| { |
| "paper_id": "2020", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T09:42:18.790412Z" |
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| "title": "A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction", |
| "authors": [ |
| { |
| "first": "Yang", |
| "middle": [], |
| "last": "Zhou", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Notre Dame", |
| "location": { |
| "postCode": "46556", |
| "region": "IN" |
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| }, |
| "email": "" |
| }, |
| { |
| "first": "Tong", |
| "middle": [], |
| "last": "Zhao", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Notre Dame", |
| "location": { |
| "postCode": "46556", |
| "region": "IN" |
| } |
| }, |
| "email": "tzhao2@nd.edu" |
| }, |
| { |
| "first": "Meng", |
| "middle": [], |
| "last": "Jiang", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Notre Dame", |
| "location": { |
| "postCode": "46556", |
| "region": "IN" |
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| "email": "mjiang2@nd.edu" |
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| "abstract": "Textual patterns (e.g., Country's president Person) are specified and/or generated for extracting factual information from unstructured data. Pattern-based information extraction methods have been recognized for their efficiency and transferability. However, not every pattern is reliable: A major challenge is to derive the most complete and accurate facts from diverse and sometimes conflicting extractions. In this work, we propose a probabilistic graphical model which formulates fact extraction in a generative process. It automatically infers true facts and pattern reliability without any supervision. It has two novel designs specially for temporal facts: (1) it models pattern reliability on two types of time signals, including temporal tag in text and text generation time; (2) it models commonsense constraints as observable variables. Experimental results demonstrate that our model significantly outperforms existing methods on extracting true temporal facts from news data.", |
| "pdf_parse": { |
| "paper_id": "2020", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "Textual patterns (e.g., Country's president Person) are specified and/or generated for extracting factual information from unstructured data. Pattern-based information extraction methods have been recognized for their efficiency and transferability. However, not every pattern is reliable: A major challenge is to derive the most complete and accurate facts from diverse and sometimes conflicting extractions. In this work, we propose a probabilistic graphical model which formulates fact extraction in a generative process. It automatically infers true facts and pattern reliability without any supervision. It has two novel designs specially for temporal facts: (1) it models pattern reliability on two types of time signals, including temporal tag in text and text generation time; (2) it models commonsense constraints as observable variables. Experimental results demonstrate that our model significantly outperforms existing methods on extracting true temporal facts from news data.", |
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| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
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| "body_text": [ |
| { |
| "text": "Temporal fact extraction is to extract (entity, value, time)-factual tuples from text data (e.g., news, tweets) for specific attributes. It acts as one of the fundamental tasks in knowledge base construction, knowledge graph population, and question answering. For example, if we were interested in country's president, the entity would be of type Location.Country, the value would be of type Person, and the time would be a valid year in the person's presidential term. Thanks to name entity recognition (NER) and typing systems (Del Corro et al., 2015) , pattern-based information extraction methods generate patterns consisted of entity types (Jiang et al., 2017; Li et al., 2018; Reimers et al., 2016) . They are widely used for good transferability across domains and datasets, unsupervised manner requiring no or very few annotations, and high efficiency. The typed patterns give only the association between entity and value. Two types of time signals can be attached to the pairs, forming temporal triples: One is temporal tag in text, e.g., the year tag next to the entity/value mentions in the sentence; the other is text generation time, i.e., the year the text document was posted. For example, given two sentences: 1) \".. Pattern-based methods discover two patterns:", |
| "cite_spans": [ |
| { |
| "start": 530, |
| "end": 554, |
| "text": "(Del Corro et al., 2015)", |
| "ref_id": "BIBREF2" |
| }, |
| { |
| "start": 646, |
| "end": 666, |
| "text": "(Jiang et al., 2017;", |
| "ref_id": "BIBREF8" |
| }, |
| { |
| "start": 667, |
| "end": 683, |
| "text": "Li et al., 2018;", |
| "ref_id": "BIBREF10" |
| }, |
| { |
| "start": 684, |
| "end": 705, |
| "text": "Reimers et al., 2016)", |
| "ref_id": "BIBREF13" |
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| ], |
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| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "\u2022 P1: former Country president Person \u2022 P2: Person, now president of Country, Then the methods can extract the following tuples. We label and for correct tuples and incorrect ones, respectively: (France, Jacques Chirac, 1995) : P1 and temporal tag; (France, Jacques Chirac, 2019): P1 and text gen. time; (France, Emmanuel Macron, 2004): P2 and temporal tag; (France, Emmanuel Macron, 2019): P2 and text gen. time. We have the following observations:", |
