Semantic Relation Extraction Research Articles

ASRextractor: A Tool extracting Semantic Relations between Arabic Named Entities

Since the MUC-7, the extraction of the Semantic Relation (SR) extraction has been started aiming to detect the significant links between Named Entities (NEs). This task is evolved in many domains to realize several objectives such as corpora and electronic NE dictionary enrichment. In this context, we propose a rue-based system called ASRextractor, which extracts and annotates SRs relating Arabic NEs (ANEs). The SR extraction is based on an annotated Arabic Wikipedia corpus and it helps us identify 18 SR types such as synonymy and origin. For the SR annotation, our proposed system reposes on an annotation syntax respecting the TEI (Text Encoding Initiative) recommendation. Moreover, ASRextractor is based on finite state transducers, which ensure both the extraction and annotation process. The established transducers are regrouped inside an analysis cascade in a predefined order. The metric values show that our obtained results are encouraging.

Analysis of Influence of Different Relations Types on the Quality of Thesaurus Application to Text Classification Problems

The main purpose of the article is to analyze how effectively different types of thesaurus relations can be used for solutions of text classification tasks. The basis of the study is an automatically generated thesaurus of a subject area, that contains three types of relations: synonymous, hierarchical and associative. To generate the thesaurus the authors use a hybrid method based on several linguistic and statistical algorithms for extraction of semantic relations. The method allows to create a thesaurus with a sufficiently large number of terms and relations among them. The authors consider two problems: topical text classification and sentiment classification of large newspaper articles. To solve them, the authors developed two approaches that complement standard algorithms with a procedure that take into account thesaurus relations to determine semantic features of texts. The approach to topical classification includes the standard unsupervised BM25 algorithm and the procedure, that take into account synonymous and hierarchical relations of the thesaurus of the subject area. The approach to sentiment classification consists of two steps. At the first step, a thesaurus is created, whose terms weight polarities are calculated depending on the term occurrences in the training set or on the weights of related thesaurus terms. At the second step, the thesaurus is used to compute the features of words from texts and to classify texts by the algorithm SVM or Naive Bayes. In experiments with text corpora BBCSport, Reuters, PubMed and the corpus of articles about American immigrants, the authors varied the types of thesaurus relations that are involved in the classification and the degree of their use. The results of the experiments make it possible to evaluate the efficiency of the application of thesaurus relations for classification of raw texts and to determine under what conditions certain relationships affect more or less. In particular, the most useful thesaurus connections are synonymous and hierarchical, as they provide a better quality of classification.

Information extraction for knowledge base construction in the music domain

The rate at which information about music is being created and shared on the web is growing exponentially. However, the challenge of making sense of all this data remains an open problem. In this paper, we present and evaluate an Information Extraction pipeline aimed at the construction of a Music Knowledge Base. Our approach starts off by collecting thousands of stories about songs from the songfacts.com website. Then, we combine a state-of-the-art Entity Linking tool and a linguistically motivated rule-based algorithm to extract semantic relations between entity pairs. Next, relations with similar semantics are grouped into clusters by exploiting syntactic dependencies. These relations are ranked thanks to a novel confidence measure based on statistical and linguistic evidence. Evaluation is carried out intrinsically, by assessing each component of the pipeline, as well as in an extrinsic task, in which we evaluate the contribution of natural language explanations in music recommendation. We demonstrate that our method is able to discover novel facts with high precision, which are missing in current generic as well as music-specific knowledge repositories.

Ontological Lexicon Enrichment: The Badea System For Semi-Automated Extraction Of Antonymy Relations From Arabic Language Corpora

language processing tools and applications; however, they are expensive to build, maintain, and extend. In this paper, we present the Badea system for the semi-automated extraction of lexical relations, specifically antonyms using a pattern-based approach to support the task of ontological lexicon enrichment. The approach is based on an ontology of “seed” pairs of antonyms in the Arabic language; we identify patterns in which the pairs occur and then use the patterns identified to find new antonym pairs in an Arabic textual corpora. Experiments are conducted on Badea using texts from three Arabic textual corpuses: KSUCCA, KACSTAC, and CAC. The system is evaluated and the patterns’ reliability and system performance is measured. The results from our experiments on the three Arabic corpora show that the pattern-based approach can be useful in the ontological enrichment task, as the evaluation of the system resulted in the ontology being updated with over 300 new antonym pairs, thereby enriching the lexicon and increasing its size by over 400%. Moreover, the results show important findings on the reliability of patterns in extracting antonyms for Arabic. The Badea system will facilitate the enrichment of ontological lexicons that can be very useful in any Arabic natural language processing system that requires semantic relation extraction.

