Domain Knowledge Agents for Information Retrieval

1994 ◽  
pp. 39-46
Author(s):  
D. R. McGregor ◽  
C. R. Renfrew ◽  
I. A. MacLeod
Keyword(s):  
Information Retrieval ◽  
Domain Knowledge ◽  
Knowledge Agents

scholarly journals Domain knowledge and information retrieval in bacteriology

1992 ◽  
Vol 67 (10) ◽  
pp. S54-6 ◽  
Author(s):  
R de Bliek ◽  
J M Martz ◽  
G M Reich ◽  
C P Friedman ◽  
B M Wildemuth
Keyword(s):  
Information Retrieval ◽  
Domain Knowledge

Information Retrieval in Medicine: The Visual and the Invisible (Information Retrieval im Gesundheitswesen: Das Visuelle und das Unsichtbare)

2006 ◽  
Vol 48 (1) ◽  
Author(s):  
Floris Wiesman ◽  
Arie Hasman ◽  
Loes Braun ◽  
Jaap van den Herik
Keyword(s):  
Information Retrieval ◽  
Domain Knowledge ◽  
Information Needs ◽  
Electronic Patient Record ◽  
Software Agent ◽  
Patient Record ◽  
Full Control ◽  
The Invisible

SummaryEspecially in knowledge-rich domains such as medicine perfect access to the literature is essential for professionals. Unfortunately, especially in knowledge-rich domains it is difficult to achieve perfect access: it is too difficult and too time consuming for users to formulate queries that yield the maximum of relevant documents and a minimum of non-relevant ones. The paper first discusses the challenges of information retrieval in medicine and various existing approaches. To address the challenges two completely opposite approaches are presented. The first supports the user by means of metabrowsing: a visual way of depicting the relations between domain concepts and documents. Metabrowsing relieves the user from the formulation of queries, while leaving him in full control. The second approach aims to minimize the interaction with the user. Information needs and queries are autonomously and proactively formulated by a software-agent who remains invisible to the user. The agent uses the electronic patient record of a particular patient and domain knowledge. As a result, the agent provides the doctor with literature that is relevant with respect to the patient at hand.

Verification of Uncurated Protein Annotations

2010 ◽  
pp. 301-314
Author(s):  
Francisco M. Couto ◽  
Mário J. Silva ◽  
Vivian Lee ◽  
Emily Dimmer ◽  
Evelyn Camon ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Molecular Biology ◽  
Domain Knowledge ◽  
Text Processing ◽  
The Other ◽  
Research Projects ◽  
Evidence Text ◽  
Biological Sources ◽  
Biology Research ◽  
Automatic Text

Molecular Biology research projects produced vast amounts of data, part of which has been preserved in a variety of public databases. However, a large portion of the data contains a significant number of errors and therefore requires careful verification by curators, a painful and costly task, before being reliable enough to derive valid conclusions from it. On the other hand, research in biomedical information retrieval and information extraction are nowadays delivering Text Mining solutions that can support curators to improve the efficiency of their work to deliver better data resources. Over the past decades, automatic text processing systems have successfully exploited biomedical scientific literature to reduce the researchers’ efforts to keep up to date, but many of these systems still rely on domain knowledge that is integrated manually leading to unnecessary overheads and restrictions in its use. A more efficient approach would acquire the domain knowledge automatically from publicly available biological sources, such as BioOntologies, rather than using manually inserted domain knowledge. An example of this approach is GOAnnotator, a tool that assists the verification of uncurated protein annotations. It provided correct evidence text at 93% precision to the curators and thus achieved promising results. GOAnnotator was implemented as a web tool that is freely available at http://xldb.di.fc.ul.pt/rebil/tools/goa/.