| "cite_spans": [ |
| { |
| "start": 195, |
| "end": 225, |
| "text": "(France, Jacques Chirac, 1995)", |
| "ref_id": null |
| } |
| ], |
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| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "\u2022 O1: Not every pattern is reliable: the pattern \"Person visited Country\" is very likely to be unreliable. Not every pattern is unreliable: the pattern \"current Country's president Person\" is very likely to be reliable. The above two pattern examples are somehow half and half. So, patterns have reliability. \u2022 O2: For temporal fact extraction, different types of time signals might be either reliable or unreliable depending on the pattern. So, there is a dependency between pattern and type of time signal, in terms of reliability. Existing truth finding approaches assumed that a structured \"source-object-claim\" database was given and then estimated the reliability of source for inferring whether the claim was true or false (Yin et al., 2008; . For example, a source could be a book seller, an object could be a book's author list, and a claim could be an author list that a seller gave for a book. One conclusion was that probabilistic graphical models (PGM) have advantages of estimating source reliability over the general data distributions, compared with bootstrapping algorithms (Yin et al., 2008; Li et al., 2018; Wang et al., 2019) . However, PGM-based truth finding models have not yet been developed for the task of information extraction. Estimating the reliability of textual patterns is new (O1). Moreover, when we focus on temporal fact extraction, modeling the dependency between pattern and type of time signals is also new (O2).", |
| "cite_spans": [ |
| { |
| "start": 730, |
| "end": 748, |
| "text": "(Yin et al., 2008;", |
| "ref_id": "BIBREF21" |
| }, |
| { |
| "start": 1091, |
| "end": 1109, |
| "text": "(Yin et al., 2008;", |
| "ref_id": "BIBREF21" |
| }, |
| { |
| "start": 1110, |
| "end": 1126, |
| "text": "Li et al., 2018;", |
| "ref_id": "BIBREF10" |
| }, |
| { |
| "start": 1127, |
| "end": 1145, |
| "text": "Wang et al., 2019)", |
| "ref_id": "BIBREF18" |
| } |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "In truth finding, it is critical to define conflicts. For the book seller's example, we assume that one book can have only one true author list; so if we knew one list was true, then any different list of the same book would be false. This originated from our commonsense. Fortunately, we have quite a few commonsense rules for temporal facts, i.e., specific attributes. On country's president, we know that", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "\u2022 one president serves only one country;", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "\u2022 one country has only one president at a time;", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "\u2022 however, one country can have multiple presidents in the history (e.g., USA, France). For the attribute sports team's player, we have commonsense rules:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "\u2022 one player serves only one club at a time;", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "\u2022 however, one club has multiple players and one player can serve multiple clubs in his/her career. We generalize possible commonsense rules:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "\u2022 C1: one value matches with only one entity;", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "\u2022 C2: one entity matches with only one value;", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "\u2022 C3: one value matches with only one entity at a time; \u2022 C4: one entity matches with only one value at a time. So, we know that the attribute country's president follows C1 and C4; and the attribute sports team's player follows C3. The third challenge (O3) is the necessity of modeling the commonsense (e.g., C1-C4) for identifying conflicts, estimating pattern reliability, and finding true temporal facts.", |
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| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "To address the three challenges (O1-O3), we propose a novel Probabilistic Graphical Model with Commonsense Constraints (PGMCC), for finding true temporal facts from the results from patternbased methods. The given input is the observed frequency of tuples extracted by a particular pattern and attached with a particular type of time signal. We model information source as a pair of pattern and type of time signal. We represent the source reliability as an unobserved variable. It becomes a generative process. We first generate a source. Next we generate a (entity, value, time)-tuple. Then we generate the frequency based on the source reliability and the tuple's trustworthiness (i.e., probability of being a truth). Moreover, we generate variables according to the commonsense rules if needed -the variable counts the values/entities that can be matched to one entity/value with or without a time constraint (at one time) from the set of true tuples. Given a huge number of patterns (i.e., 57,472) and tuples (i.e., 116,631) in our experiments, our proposed unsupervised learning model PGMCC can effectively estimate pattern reliability and find true temporal facts.", |
