Use Of Semantic Relationships Research Articles

SANTOS: Relationship-based Semantic Table Union Search

Existing techniques for unionable table search define unionability using metadata (tables must have the same or similar schemas) or column-based metrics (for example, the values in a table should be drawn from the same domain). In this work, we introduce the use of semantic relationships between pairs of columns in a table to improve the accuracy of the union search. Consequently, we introduce a new notion of unionability that considers relationships between columns, together with the semantics of columns, in a principled way. To do so, we present two new methods to discover the semantic relationships between pairs of columns. The first uses an existing knowledge base (KB), and the second (which we call a "synthesized KB") uses knowledge from the data lake itself. We adopt an existing Table Union Search benchmark and present new (open) benchmarks that represent small and large real data lakes. We show that our new unionability search algorithm, called SANTOS, outperforms a state-of-the-art union search that uses a wide variety of column-based semantics, including word embeddings and regular expressions. We show empirically that our synthesized KB improves the accuracy of union search by representing relationship semantics that may not be contained in an available KB. This result hints at a promising future of creating synthesized KBs from data lakes with limited KB coverage and using them for union search.

Semantic-Based Search Engine System for Graph Images in Academic Literatures by Use of Semantic Relationships

Semantic-Based Search Engine System for Graph Images in Academic Literatures by Use of Semantic Relationships

Methods for Specifying Scientific Data Standards and Modeling Relationships with Applications to Neuroscience.

Neuroscience continues to experience a tremendous growth in data; in terms of the volume and variety of data, the velocity at which data is acquired, and in turn the veracity of data. These challenges are a serious impediment to sharing of data, analyses, and tools within and across labs. Here, we introduce BRAINformat, a novel data standardization framework for the design and management of scientific data formats. The BRAINformat library defines application-independent design concepts and modules that together create a general framework for standardization of scientific data. We describe the formal specification of scientific data standards, which facilitates sharing and verification of data and formats. We introduce the concept of Managed Objects, enabling semantic components of data formats to be specified as self-contained units, supporting modular and reusable design of data format components and file storage. We also introduce the novel concept of Relationship Attributes for modeling and use of semantic relationships between data objects. Based on these concepts we demonstrate the application of our framework to design and implement a standard format for electrophysiology data and show how data standardization and relationship-modeling facilitate data analysis and sharing. The format uses HDF5, enabling portable, scalable, and self-describing data storage and integration with modern high-performance computing for data-driven discovery. The BRAINformat library is open source, easy-to-use, and provides detailed user and developer documentation and is freely available at: https://bitbucket.org/oruebel/brainformat.

Relevance status value model of Index Islamicus on Islamic History and Civilizations

Purpose – The purposes of this study were to measure the relevance status of Index Islamicus, evaluate the semantic correlation between a query and documents and inquire the basis of its rank. Sorting the retrieved results from the most relevant to the least relevant is the common option of an information retrieval system. This sorting mechanism or relevance judgment is computed by measuring closeness of query with its documents. Design/methodology/approach – Forming up 100 queries on Islamic History and Civilizations, with two indexing elements (keyword and concept), a laboratory experiment was generated on its first ten items of the rank. Throughout an experimental research design, the relevance status value formula was used to measure system-computed rank and compare it with mean average precision. Findings – The results showed that the average status value of Index Islamicus’s ranking on relevance criterion was 18 per cent effective in terms of retrieving precise documents. Despite the main focus of this study being only on one subject domain and the items calculated were only 1,000, this small percentage of its ranking mechanism proved that semantic correlations between queries with subject domain did not achieve the satisfactory level. Research limitations/implications – Implication of this study could be a guideline for further research on ranking mechanism of other search engines because the limitation of this study was Index Islamicus being the only database, which was the focus of this study. Practical implications – Throughout this study, Index Islamicus would be benefited knowing the status of its ranking mechanism as well as other databases can make further research on their own ranking method following this study. Social implications – Researchers and vendors of online databases can ensure their users a true platform of search engine with a proper ranking list. Originality/value – Relevance status value model for Index Islamicus on Islamic History and Civilization that allows the system to rank documents according to the match between document and query and gives the idea of a better index. The model improves the system’s ranking mechanism, and promotes the use of semantic relationships. This research promotes the computation of relevance status value by domain for capturing subject-specific relevance criteria and semantic relationships.

An Intelligent Bio-medical Document Retrieval Using Concept Map Identification

The document retrieval is one of the fast growing and complex research area in the field of information retrieval. Concept-based information retrieval is more beneficial than keyword-based access. The most important advantage is the ability to make use of semantic relationships among concepts in finding relevant documents. Removal of irrelevant documents by identifying conceptual mismatches is another benefit. The linguistic representation of concepts is words and phrases. These concepts are mental structures. The inherent conciseness of natural language can make words represent multiple concepts. Different words can represent the same or similar concepts. We present a novel method of concept mining methodology, which provides an efficient way of document retrieval. The performance of this proposed retrieval system was analyzed by the existing methods for different medical documents. The proposed retrieval system shows that the documents are retrieved effectively based on concepts and the system performs with higher accuracy value.

Changes in the Generativity and Use of Semantic Relationships Concurrent With Milieu Language Intervention

Changes associated with a milieu language intervention implemented to teach productive semantic relationships (e.g., action-object combinations) were investigated. Subjects were 11 young children with borderline to moderate levels of mental retardation, all in the early stages of language development. Following a baseline period, each subject received three to four training sessions per week in a small group interactive play situation. Measures of generativity were analyzed via pre/post group comparisons. Concurrent with the intervention subjects showed substantial gains in their ability to generatively produce semantic relationships in the intervention context and across settings, adults, and interaction styles. Subjects also correctly responded to an increasing proportion of target probe questions in both the intervention and generalization settings. The results suggest that, for most of the subjects, milieu teaching was associated with enhanced generativity and use of semantic relationships.