Application of Ontology Technology in Agricultural Information Retrieval

2013 ◽  
Vol 756-759 ◽  
pp. 1249-1253 ◽  
Author(s):  
Jin Cui Kang ◽  
Jing Long Gao
Keyword(s):  
Information Retrieval ◽  
Knowledge Base ◽  
Domain Knowledge ◽  
The Internet ◽  
Related Information ◽  
Agricultural Knowledge ◽  
Intelligent Searching ◽  
Knowledge Ontology

The agricultural information on the internet become more and more, it is very difficult to search accurate related information from such different information, in order to improve the efficiency of information retrieval on the internet, the intelligent searching technology of agricultural information based on ontology is proposed. The paper firstly introduces research on the agricultural ontology and information retrieval, and takes agriculture domain knowledge as research object, analyzes the characters of agricultural domain knowledge and semantics retrieval, then uses the agricultural ontology to make the structure of agriculture ontology knowledge, and constructs the related agricultural knowledge ontology and knowledge base, implementing the intelligent searching of the agricultural information. The results indicate that the application of agricultural ontology technology in the agricultural information retrieval not only achieves the intelligent retrieval of agricultural information, but also greatly improves the accuracy and reliability of agricultural information retrieval.

OBIRE

2010 ◽  
Vol 1 (4) ◽  
pp. 58-73
Author(s):  
Xiangyu Liu ◽  
Maozhen Li ◽  
Yang Liu ◽  
Man Qi
Keyword(s):  
Fuzzy Logic ◽  
Information Retrieval ◽  
Domain Knowledge ◽  
P2p Network ◽  
The Internet ◽  
Bibliographic Record ◽  
P2p Networks ◽  
Bibliographic Information ◽  
User Query ◽  
Bibliographic Records

It has been widely recognized that bibliographic information plays an increasingly important role for scientific research. Peer-to-peer (P2P) networks provide an effective environment for people belonging to a community to share various resources on the Internet. This paper presents OBIRE, an ontology based P2P network for bibliographic information retrieval. For a user query, OBIRE computes the degree of matches to indicate the similarity of a published record to the query. When searching for information, users can incorporate their domain knowledge into their queries which guides OBIRE to discover the bibliographic records that are of most interest of users. In addition, fuzzy logic based user recommendations are used to compute the trustiness of a set of keywords used by a bibliographic record which assists users in selecting bibliographic records. OBIRE is evaluated from the aspects of precision and recall, and experimental results show the effectiveness of OBIRE in bibliographic information retrieval.

Verification of Uncurated Protein Annotations

2011 ◽  
pp. 1360-1373
Author(s):  
Francisco M. Couto ◽  
Mário J. Silva ◽  
Vivian Lee ◽  
Emily Dimmer ◽  
Evelyn Camon ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Molecular Biology ◽  
Domain Knowledge ◽  
Text Processing ◽  
The Other ◽  
Research Projects ◽  
Evidence Text ◽  
Biological Sources ◽  
Biology Research ◽  
Automatic Text

Molecular Biology research projects produced vast amounts of data, part of which has been preserved in a variety of public databases. However, a large portion of the data contains a significant number of errors and therefore requires careful verification by curators, a painful and costly task, before being reliable enough to derive valid conclusions from it. On the other hand, research in biomedical information retrieval and information extraction are nowadays delivering Text Mining solutions that can support curators to improve the efficiency of their work to deliver better data resources. Over the past decades, automatic text processing systems have successfully exploited biomedical scientific literature to reduce the researchers’ efforts to keep up to date, but many of these systems still rely on domain knowledge that is integrated manually leading to unnecessary overheads and restrictions in its use. A more efficient approach would acquire the domain knowledge automatically from publicly available biological sources, such as BioOntologies, rather than using manually inserted domain knowledge. An example of this approach is GOAnnotator, a tool that assists the verification of uncurated protein annotations. It provided correct evidence text at 93% precision to the curators and thus achieved promising results. GOAnnotator was implemented as a web tool that is freely available at http://xldb.di.fc.ul.pt/rebil/tools/goa/.

