Information Retrieval with a Simplified Conceptual Graph-Like Representation

Ordoñez-Salinas, Sonia; Gelbukh, Alexander

doi:10.1007/978-3-642-16761-4_9

Cited by 9 publications

(8 citation statements)

References 26 publications

(29 reference statements)

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“…In this method we propose a step by step process for storing the ICD code and its actual relationship into the database, rather than directly loading from the server. This helps in identifying the diagnosis code of any given symptoms easily and accurately [4]. First, we construct clauses from the observed symptoms.…”

Section: Proposed Systemmentioning

confidence: 99%

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Conceptual Graph Representation Framework for ICD-10

Hema¹,

Selwyn²

2015

Procedia Computer Science

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Codification and representation of knowledge from structured and unstructured repositories have been the requirement for Expert Systems designing. Structured repositories remain open for knowledge extraction and representation for building a knowledgebase (KB). ICD-v10 (International Classification of Diseases), a structured repository, is the 10 th revision of the collection of Disease codes and health related problems, symptoms, complaints and external causes of injury. ICD-v10 uses a 7-bit code to represent all diseases. The main focus of our research work is to propose new methods for capturing and representing these codes in the forms of Knowledge from Semantic Structured Repository with better materialization, quality and repository usage, along with logical unit and logical entailments to form conceptual relations among them. Apart from creating a new representation structure, this work gives direction on the storage of these structures as Knowledge Unit (KU).

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Section: Proposed Systemmentioning

confidence: 99%

“…Hierarchical Graph representation of FractureConceptual Graph's can be represented in three different ways, namely; Deterministic Automatic Transformation, Statistical Automatic Transformation and Manual Transformation [4]. …”

mentioning

confidence: 99%

Conceptual Graph Representation Framework for ICD-10

Hema¹,

Selwyn²

2015

Procedia Computer Science

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“…The ISO/IEC 24707 Standard for common logic that characterizes semantics in terms of dynamic linguistic structure and model theoretical semantics is the official standard for conceptual graph linguistic structure and semantics. Nevertheless, the meaning of natural languages is difficult to change to the systems of the CGs [35]. Most works can be divided into manual development, deterministic methods and observable methodologies in the building of CGs.…”

Section: Conceptual Graphs Model (Cgm)mentioning

confidence: 99%

“…Finally, each sentence was drawn up in the concept graph using standard CG principles [32]. Ordoñez-Salinas and Gelbukh [35] proposed a linguistic utilization to be used in light of the dependence and the standard characteristic of conceptual graphs. The scientists used noun pre-modifiers and noun post-modifiers and verb contours separated from VerbNet to produce the grams of dependence, which include verb classification, their syntactic portrayal, and framework depictions, as a source of the meanings of semantic components.…”

Section: Conceptual Graphs Model (Cgm)mentioning

confidence: 99%

Graph-Based Text Representation and Matching: A Review of the State of the Art and Future Challenges

Osman

Barukub

2020

IEEE Access

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Graph-based text representation is one of the important preprocessing steps in data and text mining, Natural Language Processing (NLP), and information retrieval approaches. The graph-based methods focus on how to represent text documents in the shape of a graph to exploit the best features of their characteristics. This study reviews and lists the advantages and disadvantages of such methods employed or developed in graph-based text representations. The literature shows that some of the proposed graph-based methods suffer from a lack of representing texts in certain situations. Currently, several techniques are commonly used in graph-based text representation. However, there are still some weaknesses and shortages in these techniques and tools that significantly affect the success of graph representation and graph matching. In this review, we conduct an inclusive survey of the state of the art in graph-based text representation and learning. We provide a formal description of the problem of graph-based text representation and introduce some basic concepts. More significantly, this study proposes a new taxonomy of graph-based text representation, categorizing the existing studies based on representation characteristics and scheme techniques. In terms of the representation scheme taxonomy, we introduce four main types of conceptual graph schemes and summarize the challenges faced in each scheme. The main issues of graph representation, such as research topics and the sub-taxonomy of graph models for web documents, are introduced and categorized. This research also covers some tasks of understanding natural language processing (NLP) that depend on different types of graph structures. In addition, the graph matching taxonomy implements three main categories based on the matching approach, including structural-, semantic-, and similarity-based approaches. Moreover, a deep comparison of these approaches is discussed and reported in terms of methods and tools, the concepts of matching and locality, and the application domains that use these tools. Finally, the paper recommends seven promising future study directions in the graph-based text representation field. These recommendation points are summarized and highlighted as open problems and challenges of graphbased text representation and learning to facilitate and fill the research gaps for scientific researchers in this field.

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“…Gelbukh et.al [10] to compute max-common sub graph, to compare the similarity between graphs i.e. the document and the query.…”

Section: Similarity Measure and Scoring/rankingmentioning

confidence: 99%

Review on Information Retrieval for Desktop Search Engine

Akshay¹,

Nikhil²,

Chowdhury³

et al. 2020

ijeste

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Search is an important aspect of information management often taken for granted. Domain specific repositories are growing in both size and numbers calling for efficient search and retrieval of documents. This paper explores the possible techniques and necessary system components for a search engine charting several iterative optimizations over the last few years. This paper focuses on NLP models while retaining basic principles from other methods that assist in information search.

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Information Retrieval with a Simplified Conceptual Graph-Like Representation

Cited by 9 publications

References 26 publications

Conceptual Graph Representation Framework for ICD-10

Conceptual Graph Representation Framework for ICD-10

Graph-Based Text Representation and Matching: A Review of the State of the Art and Future Challenges

Review on Information Retrieval for Desktop Search Engine

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