| "cite_spans": [], |
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| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "Our main contributions are:", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| { |
| "text": "\u2022 We introduce the idea of PGM-based truth finding to the task of pattern-based temporal fact extraction. \u2022 We propose a new unsupervised probabilistic model with observed constraints to model the reliability of textual patterns, the trustworthiness of temporal tuples, and the commonsense rules for certain types of facts. \u2022 Experimental results show that our model can improve AUC and F1 by more than 7% over the state-of-the-art. The rest of this paper is organized as follows. Section 2 introduces the terminology and defines the problem. Section 3 presents an overview as well as details of the proposed model. Experimental results can be found in Section 4. Section 5 surveys the literature. Section 7 concludes the paper.", |
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| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "Definition 1 (Temporal fact: (entity, value, time)-tuple)). Let F = {f 1 , f 2 , f 3 , . . . } be the set of temporal facts. Each fact f is in the format of (entity, value, time). F was extracted by textual pattern-based methods.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Terminology and Problem Definition 2.1 Terminology", |
| "sec_num": "2" |
| }, |
| { |
| "text": "Definition 2 (Pattern s). Let P ( * ) = {p ( * )", |
| "cite_spans": [], |
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| "section": "Terminology and Problem Definition 2.1 Terminology", |
| "sec_num": "2" |
| }, |
| { |
| "text": "1 , . . . } be the set of pattern source, here * \u2208 {post, tag} stands for the type of time signal (i.e., \"text gen. time\" and \"temporal tag\"). One pattern paired with different types of time signals will be treated as different pattern sources.", |
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| "section": "Terminology and Problem Definition 2.1 Terminology", |
| "sec_num": "2" |
| }, |
| { |
| "text": "Definition 3 (Extraction). Let E = {e 1 , e 2 , e 3 , . . . } be the set of extractions. Our generative model will take E as input. An extraction item e is in the format of (f , p ( * ) , o). Here o stands for the observed frequency of fact tuples f that were extracted by pattern p ( * ) in E.", |
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| "section": "Terminology and Problem Definition 2.1 Terminology", |
| "sec_num": "2" |
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| { |
| "text": "Definition 4 (Constraint). Each commonsense rule (constraint) is represented as a variable. The variable is likely to be observed as 1. Examples:", |
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| "section": "Terminology and Problem Definition 2.1 Terminology", |
| "sec_num": "2" |
| }, |
| { |
| "text": "\u2022 one value matches with only one entity, denoted as C 1v\u22121e that counts the number of such entities. \u2022 one entity at one time matches with only one value, denoted as C 1(e,t)\u22121v that counts the number of values.", |
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| "section": "Terminology and Problem Definition 2.1 Terminology", |
| "sec_num": "2" |
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| "text": "Suppose the set of extractions E have been obtained by pattern-based methods from text data. We define the problem as follows: Given a set of extractions E, pattern sources P ( * ) , and the constraints C a for attribute a, infer truth T for all temporal facts F contained in E and quality information for each pattern source p ( * ) .", |
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| "section": "Problem Definition", |
| "sec_num": "2.2" |
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| "text": "We mainly discussed the model detail of PGM with multiple Constraints C 1(e,t)\u22121v and C 1v\u22121e , since it's the most complicated scenario while modeling constraint. The given input is the observed frequency of fact tuples extracted by a particular pattern and attached with a particular type of time signal. Figure 1 gives the plate notation of our model. Each node represent a variable. Blue nodes indicate hyper-parameter. Gray nodes stand for observable variable. And white nodes stand for latent variables we want to infer.", |
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| "start": 307, |
| "end": 315, |
| "text": "Figure 1", |
| "ref_id": "FIGREF1" |
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| "section": "Proposed Approach", |
| "sec_num": "3" |
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| "text": "Our approach based on PGM is a generative process. We first generate a source. Next we generate a (entity, value, time)-tuple. Then we generate the frequency based on the source reliability and the tuple's trustworthiness. Moreover, we generate variables according to the commonsense constraints. The variables counts the values/entities that can be matched to one entity/value with or without a time constraint (at one time) from the set of true tuples. The concrete meaning of each variable has been given in Table 1 .", |
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| "start": 511, |
| "end": 518, |
| "text": "Table 1", |
| "ref_id": "TABREF0" |