Evaluating the effectiveness of Farsi-English query production using ontology: a case of scientometric ontology

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sanaz Manouchehri ◽  
Mahdieh Mirzabeigi ◽  
Tahere Jowkar
Keyword(s):  
Information Retrieval ◽  
User Satisfaction ◽  
Domain Knowledge ◽  
Interactive Effect ◽  
Point Of View ◽  
Content Type ◽  
Retrieval Systems ◽  
Before And After ◽  
Information Retrieval Systems ◽  
Google Search

PurposeThis paper aims to discover the effectiveness of Farsi-English query using ontology.Design/methodology/approachThe present study is quasi-experimental. The sample consisted of 60 students and graduate and doctoral staff from Shiraz University and the Regional Center for Science and Technology. A researcher-made questionnaire was used to assess the level of English language proficiency of users, background knowledge and their level of satisfaction with search results before and after using ontology. Each user also evaluated the relevance of the top ten results on the Google search engine results page before and after using ontology.FindingsThe findings showed that the level of complexity of the task, the use of ontology, the interactive effect of the level of complexity of the task with the domain knowledge of the users, and the interactive effect of the level of complexity of the task with ontology, influence the effectiveness of retrieval results from the users' point of view. The results of the present study also showed that the level of complexity of the task, the use of ontology, and the interactive effect of the level of complexity of the task and the use of ontology, affect the level of user satisfaction.Originality/valueThe results of this research are significant in both theoretical and practical aspects. Theoretically, given the lack of research in which the interactive effect of the use of ontology has examined the level of complexity of tasks and domain knowledge of users, the present study can be considered as an attempt to improve information retrieval systems. From a practical point of view, the results of this research will help researchers and designers of information retrieval systems to understand that the use of ontologies can be used to retrieve information and improve the query and assess the needs of users and their satisfaction in this field, and ultimately, making the information retrieval process more effective.

Design of Personalized Intelligent Information Retrieval Model Based on Agent

2012 ◽  
Vol 155-156 ◽  
pp. 1175-1179
Author(s):  
Zhong Biao Sheng ◽  
Hua Ping Jia ◽  
Xiao Rong Tong
Keyword(s):  
Information Retrieval ◽  
Domain Knowledge ◽  
Retrieval System ◽  
Retrieval Model ◽  
Retrieval Task ◽  
Dynamic Information ◽  
Model Based ◽  
Intelligent Information Retrieval ◽  
Intelligent Information ◽  
Personal Interests

The features of vast distributed dynamic information on Web caused the problem of “overload” and “mislead” while query. Intelligent agent is a way to solve it. After considering the problems of users’ personal interests during the information retrieve adequately, the paper proposes an intelligent information retrieval model based-on Agent. This system integrated domain knowledge and used many arithmetic of learning user’s interest. Each Agent co-operates to finish information retrieval task, manifest the characteristics of intellectualization and individuality of in information retrieval. It is a good way to realize the highly effective intelligent retrieval system research.

Ontology Storage Models and Tools: An Authentic Survey

2016 ◽  
Vol 25 (4) ◽  
pp. 539-553 ◽  
Author(s):  
Sunitha Abburu ◽  
Suresh Babu Golla
Keyword(s):  
Information Retrieval ◽  
Domain Knowledge ◽  
High Performance ◽  
Semantic Information ◽  
Storage System ◽  
Research Work ◽  
Specific Storage ◽  
Storage Model ◽  
Semantic Information Retrieval ◽  
Ontology Storage

AbstractOntology is a formal, explicit specification of a shared conceptualization. Ontology provides domain vocabulary, domain knowledge, common understanding, shareability, information interoperability, reusability, concept hierarchy, and relationships that support semantic information retrieval. Ontology improves performance of the system by addressing interoperability issues due to semantic and syntactic heterogeneity. Vast numbers of application domain experts are using ontologies in diverse applications. Use of effective and efficient ontology storage system results improved performance in applications and enables semantic information retrieval. Many prominent researchers and software agencies have proposed and developed several ontology storage methods and tools with various features. The choice of a specific storage model/tool always depend on the specific purpose of the application and the nature of features that are available in the storage model/tool to be utilized in the specific applications. The familiarity of various ontology storage models and tools with the respective features helps user to choose an appropriate storage structure aiming at high-performance applications. The current research work is a comprehensively authentic study carryout out on various ontology storage models and tools with their respective features, which are very essential for optimum performance.

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