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Mining Knowledge Bases for Question & Answers Websites

Mining Knowledge Bases for Question & Answers Websites

In the past decade, the idea of knowledge retrieval was first explored by Clark et al [1], Hermjakob et al [2], Mariano [3] and Zheng [4]. In this decade, we also saw works in this direction from Cimiano et al [5], Yao et al [6], Zhibin et al [7]. Finally, in the last years, we saw a growing number of new approaches for knowledge retrieval, like the works from Fader et al [8], Yang et al [9], Liu et al [10], Sun et al [11] and Sun et al [12]. All this e↵orts share a common approach of parsing the queries and relating them to existing ontologies. The di↵erences lies on how to reason the parsed query and how to construct the answers. A few of these e↵orts concentrated on open-domain questions, in particular the works form Zheng et al [4], Fader et al [8] and Yang et al [9]. Our work diverges from the majority of the knowledge retrieval works by using a more loose approach for representing the knowledge bases, by using the traditional TFIDF and topic-space representations of corpus.
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PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text

PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text

In this paper we follow previous research (Sawant et al., 2019; Sun et al., 2018) in deriving answers using both a corpus and a KB. We focus on tasks in which questions require compositional (sometimes called “multi-hop”) reasoning, and a setting in which the KB is incomplete, and hence must be supplemented with information extracted from text. We also restrict ourselves in this paper to answers which correspond to KB entities. For this setting, we propose an integrated framework for (1) learning what to retrieve, from either a cor- pus, a KB, or a combination, and (2) combining this heterogeneous information into a single data structure that allows the system to reason and find the best answer. In prior work, this approach was termed an early fusion approach, and shown to im- prove over late fusion methods, in which two QA systems, one corpus-based and one KB-based, are combined in an ensemble.
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A Novel approach to Crowd sourced Websites Question Answering for Medical Knowledge

A Novel approach to Crowd sourced Websites Question Answering for Medical Knowledge

In this paper, we recognize trustworthy medical diagnoses from crowdsourcing users. As these clients are not restorative specialists, the determination answers gave by them might be loud or even wrong, which may cause genuine results. With a specific end goal to distil dependable therapeutic conclusions, it is basic to recognize solid clients from inconsistent ones. Truth disclosure techniques can be embraced for such client dependability estimation. Be that as it may, existing truth revelation strategies don't consider the rich semantic implications of the analysis answers.
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Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases

Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases

The other line of work (the IR-based) has fo- cused on mapping answers and questions into the same embedding space, where one could query any KB independent of its schema without requiring any grammar or lexicon. Bordes et al. (2014b) were the first to apply an embedding-based approach for KBQA. Later, Bordes et al. (2014a) proposed the idea of subgraph embedding, which encodes more information (e.g., answer path and context) about the candidate answer. In follow-up work (Bordes et al., 2015; Jain, 2016), memory networks (We- ston et al., 2014) were used to store candidates, and could be accessed iteratively to mimic multi-hop reasoning. Unlike the above methods that mainly use a bag-of-words (BOW) representation to en- code questions and KB resources, (Dong et al., 2015; Hao et al., 2017) apply more advanced net- work modules (e.g., CNNs and LSTMs) to encode questions. Hybrid methods have also been pro- posed (Feng et al., 2016; Xu et al., 2016; Das et al., 2017), which achieve improved results by leverag- ing additional knowledge sources such as free text. While most embedding-based approaches encode questions and answers independently, (Hao et al., 2017) proposed a cross-attention mechanism to en- code questions according to various candidate an- swer aspects. Differently, in this work, our method goes one step further by modeling the bidirectional interactions between questions and a KB.
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Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks

Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks

Existing question answering methods infer answers either from a knowledge base or from raw text. While knowledge base (KB) methods are good at answering composi- tional questions, their performance is often affected by the incompleteness of the KB. Au contraire, web text contains millions of facts that are absent in the KB, how- ever in an unstructured form. Universal schema can support reasoning on the union of both structured KBs and unstructured text by aligning them in a common embed- ded space. In this paper we extend uni- versal schema to natural language question answering, employing memory networks to attend to the large body of facts in the combination of text and KB. Our models can be trained in an end-to-end fashion on question-answer pairs. Evaluation results on S PADES fill-in-the-blank question an-
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Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

