Qualitative Effects of Knowledge Rules in Probabilistic Data Integration

One of the problems in data integration is data overlap: the fact that different data sources have data on the same real world entities. Much development time in data integration projects is devoted to entity resolution. Often advanced similarity measurement techniques are used to remove semantic duplicates from the integration result or solve other semantic conflicts, but it proofs impossible to get rid of all semantic problems in data integration. An often-used rule of thumb states that about 90% of the development effort is devoted to solving the remaining 10% hard cases. In an attempt to significantly decrease human effort at data integration time, we have proposed an approach that stores any remaining semantic uncertainty and conflicts in a probabilistic database enabling it to already be meaningfully used. The main development effort in our approach is devoted to defining and tuning knowledge rules and thresholds. Rules and thresholds directly impact the size and quality of the integration result. We measure integration quality indirectly by measuring the quality of answers to queries on the integrated data set in an information retrieval-like way. The main contribution of this report is an experimental investigation of the effects and sensitivity of rule definition and threshold tuning on the integration quality. This proves that our approach indeed reduces development effort — and not merely shifts the effort to rule definition and threshold tuning — by showing that setting rough safe thresholds and defining only a few rules suffices to produce a ‘good enough’ integration that can be meaningfully used

Using Search Engine Technology to Improve Library Catalogs

This chapter outlines how search engine technology can be used in online public access library catalogs (OPACs) to help improve users’ experiences, to identify users’ intentions, and to indicate how it can be applied in the library context, along with how sophisticated ranking criteria can be applied to the online library catalog. A review of the literature and current OPAC developments form the basis of recommendations on how to improve OPACs. Findings were that the major shortcomings of current OPACs are that they are not sufficiently user-centered and that their results presentations lack sophistication. Further, these shortcomings are not addressed in current 2.0 developments. It is argued that OPAC development should be made search-centered before additional features are applied. While the recommendations on ranking functionality and the use of user intentions are only conceptual and not yet applied to a library catalogue, practitioners will find recommendations for developing better OPACs in this chapter. In short, readers will find a systematic view on how the search engines’ strengths can be applied to improving libraries’ online catalogs

A New Approach to Information Extraction in User-Centric E-Recruitment Systems

In modern society, people are heavily reliant on information available online through various channels, such as websites, social media, and web portals. Examples include searching for product prices, news, weather, and jobs. This paper focuses on an area of information extraction in e-recruitment, or job searching, which is increasingly used by a large population of users in across the world. Given the enormous volume of information related to job descriptions and users’ profiles, it is complicated to appropriately match a user’s profile with a job description, and vice versa. Existing information extraction techniques are unable to extract contextual entities. Thus, they fall short of extracting domain-specific information entities and consequently affect the matching of the user profile with the job description. The work presented in this paper aims to extract entities from job descriptions using a domain-specific dictionary. The extracted information entities are enriched with knowledge using Linked Open Data. Furthermore, job context information is expanded using a job description domain ontology based on the contextual and knowledge information. The proposed approach appropriately matches users’ profiles/queries and job descriptions. The proposed approach is tested using various experiments on data from real life jobs’ portals. The results show that the proposed approach enriches extracted data from job descriptions, and can help users to find more relevant jobs

Automatic classification of registered clinical trials towards the Global Burden of Diseases taxonomy of diseases and injuries

Includes details on the implementation of MetaMap and IntraMap, prioritization rules, the test set of clinical trials and the classification of the external test set according to the 171 GBD categories. Dataset S1: Expert-based enrichment database for the classification according to the 28 GBD categories. Manual classification of 503 UMLS concepts that could not be mapped to any of the 28 GBD categories. Dataset S2: Expert-based enrichment database for the classification according to the 171 GBD categories. Manual classification of 655 UMLS concepts that could not be mapped to any of the 171 GBD categories, among which 108 could be projected to candidate GBD categories. Table S1: Excluded residual GBD categories for the grouping of the GBD cause list in 171 GBD categories. A grouping of 193 GBD categories was defined during the GBD 2010 study to inform policy makers about the main health problems per country. From these 193 GBD categories, we excluded the 22 residual categories listed in the Table. We developed a classifier for the remaining 171 GBD categories. Among these residual categories, the unique excluded categories in the grouping of 28 GBD categories were “Other infectious diseases” and “Other endocrine, nutritional, blood, and immune disorders”. Table S2: Per-category evaluation of performance of the classifier for the 171 GBD categories plus the “No GBD” category. Number of trials per GBD category from the test set of 2,763 clinical trials. Sensitivities, specificities (in %) and likelihood ratios for each of the 171 GBD categories plus the “No GBD” category for the classifier using the Word Sense Disambiguation server, the expert-based enrichment database and the priority to the health condition field. Table S3: Performance of the 8 versions of the classifier for the 171 GBD categories. Exact-matching and weighted averaged sensitivities and specificities for 8 versions of the classifier for the 171 GBD categories. Exact-matching corresponds to the proportion (in %) of trials for which the automatic GBD classification is correct. Exact-matching was estimated over all trials (N = 2,763), trials concerning a unique GBD category (N = 2,092), trials concerning 2 or more GBD categories (N = 187), and trials not relevant for the GBD (N = 484). The weighted averaged sensitivity and specificity corresponds to the weighted average across GBD categories of the sensitivities and specificities for each GBD category plus the “No GBD” category (in %). The 8 versions correspond to the combinations of the use or not of the Word Sense Disambiguation server during the text annotation, the expert-based enrichment database, and the priority to the health condition field as a prioritization rule. Table S4: Per-category evaluation of the performance of the baseline for the 28 GBD categories plus the “No GBD” category. Number of trials per GBD category from the test set of 2,763 clinical trials. Sensitivities and specificities (in %) of the 28 GBD categories plus the “No GBD” category for the classification of clinical trial records towards GBD categories without using the UMLS knowledge source but based on the recognition in free text of the names of diseases defining in each GBD category only. For the baseline a clinical trial records was classified with a GBD category if at least one of the 291 disease names from the GBD cause list defining that GBD category appeared verbatim in the condition field, the public or scientific titles, separately, or in at least one of these three text fields. (DOCX 84 kb