WWW 2017 Practice Talks

Date: March 17, 2017

Speakers: Simon Walk and Maulik Kamdar, BMIR, Stanford University

Talk 1: How Users Explore Ontologies on the Web: A Study of NCBO's BioPortal Usage Logs (paper)

Abstract

Ontologies in the biomedical domain are numerous, highly specialized and very expensive to develop. Thus, a crucial prerequisite for ontology adoption and reuse is effective support for exploring and finding existing ontologies. Towards that goal, the National Center for Biomedical Ontology (NCBO) has developed BioPortal-an online repository designed to support users in exploring and finding more than 500 existing biomedical ontologies. In 2016, BioPortal represents one of the largest portals for exploration of semantic biomedical vocabularies and terminologies, which is used by many researchers and practitioners. While usage of this portal is high, we know very little about how exactly users search and explore ontologies and what kind of usage patterns or user groups exist in the first place. Deeper insights into user behavior on such portals can provide valuable information to devise strategies for a better support of users in exploring and finding existing ontologies, and thereby enable better ontology reuse.

To that end, we study and group users according to their browsing behavior on BioPortal using data mining techniques. Additionally, we use the obtained groups to characterize and compare exploration strategies across ontologies. In particular, we were able to identify seven distinct browsing-behavior types, which all make use of different functionality provided by BioPortal. For example, Search Explorers make extensive use of the search functionality while Ontology Tree Explorers mainly rely on the class hierarchy to explore ontologies. Further, we show that specific characteristics of ontologies influence the way users explore and interact with the website. Our results may guide the development of more user-oriented systems for ontology exploration on the Web.

Talk 2: PhLeGrA: Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data (paper)

Abstract

Integrated approaches for pharmacology are required for the mechanism-based predictions of adverse drug reactions that manifest due to concomitant intake of multiple drugs. These approaches require the integration and analysis of biomedical data and knowledge from multiple, heterogeneous sources with varying schemas, entity notations, and formats. To tackle these integrative challenges, the Semantic Web community has published and linked several datasets in the Life Sciences Linked Open Data (LSLOD) cloud using established W3C standards. We present the PhLeGrA platform for Linked Graph Analytics in Pharmacology in this paper. Through query federation, we integrate four sources from the LSLOD cloud and extract a drug-reaction network, composed of distinct entities. We represent this graph as a hidden conditional random field (HCRF), a discriminative latent variable model that is used for structured output predictions. We calculate the underlying probability distributions in the drug-reaction HCRF using the datasets from the U.S. Food and Drug Administration's Adverse Event Reporting System. We predict the occurrence of 146 adverse reactions due to multiple drug intake with an AUROC statistic greater than 0.75. The PhLeGrA platform can be extended to incorporate other sources published using Semantic Web technologies, as well as to discover other types of pharmacological associations.