The one exception is the KB-only case for WebQuestionsSP where GRAFT-Net does 6.2% F1 points worse than Neural Symbolic Machines (Liang et al., 2017). Analysis suggested three ex- planations: (1) In the KB-only setting, the recall of subgraph retrieval is only 90.2%, which lim- its overall performance. In an oracle setting where we ensure the answers are part of the subgraph, the F1 score increases by 4.8%. (2) We use the same probability threshold for all questions, even though the number of answers may vary signifi- cantly. Models which parse the query into a sym- bolic form do not suffer from this problem since answers are retrieved in a deterministic fashion. If we tune separate thresholds for each question the F1 score improves by 7.6%. (3) GRAFT-Nets per- form poorly in the few cases where there is a con- straint involved in picking out the answer (for ex- ample, “who first voiced Meg in Family Guy”). If we ignore such constraints, and consider all enti- ties with the same sequence of relations to the seed as correct, the performance improves by 3.8% F1. Heuristics such as those used by Yu et al. (2017) can be used to improve these cases. Figure 3 shows
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CFO: Conditional Focused Neural Question Answering with Large scale Knowledge Bases

CFO: Conditional Focused Neural Question Answering with Large scale Knowledge Bases

How can we enable computers to automat- ically answer questions like “Who created the character Harry Potter”? Carefully built knowledge bases provide rich sources of facts. However, it remains a chal- lenge to answer factoid questions raised in natural language due to numerous ex- pressions of one question. In particular, we focus on the most common questions — ones that can be answered with a sin- gle fact in the knowledge base. We pro- pose CFO, a Conditional Focused neural- network-based approach to answering fac- toid questions with knowledge bases. Our approach first zooms in a question to find more probable candidate subject men- tions, and infers the final answers with a unified conditional probabilistic frame- work. Powered by deep recurrent neural networks and neural embeddings, our pro- posed CFO achieves an accuracy of 75.7% on a dataset of 108k questions – the largest public one to date. It outperforms the cur- rent state of the art by an absolute margin of 11.8%.
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Knowledge Sharing via Social Login: Exploiting Microblogging Service for Warming up Social Question Answering Websites

Knowledge Sharing via Social Login: Exploiting Microblogging Service for Warming up Social Question Answering Websites

This work is also concerned with mining across heterogeneous social networks. Recently, many re- searches focus on mapping accounts from different sites to one single identity (Zafarani and Liu, 2013; Liu et al., 2013; Kong et al., 2013). By utilizing these recent studies on linking users across communities, our work can be extended to larger scale datasets. From another perspective, cross-domain recommen- dation has also been widely studied. Zhang et al. (Zhang and Pennacchiotti, 2013a; Zhang and Pennac- chiotti, 2013b) explore how Facebook profiles can help boost product recommendation on e-commerce site. Previous work (Zhang et al., 2014) analyze user novelty seeking traits on social network and e- commerce site, which can be used to personalized recommendation and targeted advertisement. Dif- ferent from simply borrowing user’s profiles or psychological traits, our work integrates user footprints from heterogenous social networks and captures performance related characteristics more precisely. 7 Conclusion
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Declarative Question Answering over Knowledge Bases Containing Natural Language Text with Answer Set Programming

Declarative Question Answering over Knowledge Bases Containing Natural Language Text with Answer Set Programming

The question that we then address is, “can the system uti- lize the additional knowledge (for e.g. the knowledge of an “indicator”) without requiring the entire text to be given in a formal language?” We show that by using Answer Set Pro- gramming and some of its recent features (function symbols) to call external modules that are trained to do simple tex- tual entailment, it is possible do declaratively reasoning over text. We have developed a system following this approach that answers questions from life cycle text by declaratively reasoning about concepts such as “middle”, “between”, “in- dicator” over premises given in natural language text. To evaluate our method a new dataset has been created with the help of Amazon Mechanical Turk. The entire dataset con- tains 5811 questions that are created from 41 life cycle texts. A part of this dataset is used for testing. Our system achieved up to 18% performance improvements when compared to standard baselines.
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Finding Answers to Definition Questions Using Web Knowledge Bases

