An empirical meta-analysis of the life sciences linked open data on the web

While the biomedical community has published several “open data” sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from multiple sources. To tackle these challenges, the community has experimented with Semantic Web and linked data technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from more than 80 biomedical linked open data sources into an LSLOD schema graph and conduct an empirical meta-analysis to evaluate the extent of semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective. We envision that the LSLOD schema graph and the findings from this research will aid researchers who wish to query and integrate data and knowledge from multiple biomedical sources simultaneously on the Web.

An Ontology Based Information Retrieval System

Ontology provide a structured way of describing knowledge. Ontology's are usually repositories of concepts and relations between them, so using them in information retrieval seems to be a reasonable goal. The main objective in this report is to provide efficient means to move from keyword-based to concept-based information retrieval utilizing ontology's for conceptual definitions [1]. In this paper, we present the skeleton of such an IR system which works on a collection of domain specific documents and exploits the use of a domain specific ontology to improve the overall number of relevant documents retrieved. In this system, a user enters a query from which the meaningful concepts are extracted; using these concepts and domain ontology, query expansion is performed. We propose a system that matches the query terms in the ontology/schema graph and exploits the surrounding knowledge to derive an enhanced query. The enhanced query is given to the underlying basic keyword search system LUCENE [2]. In this approach we try to make use of more ontological Knowledge than IS-A and HAS-A relationships and synonyms for information retrieval.

Summarizing Personal Dataspace Based on User Interests

A personal dataspace management system (PDSMS) is a platform to manage personal data with various data types. Facing huge volume of heterogeneous personal data and complex relationships between them, it is better for users to start with a simplified, easy-to-read schema and then explore in depth only the relevant schema elements during formulating queries. Existing approaches of database schema summarization neglect user interests, which is very important in a personal dataspace. We propose a framework for building a concise resource summary based on user interests automatically in PDSMS. Our method builds the initial summary by partitioning schema graph according to its linkage information and selecting representative elements based on a novel measure on schema element typicality. Then, user interested degree for schema node is introduced to measure user interests and the initial summary is refined according to user interests. Finally, we evaluate the quality of our resource summary through a comprehensive set of experiments, and results indicate that summaries generated by our system are more effective on reducing user efforts required in formulating queries.