Ontology-based Query Expansion Research Articles

SMFM-based analogy retrieval tool for the conceptual design of innovative products

Design-by-Analogy (DbA) is a powerful approach to innovative conceptual design. Cross-domain analogies are the stimulated sources of inspiration for generating new concepts. Therefore, how to efficiently retrieve them becomes an essential issue to be solved. This paper proposes a tool based on a structure-mapping function model (SMFM) for analogy retrieval. Inspired by the structure-mapping theory, SMFM considers two in-depth features: functional relation and causal relation between functions, which are expressed by function and meta-function concepts, respectively. SMFM serves to capture design knowledge from instances (i.e., engineering or biological systems) to establish a case database. The SMFM ontology is constructed for linking knowledge representation and analogy retrieval. Its meta-function, function, and flow terms are used to index the modeled instances so as to establish an index database. Analogy retrieval is realized by the ontology-based query expansion, vector space model and weighted cosine similarity. By inputting function or meta-function queries, we retrieve the required cross-domain analogies, whose design knowledge is displayed by function models to inspire target functions or meta-functions realization. Finally, the tool is evaluated for its effectiveness in cross-domain analogies retrieval. An application example illustrates its practicality in aiding innovative design. Additionally, an experiment was conducted to test the tool. The results indicate that our tool can assist users (e.g., engineering designers) to more quickly generate new schemes to realize target meta-functions and the quality of schemes is higher.

Exploring noise control strategies for UMLS-based query expansion in health and biomedical information retrieval

In the past decades, ontology-based query expansion has been studied to improve health and biomedical information retrieval by many researchers, but the results of previous works are inconsistent. Query expansion with domain ontologies could introduce noise that degrades the retrieval performance, therefore noise control is the key to its success. In this paper, we explore three noise control strategies for UMLS-based query expansion. The first strategy is the adoption of a word-phrase hybrid retrieval model, and the other two strategies explored are expansion term weighting and term filtering. All the three strategies are implemented based on the Indri search engine and evaluated on two standard datasets, OHSUMED and TREC Genomic Track 2006. The experimental results indicate that the word-phrase hybrid retrieval model is superior to the word-based model and the pure phrase-based model, and beneficial to not only baseline retrieval but also query expansion. Expansion term weighting is an effective strategy to suppress term noise and improve retrieval performance. And expansion term filtering can also give some positive effects in most cases but is not as effective as the other two strategies. By combining the three strategies together, the best retrieval performances can be achieved on both datasets.

MOLGENIS/connect: a system for semi-automatic integration of heterogeneous phenotype data with applications in biobanks.

Motivation: While the size and number of biobanks, patient registries and other data collections are increasing, biomedical researchers still often need to pool data for statistical power, a task that requires time-intensive retrospective integration.Results: To address this challenge, we developed MOLGENIS/connect, a semi-automatic system to find, match and pool data from different sources. The system shortlists relevant source attributes from thousands of candidates using ontology-based query expansion to overcome variations in terminology. Then it generates algorithms that transform source attributes to a common target DataSchema. These include unit conversion, categorical value matching and complex conversion patterns (e.g. calculation of BMI). In comparison to human-experts, MOLGENIS/connect was able to auto-generate 27% of the algorithms perfectly, with an additional 46% needing only minor editing, representing a reduction in the human effort and expertise needed to pool data.Availability and Implementation: Source code, binaries and documentation are available as open-source under LGPLv3 from http://github.com/molgenis/molgenis and www.molgenis.org/connect.Contact: m.a.swertz@rug.nlSupplementary information: Supplementary data are available at Bioinformatics online.

Ontology-based Query Expansion for Arabic Text Retrieval

The semantic resources are important parts in the Information Retrieval (IR) such as search engines, Question Answering (QA), etc., these resources should be available, readable and understandable. In semantic web, the ontology plays a central role for the information retrieval, which use to retrieves more relevant information from unstructured information. This paper presents a semantic-based retrieval system for the Arabic text, which expands the input query semantically using Arabic domain ontology. In the proposed approach, the search engine index is represented using Vector Space Model (VSM), and the Arabic's place nouns domain ontology has been used which constructed and implemented using Web Ontology Language (OWL) from Arabic corpus. The proposed approach has been experimented on the Arabic Quran corpus, and the experiments show that the approach outperforms in terms of both precision and recall the traditional keyword- based methods.