Finding Answers to Definition Questions Using Web Knowledge Bases

Current researches on Question Answering mainly concern more complex questions than factoid ones. In this paper, we propose an approach to leverage web knowledge bases effectively. After summarizing definitions from web knowledge bases and merging them, a two-stage retrieval model based on Probabilistic Latent Semantic Analysis is employed seek documents and sentences in which the topic is similar to that in definition set. Finally, an answer ranking model is utilized to rank both statistically and semantically similar sentences between sentences retrieved and sentences in definition set. Experiment shows that our system yields a better performance than the official one of NTCIR-7. In the future, we aim at improvement of more effective topic models, which could achieve a better performance in dealing with complex question answering.
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QUINT: Interpretable Question Answering over Knowledge Bases

QUINT: Interpretable Question Answering over Knowledge Bases

In this work, we demonstrate QUINT (Abujabal et al., 2017), a state-of-the-art KB-QA system that gives step-by-step explanations of how it derives answers for questions. Furthermore, when QUINT is unable to link a specific phrase in the question to a KB item, it asks the user to reformulate the phrase. Such reformulations can be used to im- prove various components in the KB-QA pipeline such as underlying lexicons. QUINT takes the first step towards enabling interactive QA in the future, where the system can ask the user about parts of the question that it is unsure about.

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Interpretable Question Answering on Knowledge Bases and Text

Interpretable Question Answering on Knowledge Bases and Text

We only present annotators with query instances for which both models output the same answer. However, we do not restrict these answers to be the ground truth. We perform the study with three explanation methods: average attention weights, LIME and IP. We apply each of them to the same question-answer pairs, so that the explana- tion methods are equally distributed among tasks. Every task contains one query and its predicted answer (which is the same for both models), and explanations for both models by the same explana- tion method. In contrast to image classification, it would not be human-friendly to show participants all input components (i.e., all facts), since their number can be up to 5500. Hence, we show the top5 facts with the highest relevance score. The order in which model A and model B appear on the screen (i.e., which is “left” and which is “right” in Figure 2) is random to avoid biasing annotators.
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How to Rank Answers in Text Mining

How to Rank Answers in Text Mining

Throughout the entire process of the thesis, we summarize main contributions in several parts. Firstly, we develop CEW-DTW. This methodology gives us a standard—an “ideal” answer—to rank answers. It has been proved to have a better ranking performance than Dynamic Time Warping and Dynamic Time Warping-Delta. Secondly, we develop KL-CEW-DTW from CEW- DTW. This methodology rank answers from the viewpoint of distributions of keywords and noise. It is proven to be better than CEW-DTW in ranking performance. Thirdly, we develop the general entropy, which use probabilities of noise and keywords to analyze answers. We develop an imaginary answer with the maximum entropy probabilities from the global probabilities in terms of the general entropy methodology. The maximum general entropy answer gives us a way to judge which keywords are important. We also find a way to determine the optimum number of keywords. According to this optimum number, we do not need to select too many keywords. Fourthly, we study inner connections of noise and keywords by applying the Markov transition matrix. This methodology contributes to judge which two keywords are usually connected. The inner connections are helpful to find the trend of speech.
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Handling Inconsistency in Knowledge Bases

Handling Inconsistency in Knowledge Bases

read. This resulted in hundreds of sub codes. There were 687 assigned sub-category codes and 130 memos in this process, as a result of initial open coding analysis of the 18 interviews describing the social constructions of racial reconciliation in the Beloved Community. The MaxQDA qualitative data handling software program proved a useful way to handle all the data as I formulated my initial codes. Once I began the next phase of coding—thematic coding occurring in the final month of my coding schedule, things became more refined. The themes that emerged related to each of the three research questions. When I began thematic coding, I used color coding and found it helpful to use a consistent color code of yellow highlighting for question one, red for question two, and blue for question three. Using these distinctive colors for each of the research questions made it easier to return to the work and pick up the pieces again for each new data analysis and coding session. I transferred quotes from the transcripts into a summative spreadsheet, using MaxQDA. This was further streamlined as I went through the transcripts by hand.
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Handling Inconsistency in Knowledge Bases

Handling Inconsistency in Knowledge Bases

We also show the method of using the paraconsistent relational model to description logic programs. Description logic programs provide a significant degree of expressiveness, substantially greater than the RDF-Schema fragment of description logic. The essential idea of the description logic program is the flow of information between description logic and logic programs. The flow of information happens with the help of description logic atoms. They are similar to regular atoms in the logic program, but they get the information from description logic knowledge base and use it with the clauses of the logic programs. Our approach starts with finding an equivalent relation (description logic relation) for the description logic atom and defining a proper domain for every attribute in the description logic relation. Then, using the description relation, we are working towards finding the fixed-point semantics of description logic programs.
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Handling Inconsistency in Knowledge Bases

