Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning

Many cities in the world are besieged by municipal solid waste (MSW). MSW not only pollutes the ecological environment but can even induce a series of public safety crises. Risk response for MSW needs novel changes. This paper innovatively adopts the ideas and methods of semantic web ontology to build an ontology-based reasoning system for MSW risk response. Through the integration of crisis information and case resources in the field of MSW, combined with the reasoning ability of Semantic Web Rule Language (SWRL), a system of rule reasoning for risk transformation is constructed. Knowledge extraction and integration of MSW risk response can effectively excavate semantic correlation of crisis information along with key transformation points in the process of crisis evolution through rule reasoning. The results show that rule reasoning of transformation can effectively improve intelligent decision-making regarding MSW risk response

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover

A hybrid NLP & semantic knowledgebase approach for the intelligent exploration of Arabic documents

In the contemporary era, a colossal amount of information is published daily on the Web in the form of articles, documents, reviews, blogs and social media posts. As most of this data is available in the form of unstructured documents, it makes it challenging and timeconsuming to extract non-trivial, previously unknown, and potentially useful knowledge from the published documents. Hence, extracting useful knowledge from unstructured text, i.e., Information Extraction, is becoming an increasingly significant aspect of knowledge discovery. This work focuses on Information Extraction form Arabic unstructured text, which is an especially challenging task as Arabic is a highly inflectional and derivational language. The problem is compounded by the lack of mature tools and advanced research in Arabic Natural Language Processing (NLP) in comparison to European languages for instance. The principal objective of this research work is presenting a comprehensive methodology for integrating domain knowledge with Natural Language Processing techniques that were proven effective in solving most classification problems in order to improve the Information extraction process form online unstructured data. The importance of NLP tools lies in that they play a key role in allowing semantic concept tagging of unstructured text, and so realize the Semantic Web. This work presents a novel rule-based approach that uses linguistic grammar-based techniques to extract Arabic composite names from Arabic text. Our approach uniquely exploits the genitive Arabic grammar rules; in particular, the rules regarding the identification of definite nouns (معرفة) and indefinite nouns (نكرة) to support the process of extracting composite names. Furthermore, this approach does not place any constraints on the length of the Arabic composite name. The results of our experiments show that there are improvement in recognizing Arabic composite names entity in the Arabic language text. Our research also contributes a novel, knowledge-based approach to relation extraction from unstructured Arabic text, which is based on the principles of Functional Discourse Grammar (FDG). We further improve the approach by integrating it with Machine Learning relation classification, resulting in a hybrid relation extraction algorithm that can handle especially complex Arabic sentence structures. The accuracy of our relation classification efforts was extensively evaluated by means of experimental evaluation that evidenced the accuracy of the FDG relation extraction approach and the improvement gained by the Machine Learning integration. The essential NLP algorithms of entity recognition and relation extraction were deployed in a Semantic Knowledge-base that was built from the outset to model the knowledge of the problem domain. The semantic modelling of the knowledgebase aided improving the accuracy of the NLP algorithms by leveraging relevant domain knowledge published in Open Linked Datasets. Moreover, the extracted information was semantically tagged and inserted into the Semantic Knowledge-base, which facilitated building advanced rules to infer new interesting information from the extracted knowledge as well as utilising advanced query mechanisms for intelligently exploring the mined problem domain knowledge

Improve and Implement an Open Source Question Answering System

A question answer system takes queries from the user in natural language and returns a short concise answer which best fits the response to the question. This report discusses the integration and implementation of question answer systems for English and Hindi as part of the open source search engine Yioop. We have implemented a question answer system for English and Hindi, keeping in mind users who use these languages as their primary language. The user should be able to query a set of documents and should get the answers in the same language. English and Hindi are very different when it comes to language structure, characters etc. We have implemented the Question Answer System so that it supports localization and improved Part of Speech tagging performance by storing the lexicon in the database instead of a file based lexicon. We have implemented a brill tagger variant for Part of Speech tagging of Hindi phrases and grammar rules for triplet extraction. We also improve Yioop’s lexical data handling support by allowing the user to add named entities. Our improvements to Yioop were then evaluated by comparing the retrieved answers against a dataset of answers known to be true. The test data for the question answering system included creating 2 indexes, 1 each for English and Hindi. These were created by configuring Yioop to crawl 200,000 wikipedia pages for each crawl. The crawls were configured to be domain specific so that English index consists of pages restricted to English text and Hindi index is restricted to pages with Hindi text. We then used a set of 50 questions on the English and Hindi systems. We recored, Hindi system to have an accuracy of about 55% for simple factoid questions and English question answer system to have an accuracy of 63%

Sample-based XPath Ranking for Web Information Extraction

Web information extraction typically relies on a wrapper, i.e., program code or a configuration that specifies how to extract some information from web pages at a specific website. Manually creating and maintaining wrappers is a cumbersome and error-prone task. It may even be prohibitive as some applications require information extraction from previously unseen websites. This paper approaches the problem of automatic on-the-fly wrapper creation for websites that provide attribute data for objects in a ‘search – search result page – detail page’ setup. The approach is a wrapper induction approach which uses a small and easily obtainable set of sample data for ranking XPaths on their suitability for extracting the wanted attribute data. Experiments show that the automatically generated top-ranked XPaths indeed extract the wanted data. Moreover, it appears that 20 to 25 input samples suffice for finding a suitable XPath for an attribute