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Wikipedia-based Semantic Interpretation for Natural Language Processing  [PDF]
Evgeniy Gabrilovich,Shaul Markovitch
Computer Science , 2014, DOI: 10.1613/jair.2669
Abstract: Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.
A semi-automatic semantic method for mapping SNOMED CT concepts to VCM Icons  [PDF]
Jean-Baptiste Lamy,Rosy Tsopra,Alain Venot,Catherine Duclos
Computer Science , 2013,
Abstract: VCM (Visualization of Concept in Medicine) is an iconic language for representing key medical concepts by icons. However, the use of this language with reference terminologies, such as SNOMED CT, will require the mapping of its icons to the terms of these terminologies. Here, we present and evaluate a semi-automatic semantic method for the mapping of SNOMED CT concepts to VCM icons. Both SNOMED CT and VCM are compositional in nature; SNOMED CT is expressed in description logic and VCM semantics are formalized in an OWL ontology. The proposed method involves the manual mapping of a limited number of underlying concepts from the VCM ontology, followed by automatic generation of the rest of the mapping. We applied this method to the clinical findings of the SNOMED CT CORE subset, and 100 randomly-selected mappings were evaluated by three experts. The results obtained were promising, with 82 of the SNOMED CT concepts correctly linked to VCM icons according to the experts. Most of the errors were easy to fix.
Semi-Automatic Construction of a Domain Ontology for Wind Energy Using Wikipedia Articles  [PDF]
Dilek Kü?ük,Yusuf Arslan
Computer Science , 2014, DOI: 10.1016/j.renene.2013.08.002
Abstract: Domain ontologies are important information sources for knowledge-based systems. Yet, building domain ontologies from scratch is known to be a very labor-intensive process. In this study, we present our semi-automatic approach to building an ontology for the domain of wind energy which is an important type of renewable energy with a growing share in electricity generation all over the world. Related Wikipedia articles are first processed in an automated manner to determine the basic concepts of the domain together with their properties and next the concepts, properties, and relationships are organized to arrive at the ultimate ontology. We also provide pointers to other engineering ontologies which could be utilized together with the proposed wind energy ontology in addition to its prospective application areas. The current study is significant as, to the best of our knowledge, it proposes the first considerably wide-coverage ontology for the wind energy domain and the ontology is built through a semi-automatic process which makes use of the related Web resources, thereby reducing the overall cost of the ontology building process.
Automatic Semantic Domain ontology Populator (ASDP)  [cached]
A. K. Sharma,Prashant Dixit
International Journal of Advancements in Technology , 2012,
Abstract: Ontologies play a major role in supporting information exchange processes in various areas. At present, ontologies are applied to the World Wide Web for creation of semantic web. The main application area of ontology technology is Knowledge Management. In the present scenario, it is difficult to acquire knowledge and then to maintain knowledge in a given domain. Manual ontology population is labour intensive and time consuming. Hence there is need to devise a method to provide fully automatic feeding of Web-based knowledge to the ontology. Moreover, for constructing ontology automatically, there is a need to discover a way to find, structure, and display the relationships between attributes and objects of a sentence. In this paper, a technique of Automatic Semantic Domain-ontology Populator (ASDP) towards construction of given domain modeled by the database is being proposed. ASDP is a way to find, structure, and display relationships between concepts, which consist of attributes and objects. This method helps in understanding a given domain and in building a domain model for it.
Semantic Content Filtering with Wikipedia and Ontologies  [PDF]
Pekka Malo,Pyry Siitari,Oskar Ahlgren,Jyrki Wallenius,Pekka Korhonen
Computer Science , 2010,
Abstract: The use of domain knowledge is generally found to improve query efficiency in content filtering applications. In particular, tangible benefits have been achieved when using knowledge-based approaches within more specialized fields, such as medical free texts or legal documents. However, the problem is that sources of domain knowledge are time-consuming to build and equally costly to maintain. As a potential remedy, recent studies on Wikipedia suggest that this large body of socially constructed knowledge can be effectively harnessed to provide not only facts but also accurate information about semantic concept-similarities. This paper describes a framework for document filtering, where Wikipedia's concept-relatedness information is combined with a domain ontology to produce semantic content classifiers. The approach is evaluated using Reuters RCV1 corpus and TREC-11 filtering task definitions. In a comparative study, the approach shows robust performance and appears to outperform content classifiers based on Support Vector Machines (SVM) and C4.5 algorithm.
Automatic semantic annotation based on ontology and knowledge base
基于本体知识库的自动语义标注*

