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Relation discovery is a crucial task in ontology learning process. The classical approaches for relation extraction, based on statistical, syntactical or pattern matching techniques, focus typically on the taxonomic aspect. The discovery of non-taxonomic relationships is often neglected. We extend these approaches by taking into account the document structure which bears additional knowledge. This...
Semantic relation plays an important role in knowledge acquisition research. This paper proposes a method of semantic relation acquisition and automatic synthesis based on Wikipedia. First of all, we obtain the three kinds of basic semantic relations from Wikipedia and extend the semantic of concept aiming at the problem of semantic fuzziness in the semantic relation. Then, an automatic synthesis...
There is a substantial body of work on the extraction of relations from texts, most of which is based on pattern matching or on applying tree kernel functions to syntactic structures. Whereas pattern application is usually more efficient, tree kernels can be superior when assessed by the F-measure. In this paper, we introduce a hybrid approach to extracting meronymy relations, which is based on both...
This paper presents the concept of surface text patterns for extracting purpose data from the web. In order to obtain an optimal set of patterns, we have developed a method for learning purpose patterns automatically. A corpus was downloaded from the Internet using bootstrapping by providing a few hand-crafted examples of each purpose pattern to a generic search engine. This corpus was then tagged...
Web-scale relation extraction is crucial to building the Web people search engines. Previous extraction models, such as Snowball, focus only on single type extraction, while the real applications always require as many as possible types of relation. In this paper, we propose a novel Web-scale relation extraction framework Multi-Type Snowball (MultiSnowball). MultiSnowball targets at extracting multiple...
In this paper we present a relation extraction system which uses a combination of pattern based, structure based and statistical approaches. This system uses raw texts and Wikipedia articles to learn conceptual relations. Wikipedia structures are rich source of information in relation extraction and are well used in this system. A set of patterns are extracted for Persian language and are used to...
Relation annotation (RA) is a process of marking up relations among a set of entities identified from a plain text. RA is important to enterprise applications due to its capability of revealing semantics in business environments. However, RA in business environment is different from that in news domain because the entities involved in the relations in business domain often not just refer to entities...
An open domain question answering system is one of the emerging information retrieval systems available on the World Wide Web that is becoming popular day by day to get succinct and relevant answers in response of users' questions. The validation of the correctness of the answer is an important issue in the field of question answering. In this paper, we are proposing a World Wide Web based solution...
The main limits of chatbot technology are related to the building of their knowledge representation and to their rigid information retrieval and dialogue capabilities, usually based on simple "pattern matching rules". The analysis of distributional properties of words in a texts corpus allows the creation of semantic spaces where represent and compare natural language elements. This space...
With the demand of accurate and domain specific bilingual dictionaries, research in the field of automatic dictionary extraction has become popular. Due to lack of domain specific terminology in parallel corpora, extraction of bilingual terminology from Wikipedia (a corpus for knowledge extraction having a huge amount of articles, links within different languages, a dense link structure and a number...
Entity Ranking (ER) is a recently emerging search task in Information Retrieval, where the goal is not finding documents matching the query words, but instead finding entities which match types and attributes mentioned in the query. In this paper we propose a formal model to define entities as well as a complete ER system, providing examples of its application to enterprise, Web, and Wikipedia scenarios...
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