Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation

Background: The use of knowledge models facilitates information retrieval, knowledge base development, and therefore supports new knowledge discovery that ultimately enables decision support applications. Most existing works have employed machine learning techniques to construct a knowledge base. However, they often suffer from low precision in extracting entity and relationships. In this paper, we described a data-driven sublanguage pattern mining method that can be used to create a knowledge model. We combined natural language processing (NLP) and semantic network analysis in our model generation pipeline. Methods: As a use case of our pipeline, we utilized data from an open source imaging case repository, Radiopaedia.org, to generate a knowledge model that represents the contents of medical imaging reports. We extracted entities and relationships using the Stanford part-of-speech parser and the “Subject:Relationship:Object” syntactic data schema. The identified noun phrases were tagged with the Unified Medical Language System (UMLS) semantic types. An evaluation was done on a dataset comprised of 83 image notes from four data sources. Results: A semantic type network was built based on the co-occurrence of 135 UMLS semantic types in 23,410 medical image reports. By regrouping the semantic types and generalizing the semantic network, we created a knowledge model that contains 14 semantic categories. Our knowledge model was able to cover 98% of the content in the evaluation corpus and revealed 97% of the relationships. Machine annotation achieved a precision of 87%, recall of 79%, and F-score of 82%. Conclusion: The results indicated that our pipeline was able to produce a comprehensive content-based knowledge model that could represent context from various sources in the same domain

The EAGLES/ISLE initiative for setting standards: the Computational Lexicon Working Group for Multilingual Lexicons

ISLE (International Standards for Language Engineering), a transatlantic standards oriented initiative under the Human Language Technology (HLT) programme, is a continuation of the long standing EAGLES (Expert Advisory Group for Language Engineering Standards) initiative, carried out by European and American groups within the EU-US International Research Co-operation, supported by NSF and EC. The objective is to support HLT R&D international and national projects, and HLT industry, by developing and promoting widely agreed and urgently demanded HLT standards and guidelines for infrastructural language resources, tools, and HLT products. ISLE targets the areas of multilingual computational lexicons (MCL), natural interaction and multimodality (NIMM), and evaluation. For MCL, ISLE is working to: extend EAGLES work on lexical semantics, necessary to establish inter-language links; design standards for multilingual lexicons; develop a prototype tool to implement lexicon guidelines; create EAGLES-conformant sample lexicons and tag corpora for validation purposes; develop standardised evaluation procedures for lexicons. For NIMM, a rapidly innovating domain urgently requiring early standardisation, ISLE work is targeted to develop guidelines for: creation of NIMM data resources; interpretative annotation of NIMM data, including spoken dialogue; annotation of discourse phenomena. For evaluation, ISLE is working on: quality models for machine translation systems; maintenance of previous guidelines - in an ISO based framework. We concentrate in the paper on the Computational Lexicon Working Group, describing in detail the proposals of guidelines for the "Multilingual ISLE Lexical Entry" (MILE). We highlight some methodological principles applied in previous EAGLES, and followed in defining MILE. We also provide a description of the EU SIMPLE semantic lexicons built on the basis of previous EAGLES recommendations. Their importance is given by the fact that these lexicons are now enlarged to real-size lexicons within National Projects in 8 EU countries, thus building a really large infrastructural platform of harmonised lexicons in Europe. We will stress the relevance of standardised language resources also for the humanities applications. Numerous theories, approaches, systems are taken into account in ISLE, as any recommendation for harmonisation must build on the major contemporary approaches. Results will be widely disseminated, after validation in collaboration with EU and US HLT R&D projects, and industry. EAGLES work towards de facto standards has already allowed the field of Language Resources to establish broad consensus on key issues for some well-established areas - and will allow similar consensus to be achieved for other important areas through the ISLE project - providing thus a key opportunity for further consolidation and a basis for technological advance. EAGLES previous results in many areas have in fact already become de facto widely adopted standards, and EAGLES itself is a well-known trademark and a point of reference for HLT projects.Hosted by the Scholarly Text and Imaging Service (SETIS), the University of Sydney Library, and the Research Institute for Humanities and Social Sciences (RIHSS), the University of Sydney

Ontology of core data mining entities

In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines themost essential datamining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend

Entity-Oriented Search

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms

An Ontology-based Approach to Web Site Design and Development

Building a data-intensive web site is a complex task. Ad-hoc rapid prototyping approaches easily lead to unsatisfactory results, e. g. poor maintainability and extensibility. The situation becomes even more difficult when customization issues arise and web sites need to present customized views to individual users. To address this problem, a number of model-based approaches have been proposed, which attempt to simplify the design and development of data-intensive web sites. However these approaches suffer a number of limitations, such as relatively little support for the composition of sophisticated user interfaces and the specification of presentation styles and little support for customization design. In this work we propose and implement an ontology-based approach, OntoWeaver, which provides comprehensive support for the design and development of data-intensive web sites. In particular, OntoWeaver provides a set of ontologies to represent all aspects of data-intensive web sites in a declarative and re-usable format. The declarative nature of the specification of web sites opens up a number of possibilities with respect to intelligent analysis and management. Moreover, OntoWeaver includes providing high level support for developing customized web sites. Finally, it offers a powerful tool suite to support the design and development of data-intensive web sites. In the course of this research, we have also extended OntoWeaver by addressing the issue of integrating web service technology into a high level web site design framework

CoMMA Corporate Memory Management through Agents Corporate Memory Management through Agents: The CoMMA project final report

This document is the final report of the CoMMA project. It gives an overview of the different search activities that have been achieved through the project. First, a description of the general requirements is proposed through the definition of two scenarios. Then it shows the different technical aspects of the projects and the solution that has been proposed and implemented

The Landscape of Ontology Reuse Approaches

Ontology reuse aims to foster interoperability and facilitate knowledge reuse. Several approaches are typically evaluated by ontology engineers when bootstrapping a new project. However, current practices are often motivated by subjective, case-by-case decisions, which hamper the definition of a recommended behaviour. In this chapter we argue that to date there are no effective solutions for supporting developers' decision-making process when deciding on an ontology reuse strategy. The objective is twofold: (i) to survey current approaches to ontology reuse, presenting motivations, strategies, benefits and limits, and (ii) to analyse two representative approaches and discuss their merits