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| "section": "Generative Process", |
| "sec_num": "3.1" |
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| "text": "Temporal fact trustworthiness. For each temporal fact f \u2208 F, we first draw \u03b8 f , i.e., the prior truth probability of fact f , from a Beta distribution with hyper-parameter \u03b2 0 and \u03b2 1 :", |
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| "section": "Generative Process", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "\u03b8 f \u223c Beta(\u03b2 0 , \u03b2 1 ).", |
| "eq_num": "(1)" |
| } |
| ], |
| "section": "Generative Process", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "\u03b2 0 and \u03b2 1 represent the prior distribution of fact reliability. In practice, if we have a strong prior knowledge about how likely all or certain temporal facts are true, we can model it with the corresponding hyper-parameters. Otherwise, if we do not have a strong belief, we set a uniform prior, which means it's equally likely to be true or false, and our model can still infer the truth from other factors. After drawing the \u03b8 f , we generate the truth label l f from a Bernoulli distribution with parameter \u03b8 f :", |
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| "section": "Generative Process", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "EQUATION", |
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| "ref_spans": [], |
| "eq_spans": [ |
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| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "l f \u223c Bernoulli(\u03b8 f ).", |
| "eq_num": "(2)" |
| } |
| ], |
| "section": "Generative Process", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "Pattern source reliability. As aforementioned, a reliable pattern source is more likely to extract true facts with higher counts, and extract false facts Integer: the observed frequency of fact f e extracted by pattern p", |
| "cite_spans": [], |
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| "section": "Generative Process", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "( * ) e \u03bb p ( * ) 0 , \u03bb p ( * ) 1", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Generative Process", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "Real numbers: reliability of pattern p ( * ) on giving false/true fact tuples C 1v\u22121e", |
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| "eq_spans": [], |
| "section": "Generative Process", |
| "sec_num": "3.1" |
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| "text": "Real number: the number of entities given one value v C 1(e,t)\u22121v Real number: the sum of values given one entity e and one time t Hyper-Parameter \u00b5 0 , \u00b5 1", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "Generative Process", |
| "sec_num": "3.1" |
| }, |
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| "text": "Integers: prior counts of false/true tuples extracted by a textual pattern \u03ba 0 , \u03ba 1", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Generative Process", |
| "sec_num": "3.1" |
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| "text": "Integers: prior sums of false/true tuples extracted by a textual pattern \u03b2 0 , \u03b2 1", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Generative Process", |
| "sec_num": "3.1" |
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| "text": "Integers: prior counts of false/true tuples with lower counts. Therefore, we choose average count of false/true as latent pattern reliable weight, it's represented as \u03bb", |
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| "section": "Generative Process", |
| "sec_num": "3.1" |
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| { |
| "text": "p ( * ) 0 , \u03bb p ( * ) 1", |
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| "section": "Generative Process", |
| "sec_num": "3.1" |
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| "text": "for pattern p ( * ) . The Gamma distribution is utilized because it is the conjugate prior of Poisson distributions. Initially, these two parameters are generated from Gamma distribution with hyper-parameter {\u00b5 0 , \u03ba 0 }/{\u00b5 1 , \u03ba 1 }, respectively. \u00b5 0 and \u00b5 1 represent the prior number of false/true fact the pattern extract, and \u03ba 0 and \u03ba 1 determine the prior sum of false/true fact count:", |
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| "section": "Generative Process", |
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| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "\u03bb p ( * ) 0 \u223c Gamma(\u00b5 0 , \u03ba 0 ); (3) \u03bb p ( * ) 1 \u223c Gamma(\u00b5 1 , \u03ba 1 )", |
| "eq_num": "(4)" |
| } |
| ], |
| "section": "Generative Process", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "Extraction observation. For each extraction e \u2208 E, it is composed of {f, p ( * ) , o}. f e denotes the temporal fact f belongs to e, p ( * ) denotes where it's extracted, o e stands for extraction e's observation count. When the truth label of fact f e is false, o e is generated from Poisson distribution with p ( * ) 's false speaking side parameter \u03bb p ( * ) 0 . While f e is true, o e is generated from Poisson Distribution with p ( * ) 's true speaking side parameter \u03bb p ( * ) 1 :", |
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| "section": "Generative Process", |
