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Relay of information from technical documentation by contact center workers to assist clients is limited by industry standard storage formats and query mechanisms. Here we present and evaluate a new methodology for processing technical documents and tagging them against a Telecom Hardware domain ontology. We deploy classical ontological NLP approaches to extract information from both text segments...
One key step in text mining is the categorization of texts, i.e., to put texts of the same or similar contents into one group so as to distinguish texts of different contents. However, traditional word-frequency-based statistical approaches, such as VSM model, failed to reflect the complicated meaning in texts. This paper ushers in domain ontology and constructs new conceptual vector space model in...
With the fast growing development of the Web, the adoption of ontologies to improve the exploitation of information resources, is already heralded as a promising model of representation. However, the relevance of information that they contain requires regular updating, and specifically, the addition of new knowledge. Recently, new research approaches were defined in order to automatically enrich ontology...
In traditional Vector Space Model (VSM) the TF*IDF method is widely used to adjust the weight of terms in text mining. However TF*EDF can not represent the semantic information of text by neglecting the semantic relevance between terms. In this paper, an improved ontology-based VSM is presented, in which the ontology-based term similarity is used to readjust the weight of semantically related terms...
In this article we show how to find evidence of incomplete or fractured processes in non-structured reports of known business processes, by means of rules, patterns and detection of cause-effect relationships. A priori classifications and probabilities of process activities are used as inputs for the analysis and rules detection. In this method we use a domain-specific ontology associated to process...
This paper presents a novel technique of document clustering based on frequent concepts. The proposed FCDC (Frequent Concepts based Document Clustering), a clustering algorithm works with frequent concepts rather than frequent itemsets used in traditional text mining techniques. Many well known clustering algorithms deal with documents as bag of words while they ignore the important relationship between...
A Priori knowledge is the knowledge an agent has gained prior to experiences and learning. A priori knowledge acquisition along with relevant functional ontology building remain obstacles in the process of building knowledge-based agent-oriented systems. In this paper, we describe epistemic analysis techniques that we are exploring at Ctest Laboratories that are used to automatically discover ontological...
Most of text association pattern mining techniques transform texts into flat bags of words representation, which does not preserve sufficient semantics for the purpose of knowledge discovery. So the depth and accuracy of mining are not satisfying. In order to solve this problem, a novel ontology-based semantic association pattern mining model is proposed. The suggested model applies semantic role...
Biomedical entity extraction from unstructured web documents is an important task that needs to be performed in order to discover knowledge in the veterinary medicine domain. In general, this task can be approached by applying domain specific ontologies, but a review of the literature shows that there is no universal dictionary, or ontology for this domain. To address this issue, we manually construct...
Text mining is an effective means of acquiring potentially useful knowledge from text document. However, traditional text mining cannot achieve high accuracy, because it cannot effectively make use of the semantic information of the text. Ontology provides theoretical basis and technical support for semantic information representation and organization. This paper introduces and analyzes text mining...
Fetching Lexico-Syntactic patterns from text rely on pairs of words (positive instances) that represent the target relation, and finding their simultaneous occurrence in text corpus. Due to existence of WordNet thesaurus (which contains the semantic relationship between words), collecting positive instances is easy. In non-english languages, it's hard to collect large number of positive instances...
We address the process of semi-automatic ontology extension using ontology content and in ontology structure. To evaluate the proposed approach, we have conducted experiments on real data from two domains comparing several measures for ranking correspondence between present ontology concepts and new domain concepts suggested for ontology extension. The best results are achieved by combining content...
Digitizing a historical document using ontologies and natural language processing techniques can transform it from arcane text to a useful knowledge base.The Handbook on Architecture (Handbuch der Architektur) was perhaps one of the most ambitious publishing projects ever. Like a 19thcentury Wikipedia, it attempted nothing less than a full account of all architectural knowledge available at the time,...
Summary form only given. Can you imagine working in a field of research where even practitioners argue if it exists as a field at all? 'Digital Arts and Humanities' is such a field and my role within it includes the task of further developing a classification system for resources, people and all sorts of activities in this 'field': a taxonomy (soon to be ontology) of digital methods for research....
To improve the mobile searching efficiency in 3G mobile telephones, a personalized searching technology based on subject-word customizing model is presented, which is focused on the semantic association among subject words. First, a knowledge bases describing model about subject-word according to ontology is proposed. Second, the article researches to use the meanings of subject words to match combined...
Identification of protein-protein interactions (PPIs) and downstream functional events is important to biologists because these are key to building protein networks and biological pathways. Of high relevance are the PPIs involving proteins with some post-translational modification (PTM), since PTMs constitute one way of regulating protein function. However, this type of PPIs is not yet well represented...
Ontology learning aims to facilitate the construction of ontologies by decreasing the amount of effort required to produce an ontology for a new domain. However, there are few studies that attempt to automate the entire ontology learning process from the collection of domain-specific literature, to text mining to build new ontologies or enrich existing ones. In this paper, we present a complete framework...
This paper describes a linguistic text mining tool for analyzing problem reports in aerospace engineering and safety organizations. The semantic trend analysis tool (STAT) helps analysts find and review recurrences, similarities and trends in problem reports. The tool is being used to analyze engineering discrepancy reports at NASA Johnson Space Center. The tool has been augmented with a statistical...
Semantic relations are an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. Automatic semantic relation extraction system is a crucial tool that can reduce the bottleneck of knowledge acquisition in the ontologies construction. In this paper, we present a statistical approach for learning the semantic relations between...
Biomedical named entity recognition, an important step, makes preparation for extracting information from biomedical textual resources. This paper presents a hybrid approach to recognize biomedical entity, which includes POS (Part-of-Speech) tagging, rules-based and dictionary-based approach using biomedical ontology. Experiment results show our approach can find untagged biomedical entity name in...
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