

arXiv2018

Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text

We present an end-to-end approach that takes unstructured textual input and generates structured output compliant with a given vocabulary. Inspired by recent successes in neural machine translation, we treat the triples within a given knowledge graph as an independent graph language and propose an encoder-decoder framework with an attention mechanism that leverages knowledge graph embeddings. Our model learns the mapping from natural language text to triple representation in the form of subject-predicate-object using the selected knowledge graph vocabulary. Experiments on three different data sets show that we achieve competitive F1-Measures over the baselines using our simple yet effective approach. A demo video is included.

cs.CL↗

arXiv2018

Knowledge Integration for Disease Characterization: A Breast Cancer Example

With the rapid advancements in cancer research, the information that is useful for characterizing disease, staging tumors, and creating treatment and survivorship plans has been changing at a pace that creates challenges when physicians try to remain current. One example involves increasing usage of biomarkers when characterizing the pathologic prognostic stage of a breast tumor. We present our semantic technology approach to support cancer characterization and demonstrate it in our end-to-end prototype system that collects the newest breast cancer staging criteria from authoritative oncology manuals to construct an ontology for breast cancer. Using a tool we developed that utilizes this ontology, physician-facing applications can be used to quickly stage a new patient to support identifying risks, treatment options, and monitoring plans based on authoritative and best practice guidelines. Physicians can also re-stage existing patients or patient populations, allowing them to find patients whose stage has changed in a given patient cohort. As new guidelines emerge, using our proposed mechanism, which is grounded by semantic technologies for ingesting new data from staging manuals, we have created an enriched cancer staging ontology that integrates relevant data from several sources with very little human intervention.

cs.AI↗

arXiv2018

Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion

In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding. It is a challenging task because many abbreviations are ambiguous especially for intensive care medicine texts, in which phrase abbreviations are frequently used. Besides the fact that there is no universal dictionary of clinical abbreviations and no universal rules for abbreviation writing, such texts are difficult to acquire, expensive to annotate and even sometimes, confusing to domain experts. This paper proposes a novel and effective approach - exploiting task-oriented resources to learn word embeddings for expanding abbreviations in clinical notes. We achieved 82.27% accuracy, close to expert human performance.

cs.CL↗

arXiv2017

Contextual Data Collection for Smart Cities

As part of Smart Cities initiatives, national, regional and local governments all over the globe are under the mandate of being more open regarding how they share their data. Under this mandate, many of these governments are publishing data under the umbrella of open government data, which includes measurement data from city-wide sensor networks. Furthermore, many of these data are published in so-called data portals as documents that may be spreadsheets, comma-separated value (CSV) data files, or plain documents in PDF or Word documents. The sharing of these documents may be a convenient way for the data provider to convey and publish data but it is not the ideal way for data consumers to reuse the data. For example, the problems of reusing the data may range from difficulty opening a document that is provided in any format that is not plain text, to the actual problem of understanding the meaning of each piece of knowledge inside of the document. Our proposal tackles those challenges by identifying metadata that has been regarded to be relevant for measurement data and providing a schema for this metadata. We further leverage the Human-Aware Sensor Network Ontology (HASNetO) to build an architecture for data collected in urban environments. We discuss the use of HASNetO and the supporting infrastructure to manage both data and metadata in support of the City of Fortaleza, a large metropolitan area in Brazil.

cs.AI↗

arXiv2017

Human-Aware Sensor Network Ontology: Semantic Support for Empirical Data Collection

Significant efforts have been made to understand and document knowledge related to scientific measurements. Many of those efforts resulted in one or more high-quality ontologies that describe some aspects of scientific measurements, but not in a comprehensive and coherently integrated manner. For instance, we note that many of these high-quality ontologies are not properly aligned, and more challenging, that they have different and often conflicting concepts and approaches for encoding knowledge about empirical measurements. As a result of this lack of an integrated view, it is often challenging for scientists to determine whether any two scientific measurements were taken in semantically compatible manners, thus making it difficult to decide whether measurements should be analyzed in combination or not. In this paper, we present the Human-Aware Sensor Network Ontology that is a comprehensive alignment and integration of a sensing infrastructure ontology and a provenance ontology. HASNetO has been under development for more than one year, and has been reviewed, shared and used by multiple scientific communities. The ontology has been in use to support the data management of a number of large-scale ecological monitoring activities (observations) and empirical experiments.

cs.AI↗

arXiv2017

From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards

In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values has challenges as the calculation may need to combine some aspects of quality while addressing different levels of abstraction. Knowledge graphs (KGs) have been used successfully to support flexible representation, which can support improved understanding and data analysis in similar settings. This paper presents an operational description for a city KG, an indicator ontology that support indicator discovery and data visualization and an application capable of performing metadata analysis to automatically build and display dashboards according to discovered indicators. We describe our implementation in an urban mobility setting.

cs.AI↗