Knowledge bases (KBs) have attracted increasing attention due to its great success in various areas, such as Web and mobile search.Existing KBs are restricted to objective factual knowledge, such as city population or fruit shape, whereas,subjective knowledge, such as big city, which is commonly mentioned in Web and mobile queries, has been neglected. Subjective knowledge differs from objective knowledge in that it has no documented or observed ground truth. Instead, the truth relies on people's dominant opinion. Thus, we can use the crowdsourcing technique to get opinion from the crowd. In our work, we propose a system, called crowdsourced subjective knowledge acquisition (CoSKA),for subjective knowledge acquisition powered by crowdsourcing and existing KBs. The acquired knowledge can be used to enrich existing KBs in the subjective dimension which bridges the gap between existing objective knowledge and subjective queries.The main challenge of CoSKA is the conflict between large scale knowledge facts and limited crowdsourcing resource. To address this challenge, in this work, we define knowledge inference rules and then select the seed knowledge judiciously for crowdsourcing to maximize the inference power under the resource constraint. Our experimental results on real knowledge base and crowdsourcing platform verify the effectiveness of CoSKA system.
Subjective knowledge acquisition is closely related to works that associating properties with entities. Some works have been conducted for commonsense knowledge acquisition @cite_19 @cite_29 @cite_0 @cite_26 . WebChild @cite_2 presents a method for automatically constructing a large commonsense knowledge base, it contains triples that connect nouns with adjectives via fine-grained relations. Entitytagger @cite_8 , presented by , automatically associate descriptive phrases, referred to as etags (entity tags), to each entity. Instead of , these works focus on the less controversial and more objective properties, which is not related to obtaining . The most similar work is , which mines the dominant opinion on the web content of whether a subjective property applies to a type. However, they does not consider to use the existing information in knowledge base and resorting to the crowd for subjective knowledge acquisition.
@cite_26 @cite_8 @cite_29 @cite_0 @cite_19 @cite_2
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Applications are increasingly expected to make smart decisions based on what humans consider basic commonsense. An often overlooked but essential form of commonsense involves comparisons, e.g. the fact that bears are typically more dangerous than dogs, that tables are heavier than chairs, or that ice is colder than water. In this paper, we first rely on open information extraction methods to obtain large amounts of comparisons from the Web. We then develop a joint optimization model for cleaning and disambiguating this knowledge with respect to WordNet. This model relies on integer linear programming and semantic coherence scores. Experiments show that our model outperforms strong baselines and allows us to obtain a large knowledge base of disambiguated commonsense assertions. We consider the problem of entity tagging: given one or more named entities from a specific domain, the goal is to automatically associate descriptive phrases, referred to as etags (entity tags), to each entity. Consider a product catalog containing product names and possibly short descriptions. For a product in the catalog, say Ricoh G600 Digital Camera, we want to associate etags such as "water resistant", "rugged" and "outdoor" to it, even though its name or description does not mention those phrases. Entity tagging can enable more effective search over entities. We propose to leverage signals in web documents to perform such tagging. We develop techniques to perform such tagging in a domain independent manner while ensuring high precision and high recall. 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. ConceptNet is a freely available commonsense knowledge base and natural-language-processing tool-kit which supports many practical textual-reasoning tasks over real-world documents including topic-gisting, analogy-making, and other context oriented inferences. The knowledge base is a semantic network presently consisting of over 1.6 million assertions of commonsense knowledge encompassing the spatial, physical, social, temporal, and psychological aspects of everyday life. ConceptNet is generated automatically from the 700 000 sentences of the Open Mind Common Sense Project — a World Wide Web based collaboration with over 14 000 authors. This paper presents a method for automatically constructing a large commonsense knowledge base, called WebChild, from Web contents. WebChild contains triples that connect nouns with adjectives via fine-grained relations like hasShape, hasTaste, evokesEmotion, etc. The arguments of these assertions, nouns and adjectives, are disambiguated by mapping them onto their proper WordNet senses. Our method is based on semi-supervised Label Propagation over graphs of noisy candidate assertions. We automatically derive seeds from WordNet and by pattern matching from Web text collections. The Label Propagation algorithm provides us with domain sets and range sets for 19 different relations, and with confidence-ranked assertions between WordNet senses. Large-scale experiments demonstrate the high accuracy (more than 80 percent) and coverage (more than four million fine grained disambiguated assertions) of WebChild.