Tailored Semantic Annotation for Semantic Search

Abstract

This paper presents a novel method for semantic annotation and search of a target corpus using several knowledge resources (KRs). This method relies on a formal statistical framework in which KR concepts and corpus documents are homogeneously represented using statistical language models. Under this framework, we can perform all the necessary operations for an efficient and effective semantic annotation of the corpus. Firstly, we propose a coarse tailoring of the KRs w.r.t the target corpus with the main goal of reducing the ambiguity of the annotations and their computational overhead. Then, we propose the generation of concept profiles, which allow measuring the semantic overlap of the KRs as well as performing a finer tailoring of them. Finally, we propose how to semantically represent documents and queries in terms of the KRs concepts and the statistical framework to perform semantic search. Experiments have been carried out with a corpus about web resources which includes several Life Sciences catalogues and Wikipedia pages related to web resources in general (e.g., databases, tools, services, etc). Results demonstrate that the proposed method is more effective and efficient than state-ofthe-art methods relying on either context-free annotation or keyword-based search.

Semantic Annotation of Unstructured Documents Using Concepts Similarity

“…Berlanga et al [26] propose a semantic annotation/query strategy for a corpus using several knowledge bases. This method is based on a statistical framework where the concepts of the knowledge bases and the corpus documents are homogeneously represented through statistical models of language.…”

A comprehensive framework for the semantic cache systems

“…On the other hand, the authors (Ristoski and Paulheim, 2016) investigated various approaches based on integrating semantic web with knowledge discovery and the data mining processes to show the importance of linked open data for the recommender systems. A method based on many knowledge resources (KRs) for semantic search and annotation in target corpus (Berlanga et al, 2015). The method depends on a statistical framework where corpus documents and the KR concepts are uniformly represented using models of statistical language.…”

Biotea-Biolinks: A semantic infrastructure for exploring and analyzing scientific publications

“…As full-text become available, approaches aiming to provide a list of similar articles should get ready to take advantage of it. Furthermore, such approaches should also make use of entity recognition supported by efforts such as Whatizit [74,75], CMA [90,91], MetaMap [102] and the NCBO Annotator [76].…”

“…A summary of the TREC-05 topics with more than 100 relevant and partially relevant articles is presented in Table 6. In Biolinks we use a semantic annotator supporting UMLS concepts, the Concept Mapping Annotator (CMA) [90,91]. CMA follows a mapping process: text chunks in the text are judged relevant if a corresponding CUI exists in UMLS.…”

“…Despite efforts to automatically recognize ontological entities in scientific publications, e.g., Whatizit [74,75], Concept Mapping Annotator (CMA) [90,91], MetaMap [102] and the National Center for Biomedical Ontology (NCBO) Annotator [76], only MeSH terms are currently used to calculate similarity between articles in PubMed. PMRA could be adapted to work with semantic annotations as those provided by Biotea.…”