Comparing keyword search to semantic search: a case study in solving crossword puzzles using the Google™ API

2008 ◽  
Vol 38 (4) ◽  
pp. 417-445 ◽  
Author(s):  
David E. Goldschmidt ◽  
Mukkai Krishnamoorthy
Keyword(s):  
Keyword Search ◽  
Semantic Search ◽  
Crossword Puzzles

scholarly journals An Art of Review on Conceptual based Information Retrieval

Webology ◽  
2021 ◽  
Vol 18 (SI02) ◽  
pp. 21-31
Author(s):  
P. Mahalakshmi ◽  
N. Sabiyath Fathima
Keyword(s):  
Information Retrieval ◽  
Keyword Search ◽  
Semantic Representation ◽  
Research Work ◽  
Semantic Search ◽  
Future Research ◽  
Conceptual Information ◽  
Retrieval Systems ◽  
User Query ◽  
Information Retrieval Systems

Basically keywords are used to index and retrieve the documents for the user query in a conventional information retrieval systems. When more than one keywords are used for defining the single concept in the documents and in the queries, inaccurate and incomplete results were produced by keyword based retrieval systems. Additionally, manual interventions are required for determining the relationship between the related keywords in terms of semantics to produce the accurate results which have paved the way for semantic search. Various research work has been carried out on concept based information retrieval to tackle the difficulties that are caused by the conventional keyword search and the semantic search systems. This paper aims at elucidating various representation of text that is responsible for retrieving relevant search results, approaches along with the evaluation that are carried out in conceptual information retrieval, the challenges faced by the existing research to expatiate requirements of future research. In addition, the conceptual information that are extracted from the different sources for utilizing the semantic representation by the existing systems have been discussed.

Implementation of Semantic Search to Support Clinical Decision-Making

2015 ◽  
pp. 270-286
Author(s):  
Andrew Tawfik ◽  
Karl Kochendorfer
Keyword(s):  
Human Performance ◽  
Clinical Decision Making ◽  
Information Overload ◽  
Clinical Decision ◽  
Human Performance Technology ◽  
The United States ◽  
Semantic Search ◽  
Current Case ◽  
Performance Technology

The current case study is situated within a large, land grant hospital located in the Midwestern region of the United States. Although the physicians had seen an increase in medical related human performance technology (HPTs) within the organization (e.g. computer physician ordered entry) some challenges remained as the hospital sought to improve the productivity of the electronic health record (EHRs). Specifically, physicians had difficulty finding information embedded within the chart due to usability problems and information overload. To overcome the challenges, a semantic search within the chart was implemented as a solution for physicians to retrieve relevant results given the conceptual semantic pattern. The case study will discuss many elements of the implementation based on our experience and feedback from clinicians. The case will specifically highlight the importance of training and change agents within an organization.

Construction of the structural definition-based terminology ontology system and semantic search evaluation

2016 ◽  
Vol 34 (4) ◽  
pp. 705-732 ◽  
Author(s):  
Young Man Ko ◽  
Min Sun Song ◽  
Seung Jun Lee
Keyword(s):  
Keyword Search ◽  
Relevant Information ◽  
Semantic Search ◽  
Research Papers ◽  
Inference Control ◽  
Content Type ◽  
Search Results ◽  
Input Ratio ◽  
Structural Definition ◽  
Term Data

Purpose The purpose of this paper is to construct a structural definition-based terminology ontology system that defines the meanings of academic terms on the basis of properties and links terms with properties that are structured by conceptual categories (classes). This study also aims to test the possibility of semantic searches by generating inference rules and setting very complicated search scenarios. Design/methodology/approach For the study, 55,236 keywords from the articles of the “Korea Citation Index” were structurally defined and relationships among terms and properties were built. Then, the authors converted the RDB data into RDF and designed ontologies using the ontology developing tool Protégé. The authors also tested the designed ontology with the inference engine of the Protégé editor. The generated reference rules were tested by TBox and SPARQL queries. Findings The authors generated inference control rules targeting high-input-ratio data in the properties of classes by calculating the input ratio of real input data in the system, and then the authors executed a semantic search by SPARQL query by setting very complicated search scenarios, for which it would be difficult to deduce results via a simple keyword search. As a result, it was confirmed that the search results show the logical combination of semantically related term data. Practical implications The proposed terminology ontology system was constructed with the author keywords from research papers, it will be useful in searching the research papers which include the keywords as search results by the complex combination of semantic relation. And the Structural Terminology Net database could be utilized as an index database in retrieval services and the mining of informal big data through the application of well-defined semantic concepts to each term. Originality/value This paper presented a methodology for supporting IR using expanded queries based on a novel model of structural terminology-based ontology. The user who wants to access the specific topic can create query that brings the semantically relevant information. The search results show the logical combination of semantically related term data, which would be difficult to deduce results via traditional IR systems.

Supporting Biomimetic Design Through Categorization of Natural-Language Keyword-Search Results

2009 ◽  
Author(s):  
Ji Ke ◽  
J. S. Wallace ◽  
L. H. Shu
Keyword(s):  
Fuel Cell ◽  
Natural Language ◽  
Keyword Search ◽  
Bipolar Plate ◽  
Biological Information ◽  
Good Source ◽  
Search Results ◽  
Biological Phenomena ◽  
Keyword Searches

Biology is a good source of analogies for engineering design. One approach of retrieving biological analogies is to perform keyword searches on natural-language sources such as books, journals, etc. A challenge of retrieving information from natural-language sources is the potential requirement to process a large number of search results. This paper describes a categorization method that organizes a large group of diverse biological information into meaningful categories. The benefits of the categorization functionality are demonstrated through a case study on the redesign of a fuel cell bipolar plate. In this case study, our categorization method reduced the effort to systematically identify biological phenomena by up to ∼80%.