Linguistic and Statistical Analysis of the Terminology for Constructing the Thesaurus of a Specified Field

The paper is devoted to the analysis of the body of terms and terminological sources for further automation of constructing the thesaurus of a subject area, which is regarded as poetics in our work. Preliminary systematization of terminology with a linguistic and statistical approach forms the body of semantically related concepts to automate extraction of semantic relationships between terms that define the structure of the thesaurus of the specified field.

Automatic Extraction of Hypernym & Meronym Relations in English Sentences Using Dependency Parser

Relation Extraction is the process of finding relationship between named entities in a text. Automatic extraction of semantic relation between pair of nouns is an important task with many potential applications like information retrieval, information extraction, text summarization, machine translation, question answering, thesaurus construction and word sense disambiguation. This paper describes an approach that extracts hypernym and meronym relation between proper nouns in sentences of a given text. Here machine learning techniques are used to automatically extract proper nouns with a given relationship from a text corpus. This work is based on dependency parsing with ADTree and Naive Bayes classifier. The approach is based on the analysis of the paths between noun pairs in the dependency parse trees of the sentences.

Methodolo- gical Aspects of Semantic Relationship Extraction for Automatic Thesaurus Generation

The paper is devoted to analysis of methods for automatic generation of a specialized thesaurus. The main algorithm of generation consists of three stages: selection and preprocessing of a text corpus, recognition of thesaurus terms, and extraction of relations among terms. Our work is focused on exploring methods for semantic relation extraction. We developed a test bench that allow to test well-known algorithms for extraction of synonyms and hypernyms. These algorithms are based on different relation extraction techniques: lexico-syntactic patterns, morpho-syntactic rules, measurement of term information quantity, general-purpose thesaurus WordNet, and Levenstein distance. For analysis of the result thesaurus we proposed a complex assessment that includes the following metrics: precision of extracted terms, precision and recall of hierarchical and synonym relations, and characteristics of the thesaurus graph (the number of extracted terms and semantic relationships of different types, the number of connected components, and the number of vertices in the largest component). The proposed set of metrics allows to evaluate the quality of the thesaurus as a whole, reveal some drawbacks of standard relation extraction methods, and create more efficient hybrid methods that can generate thesauri with better characteristics than thesauri generated by using separate methods. In order to illustrate this fact, one of such hybrid methods is considered in the paper. It combines the best standard algorithms for hypernym and synonym extraction and generates a specialized medical thesaurus. The hybrid method leaves the thesaurus quality on the same level and finds more relations between terms than well-known algorithms.

A Rule-Based Meronymy Extraction Module for Portuguese

In this article, we improve the extraction of semantic relations between textual elements as it is currently performed by STRING, a hybrid statistical and rulebased Natural Language Processing (NLP) chain for Portuguese, by targeting whole-part relations (meronymy), that is, a semantic relation between an entity that is perceived as a constituent part of another entity, or a member of a set. In this case, we focus on the type of meronymy involving human entities and body-part nouns (Nbp) (e.g., O Pedro partiu uma perna `Pedro broke a leg': WHOLE-PART(Pedro,perna) 'WHOLE-PART(Pedro,leg)'). In order to extract this type of whole-part relations, a rule-based meronymy extraction module has been built and integrated in the grammar of the STRING system. The module was evaluated with promising results.

Automatic extraction and visualization of semantic relations between medical entities from medicine instructions

Recent years have witnessed the rapid development and tremendous research interests in healthcare domain. The health and medical knowledge can be acquired from many sources, such as professional health providers, health community generated data and textual descriptions of medicines. This paper explores the classification and extraction of semantic relation between medical entities from the unstructured medicine Chinese instructions. In this paper, three kinds of textual features are extracted from medicine instruction according to the nature of natural language texts. And then, a support vector machine based classification model is proposed to categorize the semantic relations between medical entities into the corresponding semantic relation types. Finally, the extraction algorithm is utilized to obtain the semantic relation triples. This paper also visualizes the semantic relations between medical entities with relationship graph for their future processing. The experimental results show that the approach proposed in this paper is effective and efficient in the classification and extraction of semantic relations between medical entities.

