Semantic Search Applications Research Articles

Reproductive Health and Semantics: Representations of Abortion in Semantic Models and Search Applications

Health care-related queries are a common source of information seeking online, and, in light of the June 2022 U.S. Supreme Court decision to overturn Roe v. Wade, information concerning reproductive health in the United States is now of particular interest. Increasingly, search applications turn to semantic models to assist in responding to user queries, with the knowledgebase Wikidata playing a prominent role. However, Wikidata’s representation of topics around reproductive health care, as well as the implications for user searches, are currently unclear. To further examine this, the present study analyzed Wikidata’s treatment of abortion, compared this to representations in three medical domain models, and tested web searches to gauge Wikidata’s influence on search results. Results show that, as a semantic model, Wikidata attempts to represent topics around abortion in a manner that is at once both multi-perspective and simplified, leading to logical inconsistencies when compared to domain models. Wikidata’s influence on semantically supported web search is more difficult to ascertain as search engines’ treatment of abortion appears purposely exceptional, though a strong influence from Wikipedia was noted. Findings from this study demonstrate the importance of how semantic models address the medical and health domain, and suggest the need for greater transparency in how health care information is treated within web search applications.

An Ontological Approach for Creating a Brassware Craft Knowledge Base

Terengganu brassware is a local craft product with high artistic value and traditional craftsmanship that involves a lot of creativity and elaborate artistry. This cultural heritage is in danger of disappearing because the current younger generation lacks interest, creativity and artistic skills to continue the craftsmanship. Therefore, gathering detailed knowledge of the craft from ageing practitioners can preserve the heritage. This article reports the study on brassware intangible heritage knowledge base development using an ontological approach. The ontology modelling used Ontology Development 101. Content validation result by experts found that the brassware ontology is complete, consistent, concise, precise and clear. Evaluation results show that the experts agreed on content representation (mean ≥3.80), and the ontology meets all the quality criteria (mean ≥4.03). The brassware craft ontology contributes to the intangible heritage preservation effort for one of the almost extinct local culture and practices. This intangible heritage knowledge base can be applied for the development of semantic search, mobile and gaming applications to attract and disseminate heritage information to young people. Additionally, the ontological design can serve as a guide for the design and development of a knowledge base for other types of heritage.

Identifying Semantics in Clinical Reports Using Neural Machine Translation

Clinical documents are vital resources for radiologists when they have to consult or refer while studying similar cases. In large healthcare facilities where millions of reports are generated, searching for relevant documents is quite challenging. With abundant interchangeable words in clinical domain, understanding the semantics of the words in the clinical documents is vital to improve the search results. This paper details an end to end semantic search application to address the large scale information retrieval problem of clinical reports. The paper specifically focuses on the challenge of identifying semantics in the clinical reports to facilitate search at semantic level. The semantic search works by mapping the documents into the concept space and the search is performed in the concept space. A unique approach of framing the concept mapping problem as a language translation problem is proposed in this paper. The concept mapper is modelled using the Neural machine translation model (NMT) based on encoder-decoder with attention architecture. The regular expression based concept mapper takes approximately 3 seconds to extract UMLS concepts from a single document, where as the trained NMT does the same in approximately 30 milliseconds. NMT based model further enables incorporation of negation detection to identify whether a concept is negated or not, facilitating search for negated queries.

Entity set expansion with semantic features of knowledge graphs

A large-scale knowledge graph contains a huge number of path-based semantic features, which provides a flexible mechanism to assign and expand semantics/attributes to entities. A particular set of these semantic features can be exploited on the fly, to support particular entity-oriented semantic search tasks. In this paper, we use entity set expansion as an example to show how these path-based semantic features can be effectively utilized in a semantic search application. The entity set expansion problem is to expand a small set of seed entities to a more complete set of similar entities. Traditionally, people solve this problem by exploiting the statistical co-occurrence of entities in the web pages, where semantic correlation among the seed entities is not well exploited. We propose to address the entity set expansion problem using the path-based semantic features of knowledge graphs. Our method first discovers relevant semantic features of the seed entities, which can be treated as the common aspects of these seed entities, and then retrieves relevant entities based on the discovered semantic features. Probabilistic models are proposed to rank entities, as well as semantic features, by handling the incompleteness of knowledge graphs. Extensive experiments on a public knowledge graph (i.e., DBpedia V3.9) and three public test collections (i.e., CLEF-QALD 2–4, SemSearch-LS 2011, and INEX-XER 2009) show that our method significantly outperforms the state-of-the-art techniques.

GEOCAB Portal: A gateway for discovering and accessing capacity building resources in Earth Observation

The discovery of and access to capacity building resources are often essential to conduct environmental projects based on Earth Observation (EO) resources, whether they are Earth Observation products, methodological tools, techniques, organizations that impart training in these techniques or even projects that have shown practical achievements. Recognizing this opportunity and need, the European Commission through two FP7 projects jointly with the Group on Earth Observations (GEO) teamed up with the Committee on Earth observation Satellites (CEOS). The Global Earth Observation CApacity Building (GEOCAB) portal aims at compiling all current capacity building efforts on the use of EO data for societal benefits into an easily updateable and user-friendly portal. GEOCAB offers a faceted search to improve user discovery experience with a fully interactive world map with all inventoried projects and activities. This paper focuses on the conceptual framework used to implement the underlying platform. An ISO19115 metadata model associated with a terminological repository are the core elements that provide a semantic search application and an interoperable discovery service. The organization and the contribution of different user communities to ensure the management and the update of the content of GEOCAB are addressed.

Event-based text mining for biology and functional genomics.

