Enhancing Textual Knowledge Discovery using a Context-Awareness Approach

Addressed in this paper is the issue of semantic relationship extraction from semi-structured documents. Many research efforts have been made so far on the semantic information extraction. However, much of the previous work focuses on detecting `isolated' semantic information by making use of linguistic analysis or linkage information in web pages and limited research has been done on extracting semantic relationship from the semi-structured documents. In this paper, we propose a method for semantic relationship extraction by using the logical information in the semi-structured document (semi-structured document usually has various types of structure information, e.g. a semi-structured document may be hierarchical laid out). To the best of our knowledge, extracting semantic relationships by using logical information has not been investigated previously. A probabilistic approach has been proposed in the paper. Features used in the probabilistic model have been defined.

A large volume of unstructured data, especially text data, is generated and exchanged daily. Consequently, the importance of extracting patterns and discovering knowledge from textual data is significantly increasing. As the task of automatically recognizing the relations between two or more entities, semantic relation extraction has a prominent role in the exploitation of raw text. This article surveys different approaches and types of relation extraction in English and the most prominent proposed methods in Persian. We also introduce, analyze, and compare the most important datasets available for relation extraction in Persian and English. Furthermore, traditional and emerging evaluation metrics for supervised, semi-supervised, and unsupervised methods are described, along with pointers to commonly used performance evaluation datasets. Finally, we briefly describe challenges in extracting relationships in Persian and English and dataset creation challenges.

This paper describes the integration of verbal idioms into an Natural Language Processing (NLP) system, adopting a construction approach, which is based on the prior parsing stage, so that these Multi-Word Expressions (MWE) can be taken into account in subsequent tasks, such as semantic role labeling or whole-part relation extraction. The paper focuses on body-part nouns, which are often part of many verbal idioms, and uses a manually annotated corpus to evaluate its parsing strategy. Results showed a precision of 0.92, 0.83 recall, 0.87 f-measure and an accuracy 0.99.

Hierarchical learning strategy in semantic relation extraction

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

Extracting semantic relations from the Quranic Arabic based on Arabic conjunctive patterns

In this paper, we proposed a novel approach based on topic ontology for tag recommendation. The proposed approach intelligently generates tag suggestions to blogs. In this approach, we construct topic ontology through enriching the set of categories in existing small ontology called as Open Directory Project. To construct topic ontology, a set of topics and their associated semantic relationships is identified automatically from the corpus-based external knowledge resources such as Wikipedia and WordNet. The construction relies on two folds such as concept acquisition and semantic relation extraction. In the first fold, a topic-mapping algorithm is developed to acquire the concepts from the semantic of Wikipedia. A semantic similarity-clustering algorithm is used to compute the semantic similarity measure to group the set of similar concepts. The second is the semantic relation extraction algorithm, which derives associated semantic relations between the set of extracted topics from the lexical patterns between synsets in WordNet. A suitable software prototype is created to implement the topic ontology construction process. A Jena API framework is used to organize the set of extracted semantic concepts and their corresponding relationship in the form of knowledgeable representation of Web ontology language. Thus, Protege tool provides the platform to visualize the automatically constructed topic ontology successfully. Using the constructed topic ontology, we can generate and suggest the most suitable tags for the new resource to users. The applicability of topic ontology with a spreading activation algorithm supports efficient recommendation in practice that can recommend the most popular tags for a specific resource. The spreading activation algorithm can assign the interest scores to the existing extracted blog content and tags. The weight of the tags is computed based on the activation score determined from the similarity between the topics in constructed topic ontology and content of the existing blogs. High-quality tags that has the highest activation score is recommended to the users. Finally, we conducted experimental evaluation of our tag recommendation approach using a large set of real-world data sets. Our experimental results explore and compare the capabilities of our proposed topic ontology with the spreading activation tag recommendation approach with respect to the existing AutoTag mechanism. And also discuss about the improvement in precision and recall of recommended tags on the data sets of Delicious and BibSonomy. The experiment shows that tag recommendation using topic ontology results in the folksonomy enrichment. Thus, we report the results of an experiment mean to improve the performance of the tag recommendation approach and its quality. © 2012 Wiley Periodicals, Inc.

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

Extracting the relations between medical concepts is very valuable in the medical domain. Scientists need to extract relevant information and semantic relations between medical concepts, including protein and protein, gene and protein, drug and drug, and drug and disease. These relations can be extracted from biomedical literature available on various databases. This study examines the extraction of semantic relations that can occur between diseases and drugs. Findings will help specialists make good decisions when administering a medication to a patient and will allow them to continuously be up to date in their field. The objective of this work is to identify different features related to drugs and diseases from medical texts by applying Natural Language Processing (NLP) techniques and UMLS ontology. The Support Vector Machine classifier uses these features to extract valuable semantic relationships among text entities. The contributing factor of this research is the combination of the strength of a suggested NLP technique, which takes advantage of UMLS ontology and enables the extraction of correct and adequate features (frequency features, lexical features, morphological features, syntactic features, and semantic features), and Support Vector Machines with polynomial kernel function. These features are manipulated to pinpoint the relations between drug and disease. The proposed approach was evaluated using a standard corpus extracted from MEDLINE. The finding considerably improves the performance and outperforms similar works, especially the f-score for the most important relation “cure,” which is equal to 98.19%. The accuracy percentage is better than those in all the existing works for all the relations.

