Search

Over the last decade, extensive research has been done on automatic construction of knowledge graphs from Web resources, resulting in a number of large-scale knowledge graphs such as YAGO, DBpedia, BabelNet, and Wikidata. Despite that some of these knowledge graphs are multilingual, they contain few or no linked data in Persian, and do not support tools for extracting knowledge from Persian information sources. FarsBase (available at http://farsbase.net/about) is the first Persian multi-source knowledge graph, which is specifically designed for semantic search engines to support Persian knowledge. FarsBase uses a diverse set of hybrid and flexible techniques to extract and integrate knowledge from various sources, such as Wikipedia, Web tables and unstructured texts. It also supports entity linking, which allows integration with other knowledge graphs. To maintain high accuracy for triples, we adopt a low-cost mechanism for verifying candidate knowledge by human experts, where the candidates for human verification are prioritized using different heuristics. FarsBase is being used as the semantic-search system of a Persian search engine and efficiently answers hundreds of semantic queries per second. [ABSTRACT FROM AUTHOR]

Most of machine learning and data mining techniques only focus on finding patterns satisfying technical interestingness measures. They do not consider business interestingness measures in the mining process, so most of mined patterns may not be usable in corresponding domain. Moreover, the techniques do not interpret the patterns in order that business people know what actions to take to support their business decisions. They require a considerable amount of extra work by domain experts to interpret the patterns. Actionable Knowledge Discovery techniques deliver technical and business significant patterns and support automatically converting patterns into deliverables in business-friendly and operable forms such as actions. In this paper, we incorporate fuzzy set theory and attribute flexibility for the enhancement fuzzy cost-effective action mining algorithm (F-CEAMA). We present an algorithm that suggests actions regarding to their practical applicability. The algorithm takes into account attribute flexibility and fuzzy cost of actions and attempts to maximize the fuzzy net profit. The contribution of the work is in taking the output from fuzzy decision tree, using flexibility of its attributes, and producing novel and actionable knowledge through automatic fuzzy post processing. The performance of proposed algorithm is compared to F-CEAMA using several real-life datasets taken from the UCI Machine Learning Repository. Experimental results show that the proposed algorithm outperforms F-CEAMA not only in finding more actions but also in finding actions with more fuzzy net profit. [ABSTRACT FROM AUTHOR]