Automatic Extraction of Semantic Relations from Wikipedia

We introduce a novel approach to extract semantic relations (e.g., is-a and part-of relations) from Wikipedia articles. These relations are used to build up a large and up-to-date thesaurus providing background knowledge for tasks such as determining semantic ontology mappings. Our automatic approach uses a comprehensive set of semantic patterns, finite state machines and NLP techniques to extract millions of relations between concepts. An evaluation for different domains shows the high quality and effectiveness of the proposed approach. We also illustrate the value of the newly found relations for improving existing ontology mappings.

Extracting Various Classes of Data From Biological Text Using the Concept of Existence Dependency.

One of the key goals of biological natural language processing (NLP) is the automatic information extraction from biomedical publications. Most current constituency and dependency parsers overlook the semantic relationships between the constituents comprising a sentence and may not be well suited for capturing complex long-distance dependences. We propose in this paper a hybrid constituency-dependency parser for biological NLP information extraction called EDCC. EDCC aims at enhancing the state of the art of biological text mining by applying novel linguistic computational techniques that overcome the limitations of current constituency and dependency parsers outlined earlier, as follows: 1) it determines the semantic relationship between each pair of constituents in a sentence using novel semantic rules; and 2) it applies a semantic relationship extraction model that extracts information from different structural forms of constituents in sentences. EDCC can be used to extract different types of data from biological texts for purposes such as protein function prediction, genetic network construction, and protein-protein interaction detection. We evaluated the quality of EDCC by comparing it experimentally with six systems. Results showed marked improvement.

Luščenje definicijskih kandidatov iz specializiranih korpusov

Predstavljamo metodo za luščenje definicij iz specializiranih korpusov. Metoda je bila razvita za slovenščino in angleščino, sestavljajo pa jo trije pristopi: v prvem definicije luščimo z leksikoskladenjskimi vzorci, drugi uporablja avtomatsko izluščeno terminologijo, tretji pa lušči stavke, v katerih se nahajata pojem in njegova nadpomenka iz semantičnega leksikona wordnet. Metodologijo smo preizkusili na primeru področja jezikovnih tehnologij. Za namene modeliranja izbranega področja smo zgradili primerljivi slovensko-angleški Korpus jezikovnih tehnologij, izluščene kandidate pa smo uporabili pri gradnji Glosarja jezikovnih tehnologij. Celotno metodologijo smo strnili v prosto dostopen delotok, implementiran v spletnem okolju za gradnjo delotokov Clowdflows. V delotok lahko uporabnik prek spleta naloži korpus v različnih formatih, ga jezikoslovno označi, izlušči terminologijo in kandidate za definicije ter rezultate vizualizira ali shrani.

Association Rule Mining Based Extraction of Semantic Relations Using Markov Logic Network

Ontology may be a conceptualization of a website into a human understandable, however machinereadable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.

Association Rule Mining Based Extraction of Semantic Relations Using Markov Logic Network

Association Rule Mining Based Extraction of Semantic Relations Using Markov Logic Network

Automatic extraction of ontological relations from Arabic text

Automatic extraction of semantic relationships among Arabic concepts to formulate ontology models is crucial for providing rich semantic metadata. Due to the annual increase of Arabic content on the Internet, the need for specialized tools to analyze and understand Arabic text has emerged. This research proposes a methodology that extracts ontological relationships. The goals of this research are: to extract semantic features of Arabic text, propose syntactic patterns of relationships among concepts, and propose a formal model of extracting ontological relations.The proposed methodology has been designed to analyze Arabic text using lexical semantic patterns of the Arabic language according to a set of features. Next, the features have been abstracted and enriched with formal descriptions for the purpose of generalizing the resulted rules. The rules, then, have formulated a classifier that accepts Arabic text, analyzes it, and then displays related concepts labeled with its designated relationship. Moreover, to resolve the ambiguity of homonyms, a set of machine translation, text mining, and part of speech tagging algorithms have been reused. We performed extensive experiments to measure the effectiveness of our proposed tools. The results indicate that our proposed methodology is promising for automating the process of extracting ontological relations.

