Fiction and fantasy are archetypes of long-tail domains that lack comprehensive methods for automated language processing and knowledge extraction. We present ENTYFI, the first methodology for typing entities in fictional texts coming from books, fan communities or amateur writers. ENTYFI builds on 205 automatically induced high-quality type systems for popular fictional domains, and exploits the overlap and reuse of these fictional domains for fine-grained typing in previously unseen texts. ENTYFI comprises five steps: type system induction, domain relatedness ranking, mention detection, mention typing, and type consolidation. The recall-oriented typing module combines a supervised neural model, unsupervised Hearst-style and dependency patterns, and knowledge base lookups. The precision-oriented consolidation stage utilizes co-occurrence statistics in order to remove noise and to identify the most relevant types. Extensive experiments on newly seen fictional texts demonstrate the quality of ENTYFI.

Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin.

Knowledge graphs have become a fundamental asset for search engines. A fair amount of user queries seek information on problem-solving tasks such as building a fence or repairing a bicycle. However, knowledge graphs completely lack this kind of how-to knowledge. This paper presents a method for automatically constructing a formal knowledge base on tasks and task-solving steps, by tapping the contents of online communities such as WikiHow. We employ Open-IE techniques to extract noisy candidates for tasks, steps and the required tools and other items. For cleaning and properly organizing this data, we devise embedding-based clustering techniques. The resulting knowledge base, HowToKB, includes a hierarchical taxonomy of disambiguated tasks, temporal orders of sub-tasks, and attributes for involved items. A comprehensive evaluation of HowToKB shows high accuracy. As an extrinsic use case, we evaluate automatically searching related YouTube videos for HowToKB tasks.