Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate that this is mainly attributed to the lack of a comprehensive repository of size information. In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web. By maximizing the joint likelihood of textual and visual observations, our method learns reliable relative size estimates, with no explicit human supervision. We introduce the relative size dataset and show that our method outperforms competitive textual and visual baselines in reasoning about size comparisons.
A few researchers @cite_16 @cite_1 use manually curated commonsense knowledge base such as OpenCyc @cite_32 for answering questions about numerical information. These knowledge resources (e.g., ConceptNet @cite_20 ) usually consist of taxonomic assertions or generic relations, but do not include size information. Manual annotations of such knowledge is not scalable. Our efforts will result in extracting size information to populate such knowledge bases (esp. ConceptNet) with size information at scale.
@cite_16 @cite_1 @cite_32 @cite_20
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Knowledge acquisition is the essential process of extracting and encoding knowledge, both domain specific and commonsense, to be used in intelligent systems. While many large knowledge bases have been constructed, none is close to complete. This paper presents an approach to improving a knowledge base efficiently under resource constraints. Using a guiding knowledge base, questions are generated from a weak form of similarity-based inference given the glossary mapping between two knowledge bases. The candidate questions are prioritized in terms of the concept coverage of the target knowledge. Experiments were conducted to find questions to grow the Chinese ConceptNet using the English ConceptNet as a guide. The results were evaluated by online users to verify that 94.17 of the questions and 85.77 of the answers are good. In addition, the answers collected in a six-week period showed consistent improvement to a 36.33 increase in concept coverage of the Chinese commonsense knowledge base against the English ConceptNet. We propose a novel algorithm for inducing semantic taxonomies. Previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discovered textual patterns. By contrast, our algorithm flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowledge of a word's coordinate terms to help in determining its hypernyms, and vice versa. We apply our algorithm on the problem of sense-disambiguated noun hyponym acquisition, where we combine the predictions of hypernym and coordinate term classifiers with the knowledge in a preexisting semantic taxonomy (WordNet 2.1). We add 10,000 novel synsets to WordNet 2.1 at 84 precision, a relative error reduction of 70 over a non-joint algorithm using the same component classifiers. Finally, we show that a taxonomy built using our algorithm shows a 23 relative F-score improvement over WordNet 2.1 on an independent testset of hypernym pairs. Recently, large-scale knowledge bases have been constructed by automatically extracting relational facts from text. Unfortunately, most of the current knowledge bases focus on static facts and ignore the temporal dimension. However, the vast majority of facts are evolving with time or are valid only during a particular time period. Thus, time is a significant dimension that should be included in knowledge bases. In this paper, we introduce a complete information extraction framework that harvests temporal facts and events from semi-structured data and free text of Wikipedia articles to create a temporal ontology. First, we extend a temporal data representation model by making it aware of events. Second, we develop an information extraction method which harvests temporal facts and events from Wikipedia infoboxes, categories, lists, and article titles in order to build a temporal knowledge base. Third, we show how the system can use its extracted knowledge for further growing the knowledge base. We demonstrate the effectiveness of our proposed methods through several experiments. We extracted more than one million temporal facts with precision over 90 for extraction from semi-structured data and almost 70 for extraction from text. Cross-document coreference, the task of grouping all the mentions of each entity in a document collection, arises in information extraction and automated knowledge base construction. For large collections, it is clearly impractical to consider all possible groupings of mentions into distinct entities. To solve the problem we propose two ideas: (a) a distributed inference technique that uses parallelism to enable large scale processing, and (b) a hierarchical model of coreference that represents uncertainty over multiple granularities of entities to facilitate more effective approximate inference. To evaluate these ideas, we constructed a labeled corpus of 1.5 million disambiguated mentions in Web pages by selecting link anchors referring to Wikipedia entities. We show that the combination of the hierarchical model with distributed inference quickly obtains high accuracy (with error reduction of 38 ) on this large dataset, demonstrating the scalability of our approach.