Construction claim analysis largely depends on determining the existence of impact factors. Extensive research has been conducted on the relationships between impact factors and outcomes of construction litigation from various perspectives and using various methodologies. However, only a few of them have taken into consideration the automated semantic interpretation of impact factors contained in a construction litigation case. In this paper, based on previous pilot studies on the domain ontologies of construction contractual semantics, a rule-based NLP (Natural Language Processing) methodology for semantically interpreting impact factors for construction claim cases is proposed. Based on the available NLP techniques and domain ontologies, this methodology utilizes a rule-based mechanism to achieve the mapping from textual elements to ontology entities. In this way, the support of domain ontology is provided to enhance the performance of the impact factor interpretation process in the text. Also, it was found that several software packages can work together to satisfy the demand for the implementation of this methodology. Further, to test the validity of this methodology, several case studies focusing on DSC (Differing Site Conditions) claims are conducted by adopting cases from legal databases as data. The significance of this research is that it provides a more automated functionality to the traditional approach of claim outcome prediction by adding one more semantic factor-interpreting layer to it, and by also exploring the application of ontology-based NLP in the domain of construction claim analysis via text processing.

This paper presents an approach to construct rules automatically to identify semantic relations, such as Hypernym, Part_Of and Material from Modern Chinese Standard Dictionary (MCSD). This approach combines all the useful information such as part of speeches, syntactic information, and extracted semantic relations together in order to identify as many semantic relations as possible. However, not all the information provided, like syntactic information, is reliable. A method is required to ensure that the right information up to a point can be picked. And then all possible semantic relations are constructed as a net after being identified. Finally, we evaluate the net by determining word similarity.

Geographic information integration and fusion from multi-sources geospatial data is still a challenge because of semantic heterogeneity of geospatial entity data. These will delay geospatial cooperation decision support and integrated assessment application for natural resources and environmental problem. Ontology as a conceptualization and knowledge representation of application domain have provided a potential and aided support for entity matching and information integration of multiple sources and heterogeneous data. However, many data sources may not provided ontology definition and formalization representation. This paper will provide a method of graph model based ontologies representation and ontologies learning from data sources schema. Entity matching and fusion with ontology support is also discussed. This paper has classified unary properties and binary properties in ontology representation. Binary semantic relations in ontologies are analyzed. Based on semantic relations, the Tree model and Network model of ontology representation are defined. Through analyzing entity data schema, semantic relations extraction and learning are also discussed in constitution of entity ontologies. The framework of entity fusion with ontology support has been designed. Finally entity matching and fusion methods are discussed.

This book chapter presents several natural language processing (NLP) and machine learning (ML) techniques that can help achieve a better medical practice by means of extracting relevant medical information from the wealth of textual data. The chapter describes three major tasks: building intelligent tools that can help in the clinical decision making, tools that can automatically identify relevant medical information from the life-science literature, and tools that can extract semantic relations between medical concepts. Besides introducing and describing these tasks, methodological settings accompanied by representative results obtained on real-life data sets are presented.

This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7 on Semantic Relation Extraction and Classification in Scientific Papers. First we extract the shortest dependency path (sdp) between two entities, then we introduce a convolutional neural network (CNN) which takes the shortest dependency path embeddings as input and performs relation classification with differing objectives for each subtask of the shared task. This approach achieved overall F1 scores of 76.7 and 83.2 for relation classification on clean and noisy data, respectively. Furthermore, for combined relation extraction and classification on clean data, it obtained F1 scores of 37.4 and 33.6 for each phase. Our system ranks 3rd in all three sub-tasks of the shared task.

Extracting information from the medical imaging reports with domain ontology has attracted much attention in medical natural language processing field. Based on the unstructured characteristics of medical image report text, the existing image report domain ontology is constructed by ontology learning method, including ontology learning method based on language and ontology learning method based on machine learning. However, these existing methods ignore the anatomical knowledge embedded in medical imaging reports, which is very useful for extracting entity relationships from reports. In order to solve the above problems, this paper proposes a domain ontology construction method for medical image reporting based on anatomy knowledge, which combines the prior knowledge of pathology and anatomy to obtain the basic framework of the domain ontology as the knowledge driver. In particular, our proposed approach consists of two tasks. The first task is to convert each text report into a semantic tree through an anatomic knowledge based semantic subtree generation algorithm. Semantic subtree generation algorithm mainly consists of three parts: framework positioning, relation extraction and adding relation. Firstly, the text is located based on anatomical knowledge, and the relational extraction mainly adopts the method of dependency syntactic analysis to extract the three semantic relations contained in the text. Finally, add the relationship to the corresponding branch. The second task is to obtain the domain ontology by merging semantic subtree. This step is mainly to obtain the domain ontology by merging the nodes of the XML structure semantic tree. The experimental results show that this method can be used for relational extraction and domain ontology construction, laying a good foundation for the follow-up research.