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| "text": "o e \u223c P oisson(\u03bb p ( * ) 0 ) if l fe = 0, o e \u223c P oisson(\u03bb p ( * )", |
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| "section": "Generative Process", |
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| "text": "1 ) if l fe = 1.", |
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| "section": "Generative Process", |
| "sec_num": "3.1" |
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| "text": "Constraints. Finally, we draw the constraint variables. In temporal fact extraction, we define two variables C 1(e,t)\u22121v and C 1v\u22121e . C 1(e,t)\u22121v limits the number of truth on certain constraint key {e, t}. There are as many C 1(e,t)\u22121v variables as unique {e, t} keys:", |
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| { |
| "text": "EQUATION", |
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| "start": 0, |
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| "raw_str": "C e,t = f l f , f \u2208 F e,t ,", |
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| } |
| ], |
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| "sec_num": "3.1" |
| }, |
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| "text": "where F e,t denotes a set of f with same {e, t}. Each C 1(e,t)\u22121v is generated by F e,t set. C 1v\u22121e ilimits the truth of fact with same {v}:", |
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| "text": "C v = e\u2208E l e,v \uf8f1 \uf8f2 \uf8f3 l e,v = 1, if \u2203l f = 1, f \u2208 F e,v ; l e,v = 0, otherwise. (7) where F v denotes set of fact with value v, F e,v", |
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| "text": "stands for a set of temporal fact f with same {e, v}, F e,v \u2208 F v . l e,v denoted the truth label of v, e. Each C 1v\u22121e is generated by F v . If there is true fact f \u2208 F e,v , then l e,v equals to one, otherwise, l e,v equal with zero.", |
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| "sec_num": "3.1" |
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| "text": "We focus on attribute country's president and experiment on the same data set in the work of (Wang et al., 2019) . It has 9,876,086 news articles (4 billion words) published from 1994-2010. We have 57,472 patterns, 116,631 temporal fact tuples, and 1,326,164 extractions. The dataset's ground truth was collected from Google and Wikipedia. It includes 3,175 true temporal facts of 130 countries.", |
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| "start": 93, |
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| "text": "(Wang et al., 2019)", |
| "ref_id": "BIBREF18" |
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| "section": "Dataset", |
| "sec_num": "4.1" |
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| "text": "We compare our model with: \u2022 TRUTHFINDER (Yin et al., 2008) : It was a bootstrapping algorithm for structured data using C 1v\u22121e .", |
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| "start": 41, |
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| "text": "(Yin et al., 2008)", |
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| "section": "Competitive methods", |
| "sec_num": "4.2.1" |
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| "text": "\u2022 LTM : It was a probabilistic model, assuming that the truth about an object contains more than one value. We set \"object\" as {entity, time} and set value as the temporal fact's value. \u2022 TRUEPIE (Li et al., 2018) : It was a bootstrapping method using C 1(v)\u22121e and estimating pattern reliability.", |
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| { |
| "start": 196, |
| "end": 213, |
| "text": "(Li et al., 2018)", |
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| "section": "Competitive methods", |
| "sec_num": "4.2.1" |
| }, |
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| "text": "\u2022 MAJVOTE (Goldman and Warmuth, 1995): It used the weighted majority voting strategy and returned the most frequent temporal fact.", |
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| "section": "Competitive methods", |
| "sec_num": "4.2.1" |
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| "text": "\u2022 TFWIN (Wang et al., 2019) : It was the state-ofthe-art bootstrapping method for truth discovery on fact extraction. However, error propagation is serious in its iterative process.", |
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| "start": 8, |
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| "sec_num": "4.2.1" |
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| "text": "All the methods can only find truth of temporal fact at one time point, e.g., (French, Jacques Chirac, 1995) . However, due to the incompleteness of fact description in data, some time points of temporal facts could be missing. One way to improve the evaluation is to composite true temporal fact time points {e, v, t} into temporal fact time period {e, v, [t min , t max ]}. We evaluate the performance on both temporal fact time point {e, v, t} and temporal fact time period {e, v, [t min , t max ]}. To evaluate on time period {e, v, [t min , t max ]}, we look at every single time points (e, v, t) in the period (t \u2208 [t min , t max ]).", |
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| "start": 78, |
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| "text": "(French, Jacques Chirac, 1995)", |