Composition of semantic relations

Extracting semantic relations from text is a preliminary step towards understanding the meaning of text. The more semantic relations are extracted from a sentence, the better the representation of the knowledge encoded into that sentence. This article introduces a framework for the Composition of Semantic Relations (CSR). CSR aims to reveal more text semantics than existing semantic parsers by composing new relations out of previously extracted relations. Semantic relations are defined using vectors of semantic primitives, and an algebra is suggested to manipulate these vectors according to a CSR algorithm. Inference axioms that combine two relations and yield another relation are generated automatically. CSR is a language-agnostic, inventory-independent method to extract semantic relations. The formalism has been applied to a set of 26 well-known relations and results are reported.

Automatic generation of multiple choice questions using dependency-based semantic relations

In this paper, we present an unsupervised dependency-based approach to extract semantic relations to be applied in the context of automatic generation of multiple choice questions (MCQs). MCQs also known as multiple choice tests provide a popular solution for large-scale assessments as they make it much easier for test-takers to take tests and for examiners to interpret their results. Manual generation of MCQs is a very expensive and time-consuming task and yet they often need to be produced on a large scale and within short iterative cycles. We approach the problem of automated MCQ generation with the help of unsupervised relation extraction, a technique used in a number of related natural language processing problems. The goal of Unsupervised relation extraction is to identify the most important named entities and terminology in a document and then recognise semantic relations between them, without any prior knowledge as to the semantic types of the relations or their specific linguistic realisation. We use these techniques to process instructional texts and identify those facts (terminology, entities, and semantic relations between them) that are likely to be important for assessing test-takers' familiarity with the instructional material. We investigate an approach to learn semantic relations between named entities by employing a dependency tree model. Our findings show that an optimised configuration of our MCQ generation system is capable of attaining high precision rates, which are much more important than recall in the automatic generation of MCQs. We also carried out a user-centric evaluation of the system, where subject domain experts evaluated automatically generated MCQ items in terms of readability, usefulness of semantic relations, relevance, acceptability of questions and distractors and overall MCQ usability. The results of this evaluation make it possible for us to draw conclusions about the utility of the approach in practical e-Learning applications.

Semi-automatic Domain Ontology Construction from Spoken Corpus in Tunisian Dialect: Railway Request Information

In this paper, we present a hybrid method for semi-automatic building of domain ontology from spoken dialogue corpus in Tunisian Dialect for the railway request information domain. The proposed method is based on a statistical method for term and concept extraction and a linguistic method for semantic relation extraction. This method consists of three fundamental phases, namely the corpus construction and treatment, the ontology construction and the ontology evaluation. The proposed method is implemented through the ABDO system to generate the RIO ontology that contains 14 concepts, 25 semantic relations and 387 concepts instances. The generated domain ontology is used to semantically label Tunisian dialect utterances in spoken dialogue.

Automatic Topic Ontology Construction Using Semantic Relations from WordNet and Wikipedia

Due to the explosive growth of web technology, a huge amount of information is available as web resources over the Internet. Therefore, in order to access the relevant content from the web resources effectively, considerable attention is paid on the semantic web for efficient knowledge sharing and interoperability. Topic ontology is a hierarchy of a set of topics that are interconnected using semantic relations, which is being increasingly used in the web mining techniques. Reviews of the past research reveal that semiautomatic ontology is not capable of handling high usage. This shortcoming prompted the authors to develop an automatic topic ontology construction process. However, in the past many attempts have been made by other researchers to utilize the automatic construction of ontology, which turned out to be challenging due to time, cost and maintenance. In this paper, the authors have proposed a corpus based novel approach to enrich the set of categories in the ODP by automatically identifying the concepts and their associated semantic relationship with corpus based external knowledge resources, such as Wikipedia and WordNet. This topic ontology construction approach relies on concept acquisition and semantic relation extraction. A Jena API framework has been developed to organize the set of extracted semantic concepts, while Protégé provides the platform to visualize the automatically constructed topic ontology. To evaluate the performance, web documents were classified using SVM classifier based on ODP and topic ontology. The topic ontology based classification produced better accuracy than ODP.

Effective Tag Recommendation System Based on Topic Ontology UsingWikipediaand WordNet

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.