A Relation Extraction Approach for Clinical Decision Support

In this paper, we investigate how semantic relations between concepts extracted from medical documents can be employed to improve the retrieval of medical literature. Semantic relations explicitly represent relatedness between concepts and carry high informative power that can be leveraged to improve the effectiveness of retrieval functionalities of clinical decision support systems. We present preliminary results and show how relations are able to provide a sizable increase of the precision for several topics, albeit having no impact on others. We then discuss some future directions to minimize the impact of negative results while maximizing the impact of good results.Comment: 4 pages, 1 figure, DTMBio-KMH 2018, in conjunction with ACM 27th Conference on Information and Knowledge Management (CIKM), October 22-26 2018, Lingotto, Turin, Ital

A Comparison of Automated Journal Recommender Systems

Choosing the right journal for an article can be a challenge. Automated manuscript matching can help authors with the decision by recommending suitable journals based on user-defined criteria. Several approaches for efficient matching have been proposed in the research literature. However, only a few actual recommender systems are available for end users. In this paper, we present an overview of available services and compare their key characteristics such as input values, functionalities, and privacy. We conduct a quantitative analysis of their recommendation results: (a) examining the overlap in the results and pointing out the similarities among them; (b) evaluating their quality with a comparison of their accuracy. Due to the providers’ lack of transparency about the used technologies, the results cannot be easily interpreted. This highlights the need for openness about the used algorithms and data sets

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

Knowledge Enhanced Representations to Reduce the Semantic Gap in Clinical Decision Support

The semantic gap between queries and documents is a longstanding problem in Information Retrieval (IR), and it poses a critical challenge for medical IR due to the large presence in the medical language of synonymous and polysemous words, along with context-specific expressions. Two main lines of work have emerged in the past years to tackle this issue: (i) the use of external knowledge resources to enhance query and document bag-of-words representations; and (ii) the use of semantic models, based on the distributional hypothesis, which perform matching on latent representations of documents and queries. The presented research investigates the use of external knowledge resources in both lines \u2013 with a focus on knowledge-enhanced unsupervised neural latent representations and their analysis in terms of effectiveness and semantic representativeness

Case-Based Retrieval Using Document-Level Semantic Networks

We propose a research that aims at improving the effectiveness of case-based retrieval systems through the use of automatically created document-level semantic networks. The proposed research leverages the recent advancements in information extraction and relational learning to revisit and advance the core ideas of concept-centered hypertext models. The automatic extraction of semantic relations from documents --- and their centrality in the creation and exploitation of the documents' semantic networks --- represents our attempt to go one step further than previous approaches

Developing unsupervised knowledge-enhanced models to reduce the semantic Gap in information retrieval

In this thesis we tackle the semantic gap, a long-standing problem in Information Retrieval(IR). The semantic gap can be described as the mismatch between users’ queries and the way retrieval models answer to such queries. Two main lines of work have emerged over the years to bridge the semantic gap: (i) the use of external knowledge resources to enhance the bag-of-words representations used by lexical models, and (ii) the use of semantic models to perform matching between the latent representations of queries and documents. To deal with this issue, we first perform an in-depth evaluation of lexical and semantic models through different analyses. The objective of this evaluation is to understand what features lexical and semantic models share, if their signals are complementary, and how they can be combined to effectively address the semantic gap. In particular, the evaluation focuses on (semantic) neural models and their critical aspects. Then, we build on the insights of this evaluation to develop lexical and semantic models addressing the semantic gap. Specifically, we develop unsupervised models that integrate knowledge from external resources, and we evaluate them for the medical domain – a domain with a high social value, where the semantic gap is prominent, and the large presence of authoritative knowledge resources allows us to explore effective ways to leverage external knowledge to address the semantic gap. For lexical models, we propose and evaluate several knowledge-based query expansion and reduction techniques. These query reformulations are used to increase the probability of retrieving relevant documents by adding to or removing from the original query highly specific terms. Regarding semantic models, we first analyze the limitations of the knowledge-enhanced neural models presented in the literature. Then, to overcome these limitations, we propose SAFIR, an unsupervised knowledge-enhanced neural framework for IR. The representations learned within this framework are optimized for IR and encode linguistic features that are relevant to address the semantic gap