In this article, we describe a conceptual model for video games and interactive media. Existing conceptual models such as the Functional Requirements for Bibliographic Records (FRBR) are not adequate to represent the unique descriptive attributes, levels of variance, and relationships among video games. Previous video game-specific models tend to focus on the development of video games and their technical aspects. Our model instead attempts to reflect how users such as game players, collectors, and scholars understand video games and the relationships among them. We specifically consider use cases of gamers, with future intentions of using this conceptual model as a foundation for developing a union catalog for various libraries and museums. In the process of developing the model, we encountered many challenges, including conceptual overlap with and divergence from FRBR, entity scoping, complex relationships among entities, and the question of how to model additional content for game expansion. Future work will focus on making this model interoperable with existing ontologies as well as further understanding and description of content and relationships.

The Synthetic Biology Knowledge System (SBKS) is an instance of the SynBioHub repository that includes text and data information that has been mined from papers published in ACS Synthetic Biology. This paper describes the SBKS curation framework that is being developed to construct the knowledge stored in this repository. The text mining pipeline performs automatic annotation of the articles using natural language processing techniques to identify salient content such as key terms, relationships between terms, and main topics. The data mining pipeline performs automatic annotation of the sequences extracted from the supplemental documents with the genetic parts used in them. Together these two pipelines link genetic parts to papers describing the context in which they are used. Ultimately, SBKS will reduce the time necessary for synthetic biologists to find the information necessary to complete their designs.

Textual entailment is a relationship that obtains between fragments of text when one fragment in some sense implies the other fragment. The automation of textual entailment recognition supports a wide variety of textbased tasks, including information retrieval, information extraction, question answering, text summarization, and machine translation. Much ingenuity has been devoted to developing algorithms for identifying textual entailments, but relatively little to saying what textual entailment actually is. This article is a review of the logical and philosophical issues involved in providing an adequate definition of textual entailment. We show that many natural definitions of textual entailment are refuted by counterexamples, including the most widely cited definition of Dagan et al. We then articulate and defend the following revised definition: T textually entails H 5 df typically, a human reading T would be justified in inferring the proposition expressed by H from the proposition expressed by T. We also show that textual entailment is context-sensitive, nontransitive, and nonmonotonic.

Anime is increasingly becoming recognized as an important commercial product and cultural artifact. However, little is known regarding users' information needs and behavior related to anime. This study specifically attempts to improve our understanding of how people seek anime recommendations. We analyzed 546 user questions in natural language, collected from a Korean Q&A website Naver Knowledge-iN, where users are asking for anime recommendations. The findings suggest the importance of establishing robust metadata for the seven commonly used features for anime recommenders (i.e., title, genre, artistic style, story, character description, series title, and mood) in digital libraries, as well as allowing users to specify known anime and series titles as examples for seeking similar items, or examples of the kinds of items to be excluded.

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