An Ontology-Based Semantic Question Complexity Model and Its Applications in Neuroinformatics

An Ontology-Based Semantic Question Complexity Model and Its Applications in Neuroinformatics

An Extended Belief Network Approach for Keyword-Based Search over PLM Databases

Relational databases in PLM (product lifecycle management) system are commonly searched using structured query languages. The user needs to know the data schema to be able to ask suitable queries. In contrast, the IR (information retrieval) community has popularized keyword-based search that allows users to use keywords to access unstructured data. In this paper, we integrate IR techniques with structure data search and present an approach for querying PLM databases using keywords. This approach has two significant novel features: an ontology-based query expansion and dealing with semantic uncertainty of keywords based on extended belief network. We applied this approach to a practical PLM database to verify the usefulness of the proposed method.

An empirical analysis of ontology-based query expansion for learning resource searches using MERLOT and the Gene ontology

This paper proposes an expansion of queries based on formal domain ontologies in the context of the search for learning resources in repositories. The expansion process uses the relation types that are represented in these models; common ontological relations, and ontological relations specific to domain and traditional terminology relations, typical of thesauri. The tests were conducted using Gene ontology as the knowledge base and MERLOT is used as the test repository. The results of this study case indicate that, at similar levels of precision, expanded queries improve levels of novelty and coverage compared to the original query (without expansion), i.e. expanded queries allow the user to retrieve relevant objects, which might not be obtained without expansion.

A practical ontology query expansion algorithm for semantic-aware learning objects retrieval

Following the rapid development of Internet, particularly web page interaction technology, distant e-learning has become increasingly realistic and popular. To solve the problems associated with sharing and reusing teaching materials in different e-learning systems, several standard formats, including SCORM, IMS, LOM, and AICC, etc., recently have been proposed by several different international organizations. SCORM LOM, namely learning object metadata, facilitates the indexing and searching of learning objects in a learning object repository through extended sharing and searching features. However, LOM suffers a weakness in terms of semantic-awareness capability. Most information retrieval systems assume that users have cognitive ability regarding their needs. However, in e-learning systems, users may have no idea of what they are looking for and the learning object metadata. This study presents an ontological approach for semantic-aware learning object retrieval. This approach has two significant novel features: a fully automatic ontology-based query expansion algorithm for inferring and aggregating user intention based on their original short query, and another “ambiguity removal” procedure for correcting inappropriate user query terms. This approach is sufficiently generic to be embedded to other LOM-based search mechanisms for semantic-aware learning object retrieval. Focused on digital learning material and contrasted to other traditional keyword-based search technologies, the proposed approach has experimentally demonstrated significantly improved retrieval precision and recall rate.

A review of ontology based query expansion

This paper examines the meaning of context in relation to ontology based query expansion and contains a review of query expansion approaches. The various query expansion approaches include relevance feedback, corpus dependent knowledge models and corpus independent knowledge models. Case studies detailing query expansion using domain-specific and domain-independent ontologies are also included. The penultimate section attempts to synthesise the information obtained from the review and provide success factors in using an ontology for query expansion. Finally the area of further research in applying context from an ontology to query expansion within a newswire domain is described.

Ontology based query expansion framework for use in medical information systems

This paper presents a framework which combines data and text retrieval techniques to exercise and evaluate ontology based query expansions. We prepare by using linguistic techniques to identify query and document concepts, locating them in a ontologically defined semantic space. Expansions originate from the identified query concepts, with success determined by matching in the relevant document set. We identify three orthogonal dimensions that can affect query expansion success; relationship source, success measure technique, and query expansion technique. Expansion technique is further divided into six different categories: simple pruning, complex probability, voting, directional, semantic propagation, and multiple source concept. We describe each technique and show examples where they would be useful. The system architecture used facilitates plugging in of various expansion and evaluation routines, and flowing results from one method to the next. The system is useful for microanalysis of query expansion, discovering which components of ontological derived knowledge most influence query expansion success. In this work, we apply our framework to the medical domain.

Terminology-driven literature mining and knowledge acquisition in biomedicine

In this paper we describe Tagged Information Management System (TIMS), an integrated knowledge management system for the domain of molecular biology and biomedicine, in which terminology-driven literature mining, knowledge acquisition (KA), knowledge integration (KI), and XML-based knowledge retrieval are combined using tag information management and ontology inference. The system integrates automatic terminology acquisition, term variation management, hierarchical term clustering, tag-based information extraction (IE), and ontology-based query expansion. TIMS supports introducing and combining different types of tags (linguistic and domain-specific, manual and automatic). Tag-based interval operations and a query language are introduced in order to facilitate KA and retrieval from XML documents. Through KA examples, we illustrate the way in which literature mining techniques can be utilised for knowledge discovery from documents.