Intelligent question answering systems provide specific answers to users' questions. This paper describes a unique AI system that uses online knowledge basesand reasoning tools to answers questions posed by the Sporcle quiz site.

The scalability of optimal sequential planning can be improved by using external-memory graph search. State-of-the-art external-memory graph search algorithms rely on a state-space projection function, or hash function, that partitions the stored nodes of the state-space search graph into groups of nodes that are stored as separate files on disk. Search performance depends on properties of the partition; whether the number of unique nodes in a file always fits in RAM, the number of files into which the nodes of the state-space graph are partitioned, and how well the partition captures local structure in the graph. Previous work relies on a static partition of the state space, but it can be difficult for a static partition to simultaneously satisfy all of these criteria. We introduce a method for dynamic partitioning and show that it leads to improved search performance in solving STRIPS planning problems.

We investigate two publicly available web knowledge bases, Wikipedia and Yago, in an attempt to leverage semantic information and increase the performance level of a state-of-the-art coreference resolution (CR) engine. We extract semantic compatibility and aliasing information from Wikipedia and Yago, and incorporate it into a CR system. We show that using such knowledge with no disambiguation and filtering does not bring any improvement over the baseline, mirroring the previous findings. We propose, therefore, a number of solutions to reduce the amount of noise coming from web resources: using disambiguation tools for Wikipedia, pruning Yago to eliminate the most generic categories and imposing additional constraints on affected mentions. Our evaluation experiments on the ACE-02 corpus show that the knowledge, extracted from Wikipedia and Yago, improves our system's performance by 2-3 percentage points.

The primary goal of this study is to assess two approaches for detecting comprehension processes in R-SAT (Reading Strategy Assessment Tool). One approach is based on Latent Semantic Analysis (LSA) while the other is a combination of literal word matching and soundex. A secondary goal is to assess the potential for detecting specific reading comprehension strategies, either in isolation or combination. Participants typed “think-aloud” protocols while reading texts presented on computers. Human judges rated these protocols for the presence of the various reading comprehension strategies. LSA, word, and combined algorithms were compared and the results showed that a combination of both approaches yielded the best results. However, performance of the combined algorithm varied in terms of the type of processes and the grain size of the human coding system. Lastly, the use of reading strategies (either in isolation or combination) is positivity related to students’ Gates–MacGinitie reading comprehension scores, which illustrates the merit of this approach for assessing comprehension skill.

However, word concreteness is not an attribute that a A single word in the human language has many complex computer can directly compute. One means of assessing dimensions such as semantics, parts of speech, lexical type, the characteristics of words is by having humans rate them imagability, concreteness, familiarity, etc. It is important to on the dimensions of interest. Humans are proficient in know the dimensions of words in languages so that we can categorizing words into linguistic dimensions, but it is develop a better theoretical understanding of language and impractical to have humans rating tens of thousands of also to build tools that simulate human intelligence and words that we would need for psycholinguistic research.

This study presents an evaluation of WordNet-based semantic similarity and relatedness measures in tasks focused on concept similarity. Assuming similarity as distinct from relatedness, the goal is to fill a gap within the current body of work in the evaluation of similarity and relatedness measures. Past studies have either focused entirely on relatedness or only evaluated judgments over words rather than concepts. In this study, first, concept similarity measures are evaluated over human judgments by using existing sets of word similarity pairs that we annotated with word senses. Next, an application-oriented study is presented by integrating similarity and relatedness measures into an algorithm which relies on concept similarity. Interestingly, the results find metrics categorized as measuring relatedness to be strongest in correlation with human judgments of concept similarity, though the difference in correlation is small. On the other hand, an information content metric, categorized as measuring similarity, is notably strongest according to the application-oriented evaluation.

With the rapid development of technologies for fast access to the Internet and the popularization of digital cameras, enormous digital images are posted and shared online everyday. Simultaneously, web images are usually organized by topics of events and are often assigned appropriate topic-related text descriptions. Given a set of images along with corresponding texts, a challenging problem is how to utilize the available information to perform image retrieval tasks, such as image classification and image clustering. Previous works on image categorization focus on either adopting text or image features, or simply combining these two types of information together. In this paper, we propose two novel approaches (Dynamic Weighting and Region-based Semantic Concept Integration) to categorize the images under the "supervision" of topic-related text descriptions; In addition, we provide a comparative experimental investigation on utilizing text and image information to tackle image classification. Empirical experiments on a manually collected image dataset (consisting of images related to the events after disasters) demonstrate the efficacy of our proposed classification methods.

We describe a software tool to select among camera feeds from multiple virtual cameras in a virtual environment using semiring constraint satisfaction problem techniques (SCSP), a soft constraint approach. We show how to encode a designer's preferences, and select the best camera feed even in over-constrained or under-constrained environments. The system functions in real time for dynamic scenes, using only current information (ie. no prediction). To reduce computation costs for a final implementation, the SCSP evaluation can be cached and converted to native code. Our approach is implemented in two virtual environments: a virtual hockey game using a spectator viewpoint, and a virtual 3D maze game using a third person perspective. Comparisons against hard constraints (constraint satisfaction problems) are made.

Considering directed graphs on n vertices and m edges with real (possibly negative) weights, we present two new, efficient algorithms for computing all-pairs shortest paths (APSP). These algorithms make use of directed path consistency (DPC) along a vertex ordering d. The algorithms run in O(n 2 w d ) time, where w d is the graph width induced by this vertex ordering. For graphs of constant treewidth, this yields O(n 2 ) time, which is optimal. On chordal graphs, the algorithms run in O(nm) time. We show empirically that also in many general cases, both constructed and from realistic benchmarks, the algorithms often outperform Johnson's algorithm, which represents the current state of the art with a run time of O(nm + n 2 log n). These algorithms can be used for temporal and spatial reasoning, e.g. for the Simple Temporal Problem (STP), which underlines its relevance to the planning and scheduling community.

Ontologies have been criticized because they are not sufficiently flexible, and thus cannot capture the dynamism and complexity of reality. However, they have increasingly come into focus because of the need for knowledge management in both general and specialized knowledge domains. EcoLexicon is a frame-based visual thesaurus on the environment that is gradually evolving towards the status of a formal ontology. For this purpose, the information in its relational database is in the process of being linked to the ontological system of FunGramKB, a multipurpose knowledge base that has been specifically designed for natural language understanding with modules for lexical, grammatical, and conceptual knowledge. This enables the explicitation of specialized knowledge as an extension of general knowledge through its representation in the domain-specific satellite ontology of a main general ontology.