QI Xin,XIAO Min,SUN Jian-peng,
戚欣
,肖敏,孙建鹏

计算机应用研究 , 2011,
Abstract: In order to generate the metadata of semantic web, semantic information need be extracted. Facing the mass scale of web documents, Compared to artificial or semi-automatic semantic annotation, automatic semantic annotation is a feasible method. To recognize candidate named entities, the semantic dictionary is designed and semantic distance between entities is calculated by semantic relevance path. The most complex problem in semantic annotation is semantic disambiguation. A semantic disambiguation method based on the shortest path and n-gram is proposed. After semantic annotation using this method, the results need to be converted to semantic indexes for semantic information retrieval. Experiments have been made on a news corpus. The result shows that the method is effective for the task of automatic semantic annotation.
Semantic Measures for the Comparison of Units of Language, Concepts or Instances from Text and Knowledge Base Analysis  [PDF]
Sébastien Harispe,Sylvie Ranwez,Stefan Janaqi,Jacky Montmain
Computer Science , 2013,
Abstract: Semantic measures are widely used today to estimate the strength of the semantic relationship between elements of various types: units of language (e.g., words, sentences, documents), concepts or even instances semantically characterized (e.g., diseases, genes, geographical locations). Semantic measures play an important role to compare such elements according to semantic proxies: texts and knowledge representations, which support their meaning or describe their nature. Semantic measures are therefore essential for designing intelligent agents which will for example take advantage of semantic analysis to mimic human ability to compare abstract or concrete objects. This paper proposes a comprehensive survey of the broad notion of semantic measure for the comparison of units of language, concepts or instances based on semantic proxy analyses. Semantic measures generalize the well-known notions of semantic similarity, semantic relatedness and semantic distance, which have been extensively studied by various communities over the last decades (e.g., Cognitive Sciences, Linguistics, and Artificial Intelligence to mention a few).
Exploring semantically-related concepts from Wikipedia: the case of SeRE  [PDF]
Daniel Hienert,Dennis Wegener,Siegfried Schomisch
Computer Science , 2015,
Abstract: In this paper we present our web application SeRE designed to explore semantically related concepts. Wikipedia and DBpedia are rich data sources to extract related entities for a given topic, like in- and out-links, broader and narrower terms, categorisation information etc. We use the Wikipedia full text body to compute the semantic relatedness for extracted terms, which results in a list of entities that are most relevant for a topic. For any given query, the user interface of SeRE visualizes these related concepts, ordered by semantic relatedness; with snippets from Wikipedia articles that explain the connection between those two entities. In a user study we examine how SeRE can be used to find important entities and their relationships for a given topic and to answer the question of how the classification system can be used for filtering.
Interactive Retrieval Based on Wikipedia Concepts  [PDF]
Lanbo Zhang
Computer Science , 2014,
Abstract: This paper presents a new user feedback mechanism based on Wikipedia concepts for interactive retrieval. In this mechanism, the system presents to the user a group of Wikipedia concepts, and the user can choose those relevant to refine his/her query. To realize this mechanism, we propose methods to address two problems: 1) how to select a small number of possibly relevant Wikipedia concepts to show the user, and 2) how to re-rank retrieved documents given the user-identified Wikipedia concepts. Our methods are evaluated on three TREC data sets. The experiment results show that our methods can dramatically improve retrieval performances.
Semantic knowledge bases construction based on Wikipedia
基于维基百科的语义知识库及其构建方法研究*

ZHANG Hai-su,MA Da-ming,DENG Zhi-long,
张海粟
,马大明,邓智龙

计算机应用研究 , 2011,
Abstract: Wikipedia is one of the largest online encyclopedias, which takes the mechanism of online cooperating editing. It is high-quantity, wide-coverage, evolving and semi-structural, and has become a well corpus of semantic knowledge bases. This paper analysed the statics and mechanism of Wikipedia, summaried the semantic knowledge bases and their construction techniques, concluding concepts and relationships mining, at last discussed the open problems in knowledge construction based on Wikipedia.
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