Mihaela A. Bornea

Building an efficient RDF store over a relational database

Efficient storage and querying of RDF data is of increasing importance, due to the increased popularity and widespread acceptance of RDF on the web and in the enterprise. In this paper, we describe a novel storage and query mechanism for RDF which works on top of existing relational representations. Reliance on relational representations of RDF means that one can take advantage of 35+ years of research on efficient storage and querying, industrial-strength transaction support, locking, security, etc. However, there are significant challenges in storing RDF in relational, which include data sparsity and schema variability. We describe novel mechanisms to shred RDF into relational, and novel query translation techniques to maximize the advantages of this shredded representation. We show that these mechanisms result in consistently good performance across multiple RDF benchmarks, even when compared with current state-of-the-art stores. This work provides the basis for RDF support in DB2 v.10.1.

Problem-oriented patient record summary: An early report on a Watson application

As the use of Electronic Medical Records (EMRs) becomes widespread, the amount of data in an EMR becomes a challenge for its comprehension. We developed problem-oriented EMR summarization to address this issue, as a part of a larger effort of adapting IBM Watson to the medical domain. The problem-orientation refers to the central role of a patients medical problems in the summary. The summarization uses a generated problem list, relates these generated medical problems to relevant clinical data, and organizes the clinical data in a medically meaningful manner. Watson analytics are used for creating the summarization. This is a step in building the next generation EMR, one that is based not on just keeping record but instead on a conceptual understanding of medicine, thereby crossing the threshold from record storage to an intelligent entity for clinical decision making.

Stacking With Auxiliary Features for Entity Linking in the Medical Domain.

Linking spans of natural language text to concepts in a structured source is an important task for many problems. It allows intelligent systems to leverage rich knowledge available in those sources (such as concept properties and relations) to enhance the semantics of the mentions of these concepts in text. In the medical domain, it is common to link text spans to medical concepts in large, curated knowledge repositories such as the Unified Medical Language System. Different approaches have different strengths: some are precision-oriented, some recalloriented; some better at considering context but more prone to hallucination. The variety of techniques suggests that ensembling could outperform component technologies at this task. In this paper, we describe our process for building a Stacking ensemble using additional, auxiliary features for Entity Linking in the medical domain. Our best model beats several baselines and produces state-of-the-art results on several medical datasets.

An Executable Specification for SPARQL

Linked Data on the web consists of over 1000 datasets from a variety of domains. They are queried with the SPARQL query language. There exist many implementations of SPARQL, and this rich ecosystem has demanded a precise specification and compliance tests. However, the SPARQL specification has grown in complexity, and it is increasingly difficult for developers to validate their implementations. In this paper, we present a declarative specification for SPARQL, based on relational logic. It describes SPARQL with just a few operators, and is executable: queries written in it can be directly executed against real datasets.

An Offline Optimal SPARQL Query Planning Approach to Evaluate Online Heuristic Planners

In graph databases, a given graph query can be executed in a large variety of semantically equivalent ways. Each such execution plan produces the same results, but at different computation costs. The query planning problem consists of finding, for a given query, an execution plan with the minimum cost. The traditional greedy or heuristic cost-based approaches addressing the query planning problem do not guarantee by design the optimality of the chosen execution plan. In this paper, we present a principled framework to solve the query planning problem by casting it into an Integer Linear Programming problem, and discuss its applications to testing and improving heuristic-based query planners.