Robot Task Planning in Deterministic and Probabilistic Conditions Using Semantic Knowledge Base

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.

Formal Definition and Modeling Language of Association-Oriented Database Metamodel (AssoBase)

The paper provides a concise discussion of the most important theoretical aspects of the Association-Oriented Database (AODB) Metamodel. Even though the model has been practically verified, the author has focused on its formal aspects and modeling language. The AODB Metamodel has been developed for the purposes of building the knowledge representation systems. Basically, such systems are structurally and functionally complex, hence they require advanced solutions to be applied for the purpose of data modeling. The modeling language enables designing database structures in the AODB Metamodel, taking into account various features of this database metamodel. The language in question is fully integrated and compatible with AODB Metamodel. It has been developed for the purposes of this metamodel, it operates with categories specific to it and, as such, it constitutes neither a version nor an extension of any of the existing languages. The second part of the paper provides the definition and discussion concerning the graphical modeling language — Association-Oriented Modeling Language (AML). The last section of the paper introduces the case-study that presents the key features of the metamodel, as well as the use of modeling language. The topics of presented examples comprise a simplified model of degree programs for universities and the model of Ontological Core, the main module of Semantic Knowledge Base (SKB).

We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter

Although common sense and connotative knowledge come naturally to most people, computers still struggle to perform well on tasks for which such extratextual information is required. Automatic approaches to sentiment analysis and irony detection have revealed that the lack of such world knowledge undermines classification performance. In this article, we therefore address the challenge of modeling implicit or prototypical sentiment in the framework of automatic irony detection. Starting from manually annotated connoted situation phrases (e.g., “flight delays,” “sitting the whole day at the doctor’s office”), we defined the implicit sentiment held towards such situations automatically by using both a lexico-semantic knowledge base and a data-driven method. We further investigate how such implicit sentiment information affects irony detection by assessing a state-of-the-art irony classifier before and after it is informed with implicit sentiment information.