Construction of metadata database structured by conceptual elements of text structure and semantic search evaluation of Korean studies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Young Man Ko ◽  
Min Sun Song ◽  
Seung Jun Lee
Keyword(s):  
Keyword Search ◽  
Search Algorithm ◽  
Interdisciplinary Studies ◽  
Text Structure ◽  
Semantic Search ◽  
Research Articles ◽  
Content Type ◽  
Search Results ◽  
Korean Studies ◽  
Data Points

Purpose This study aims to develop metadata of conceptual elements based on the text structure of research articles on Korean studies, to propose a search algorithm that reflects the combination of semantically relevant data in accordance with the search intention of research paper and to examine the algorithm whether there is a difference in the intention-based search results. Design/methodology/approach This study constructed a metadata database of 5,007 research articles on Korean studies arranged by conceptual elements of text structure and developed F1(w)-score weighted to conceptual elements based on the F1-score and the number of data points from each element. This study evaluated the algorithm by comparing search results of the F1(w)-score algorithm with those of the Term Frequency- Inverse Document Frequency (TF-IDF) algorithm and simple keyword search. Findings The authors find that the higher the F1(w)-score, the closer the semantic relevance of search intention. Furthermore, F1(w)-score generated search results were more closely related to the search intention than those of TF-IDF and simple keyword search. Research limitations/implications Even though the F1(w)-score was developed in this study to evaluate the search results of metadata database structured by conceptual elements of text structure of Korean studies, the algorithm can be used as a tool for searching the database which is a tuning process of weighting required. Practical implications A metadata database based on text structure and a search method based on weights of metadata elements – F1(w)-score – can be useful for interdisciplinary studies, especially for semantic search in regional studies. Originality/value This paper presents a methodology for supporting IR using F1(w)-score—a novel model for weighting metadata elements based on text structure. The F1(w)-score-based search results show the combination of semantically relevant data, which are otherwise difficult to search for using similarity of search words.

Semantic Search based on Ontology with Case Study: Indonesian Batik

2017 ◽  
Vol 170 (7) ◽  
pp. 20-23
Author(s):  
Tri Kustanti ◽  
Eko Sediyono ◽  
Oky Dwi
Keyword(s):  
Semantic Search

Interrogation Based on Semantic Annotations

2017 ◽  
Vol 9 (2) ◽  
pp. 47-67
Author(s):  
Latreche Abdelkrim ◽  
Lehireche Ahmed ◽  
Kadda Benyahia
Keyword(s):  
Information Search ◽  
Information Needs ◽  
Keyword Search ◽  
Contextual Information ◽  
Query Languages ◽  
Semantic Search ◽  
Keyword Query ◽  
Web Based ◽  
Semantic Annotations ◽  
New Process

Traditional information search approaches do not explicitly capture the meaning of a keyword query, but provide a good way for the user to express his or her information needs based on the keywords. In principle, semantic search aims to produce better results than traditional keyword search, but its progression has retarded because of to the complexity of the query languages. In this article, the authors present an approach to adapt keyword queries to querying the semantic web based on semantic annotations: the approach automatically construct structured formal queries from keywords. The authors propose a new process where they introduce a novel context-based query autocompletion feature to help the users to construct their keywords query by suggesting queries given prefixes. They also address the problem of context-based generating formal queries by exploiting user's query history, where previous queries can be used as contextual information for generating a new query. With the first tests, the authors' approach achieved encouraging results.

Comparative Performance Evaluation of Keyword and Semantic Search Engines using Different Query Set Categories

2020 ◽  
Vol 13 (5) ◽  
pp. 1057-1070
Author(s):  
Poonam Jatwani ◽  
Pradeep Tomar ◽  
Vandana Dhingra
Keyword(s):  
Performance Evaluation ◽  
Search Engines ◽  
Experimental Evaluation ◽  
Keyword Search ◽  
Research Work ◽  
Semantic Search ◽  
Comparative Performance ◽  
Handling Performance ◽  
Single Term ◽  
Relevance Measures

Background: Keyword search engines are unable to understand the intention of user as a result they produce enormous results for user to distinguish between relevant and non relevant answers of user queries. This has led to rise in requirement to study search capabilities of different search engines. In this research work, experimental evaluation is done based on different metrics to distinguish different search engines on the basis of type of query that can be handled by them. Methods: To check the semantics handling performance, four types of query sets consisting of 20 queries of agriculture domain are chosen. Different query set are single term queries, two term queries, three term queries and NLP queries. Queries from different query set were submitted to Google, DuckDuckGo and Bing search engines. Effectiveness of different search engines for different nature of queries is experimented and evaluated in this research using Grade relevance measures like Cumulative Gain, Discounted Cumulative Gain, Ideal Discounted Cumulative Gain, and Normalized Discounted Cumulative Gain in addition to the precision metric. Results: Our experimental results demonstrate that for single term query, Google retrieves more relevant documents and performs better and DuckDuckGo retrieves more relevant documents for NLP queries. Conclusion: Analysis done in this research shows that DuckDuckGo understand human intention and retrieve more relevant result, through NLP queries as compared to other search engines.

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