Handling Inconsistency in Knowledge Bases

Luck egalitarianism is a view that connects what a person is entitled to with what they voluntary choose. The idea is that inequalities in economic goods can only be justified if differences in voluntary choices alone account for these inequalities. 15 Critics of luck egalitarianism have misconstrued the luck egalitarian position as being committed to letting people suffer for all of their poor choices. As Brown points out, “’luck egalitarians do not actually claim that every instance of voluntary choice should incur full responsibility.” 16 The question then remains, what kind of voluntary choices can account for these inequalities? Some luck egalitarians hold that distributions ought to reflect ambition-sensitivity, while others are concerned with effort. 17 Regardless of how voluntary choices that confer responsibility are specified, the critique offered here should apply. I use the term economic responsibility to refer to those voluntary choices that people are responsible for and that confer entitlement. This includes hard work, effort, prudence, or whatever else a luck egalitarian theorist might believe confers entitlement. I will assume that the only thing that is untouched by luck is economic responsibility.
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Handling Inconsistency in Knowledge Bases

Handling Inconsistency in Knowledge Bases

states continued to engender prejudice and inequality for bi-national same-sex couples; 2 if, when, and the process by which bi-national same-sex couples developed a cohesive identity an[r]

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Handling Inconsistency in Knowledge Bases

Handling Inconsistency in Knowledge Bases

courtyard. He played so loud one day that the judge ordered the sheriff to administer 39 lashes. After receiving 13 Billy pointed out the alleviating laws, intended to protect debtors, which required that they only had to pay one-third of their debt annually. Based on the statute Billy claimed the right and appealed to the judge to stop the lashes. The joke surely brought quite a chuckle to former slaveholders back in the postbellum south. This same story with additional embellishments appeared in The Atlanta Weekly Constitution under the byline of Colonel W. H. Sparks, so it was clear that this was a widely told story. Interestingly the source of humor was based on conflictive frames of contradiction, in this case an uneducated slave having the knowledge to protect himself by use of the law. This humorous story only worked because it drew upon an unspoken understanding that slaves could not possibly be so smart. Ultimately, it reinforced the accepted social structure of slaves’ lack of intellectual capacity. Historians find value in the story for two reasons, the recourse to the “thirding law” and the underlying social structure. The presence of a slave in the courtyard on court day, entertaining the crowds contrast sharply with the gang laborer imagine historians often discuss. The thirding law was an obscure law passed by the state assembly in 1808 and repealed the same year. This statute which was in effect only 211 day and passed as result of Thomas Jefferson’s Embargo Act of 1807, hit merchants, cotton factors, and bankers especially hard. The reference also illustrates the importance of debt in the south and how there ran a threat of opposition through the south that opposed the financial power that debt created. 171
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Handling Inconsistency in Knowledge Bases

Handling Inconsistency in Knowledge Bases

Another method of supporting these findings is to explore rival explanations and test them against the data (Miles et al., 2015). An apparent rival explanation for the success of the all-day planning session could be that the department chair simply gained control of the agenda and thereby prevented other tasks from interfering with the planning time. Counter-arguments to this rival explanation include asking the question, “Why hadn’t the group simply taken control previously?” A plausible answer is that the group never had an issue like CRISPA to approach the setting of the agenda. Second, the post-interview responses unanimously supported the assertion that the participants believed the application of CRISPA made a difference during planning. Further, those interview results also confirmed that each participant applied some principle or principles of CRISPA during their lesson delivery of the revolution unit. Third, the findings match those of existing literature on CRISPA (Conrad, et al. (2015); Uhrmacher & Moroye, 2007, 2009). Teachers who participate in CRISPA report a renewed energy and vitality to their planning and the participants in this research had the same experience. Finally, the participants were asked to conduct member checks of chapters four and five. Only two
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Handling Inconsistency in Knowledge Bases

Handling Inconsistency in Knowledge Bases

Furthermore, the present study investigated how social settings such as sororities affects the relationship between efficacy and leadership participation for sorority members in the Unit[r]

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