The assessment of genome function requires a mapping between genome-derived entities and biochemical reactions, and the biomedical literature represents a rich source of information about reactions between biological components. However, the increasingly rapid growth in the volume of literature provides both a challenge and an opportunity for researchers to isolate information about reactions of interest in a timely and efficient manner. In response, recent text mining research in the biology domain has been largely focused on the identification and extraction of ‘events’, i.e. categorised, structured representations of relationships between biochemical entities, from the literature. Functional genomics analyses necessarily encompass events as so defined. Automatic event extraction systems facilitate the development of sophisticated semantic search applications, allowing researchers to formulate structured queries over extracted events, so as to specify the exact types of reactions to be retrieved. This article provides an overview of recent research into event extraction. We cover annotated corpora on which systems are trained, systems that achieve state-of-the-art performance and details of the community shared tasks that have been instrumental in increasing the quality, coverage and scalability of recent systems. Finally, several concrete applications of event extraction are covered, together with emerging directions of research.

Integrating Digital Pens in Breast Imaging for Instant Knowledge Acquisition

Future radiology practices assume that the radiology reports should be uniform, comprehensive, and easily managed. This means that reports must be readable to humans and machines alike. In order to improve reporting practices in breast imaging, we allow the radiologist to write structured reports with a special pen on paper with an invisible dot pattern. In this way, we provide a knowledge‐acquisition system for printed mammography patient forms for the combined work with printed and digital documents. In this domain, printed documents cannot be easily replaced by computer systems because they contain free‐form sketches and textual annotations, and the acceptance of traditional PC reporting tools is rather low among the doctors. This is due to the fact that current electronic reporting systems significantly add to the amount of time it takes to complete the reports. We describe our real‐time digital paper application and focus on the use case study of our deployed application. We think that our results motivate the design and implementation of intuitive pen‐based user interfaces for the medical reporting process and similar knowledge work domains. Our system imposes only minimal overhead on traditional form‐filling processes and provides for a direct, ontology‐based structuring of the user input for semantic search and retrieval applications, as well as other applied artificial intelligence scenarios, which involve manual form‐based data acquisition.

Integrating Digital Pens in Breast Imaging for Instant Knowledge Acquisition

Future radiology practices assume that the radiology reports should be uniform, comprehensive, and easily managed. This means that reports must be “readable” to humans and machines alike. In order to improve reporting practices in breast imaging, we allow the radiologist to write structured reports with a special pen on paper with an invisible dot pattern. In this way, we provide a knowledge acquisition system for printed mammography patient forms for the combined work with printed and digital documents. In this domain, printed documents cannot be easily replaced by computer systems because they contain free-form sketches and textual annotations, and the acceptance of traditional PC reporting tools is rather low among the doctors. This is due to the fact that current electronic reporting systems significantly add to the amount of time it takes to complete the reports. We describe our real-time digital paper application and focus on the use case study of our deployed application. We think that our results motivate the design and implementation of intuitive pen-based user interfaces for the medical reporting process and similar knowledge work domains. Our system imposes only minimal overhead on traditional form-filling processes and provides for a direct, ontology-based structuring of the user input for semantic search and retrieval applications, as well as other applied artificial intelligence scenarios which involve manual form-based data acquisition.

Rational Research model for ranking semantic entities

Ranking plays important role in contemporary information search and retrieval systems. Among existing ranking algorithms, link analysis based algorithms have been proved to be effective for ranking documents retrieved from large-scale text repositories such as the current Web. Recent developments in semantic Web raise considerable interest in designing new ranking paradigms for various semantic search applications. While ranking methods in this context exist, they have not gained much popularity. In this article we introduce the idea of the “Rational Research” model which reflects search behaviour of a “rational” researcher in a scientific research environment, and propose the RareRank algorithm for ranking entities in semantic search systems, in particular, we focus on elaborating the rationale and implementation of the algorithm. Experiments are performed using the RareRank algorithm and the results are evaluated by domain experts using popular ranking performance measures. A comparison study with existing link-based ranking algorithms reveals the benefits of the proposed method.

DOORS to the Semantic Web and Grid With a PORTAL for Biomedical Computing

The semantic web remains in the early stages of development. It has not yet achieved the goals envisioned by its founders as a pervasive web of distributed knowledge and intelligence. Success will be attained when a dynamic synergism can be created between people and a sufficient number of infrastructure systems and tools for the semantic web in analogy with those for the original web. The domain name system (DNS), web browsers, and the benefits of publishing web pages motivated many people to register domain names and publish web sites on the original web. An analogous resource label system, semantic search applications, and the benefits of collaborative semantic networks will motivate people to register resource labels and publish resource descriptions on the semantic web. The Domain Ontology Oriented Resource System (DOORS) and Problem Oriented Registry of Tags and Labels (PORTAL) are proposed as infrastructure systems for resource metadata within a paradigm that can serve as a bridge between the original web and the semantic web. The Internet Registry Information Service (IRIS) registers [corrected] domain names while DNS publishes domain addresses with mapping of names to addresses for the original web. Analogously, PORTAL registers resource labels and tags while DOORS publishes resource locations and descriptions with mapping of labels to locations for the semantic web. BioPORT is proposed as a prototype PORTAL registry specific for the problem domain of biomedical computing.

Assessing thesaurus-based annotations for semantic search applications

Statistical methods for automated document indexing are becoming an alternative to the manual assignment of keywords. We argue that the quality of the thesaurus used as a basis for indexing in regard to its ability to adequately cover the contents to be indexed and as a basis for the specific indexing method used is of crucial importance in automatic indexing. We present an interactive tool for thesaurus evaluation that is based on a combination of statistical measures and appropriate visualisation techniques that supports the detection of potential problems in a thesaurus. We describe the methods used and show that the tool supports the detection and correction of errors, leading to a better indexing result.