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| "section": "Evaluation settings", |
| "sec_num": "4.2.2" |
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| "text": "We evaluate all competitive methods using precision, recall, F1 score, and AUC (Area Under the Curve). Precision is the the fraction of temporal fact truth among all the temporal fact that were labelled as true. Recall is the fraction of true temporal facts our approach finds among the ground truth temporal facts. F1 score is the harmonic mean of precision and recall. For all of the metrics, higher score indicates that the method performs better.", |
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| "section": "Evaluation metrics", |
| "sec_num": "4.2.3" |
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| "text": "The results are given in Table 2 . Our proposed method PGMCC consistently outperforms all the baselines on finding (country, president, time)-facts (i.e., presidential terms).", |
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| "ref_spans": [ |
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| "start": 25, |
| "end": 32, |
| "text": "Table 2", |
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| "section": "Effectiveness", |
| "sec_num": "4.3" |
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| "text": "PGMCC vs LTM: PGMCC performs significantly better than LTM (+34.5% AUC; +64.4% F1) on evaluating time points, and performs better with (+40.45% AUC; +71.2% F1) on evaluating time periods. LTM was designed to solve structured truth finding like the bookseller example. So, there were many conflicts when applied to temporal fact extraction. PGMCC has multi-constraint as observable variables to alleviate the issue.", |
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| "text": "PGMCC vs TFWIN: PGMCC performs better than TFWIN (+2.4% AUC; +2.8% F1) on evaluating time points, and performs better with (+5.2% AUC; +8.3% F1) on evaluating time periods. TFWIN started with seed patterns and defined constraints as a rule to eliminate conflicting tuples. However, the inference on conflicts was based on local information (i.e., the current pattern reliability estimation). During this process, error might propagate through iterations. PGMCC is a probabilistic graphical model that can avoid error propagation by modeling constraints as variables and inferring truth with the global data distributions.", |
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| "text": "PGMCC with different constraints: See the last two rows in Table 2 . For both PGMCC and TFWIN models, a complete constraint set, i.e., {C 1(v)\u22121e and C 1(e,t)\u22121v }, gives the best performance. Partial constraint cannot fully identify conflicts or false tuples. C 1(e,t)\u22121v plays a significant role in extracting country's president. Table 3 presented some pattern examples and their scores. Here are our observations. First, the pattern \"president Person of Country\" is the only pattern that shows high reliability on both types of time signals (above 0.85). Second, the textual patterns that describe the current presidency are likely to have higher reliability on text gen. time than temporal tag, because the presidency was likely to be in the same time as the document was generated. These patterns usually have words such as \"current\", \"newly\", and \"now\". Third, the textual patterns that describe the past presidency are likely to have higher reliability on \"tag\" than \"post\", because the presidency was likely to be in the same time as the event (described in the sentence) happened but before the time of the document being generated. These patterns usually have words such as \"have governed\", \"have ruled\", \"former\", and \"formerly\".", |
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| "start": 59, |
| "end": 66, |
| "text": "Table 2", |
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| "text": "Table 3", |
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| } |
| ], |
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| "section": "Effectiveness", |
| "sec_num": "4.3" |
| }, |
| { |
| "text": "In this section, we review two relevant fields to our work, temporal fact extraction and truth discovery.", |
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| "section": "Related Work", |
| "sec_num": "5" |
| }, |
| { |
| "text": "In big data era, the issue of \"Veracity\" on resolving conflicts among multi-source information is quite serious (Berti-Equille, 2015; Vydiswaran et al., 2011; Waguih and Berti-Equille, 2014; Dong et al., 2009; Galland et al., 2010; Xiao et al., 2016; Yin and Tan, 2011) . Truth discovery methods find trustworthy information from conflicting multi-source (Xiao et al., 2015; . Several truth discovery methods have been proposed for various scenarios, and they have been successfully applied in diverse application domains. A few truth discovery methods are probabilistic model. LTM solved the \"Book's author list problem\" and modeled its source in two-fold quality . GTM solved the task of finding true numeric value of \"New York City's population\" . TEXTTRUTH found the true answer for a question from multi users (Zhang et al., 2018b) .", |
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| "text": "(Berti-Equille, 2015;", |
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| "start": 134, |
| "end": 158, |
| "text": "Vydiswaran et al., 2011;", |
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| "start": 159, |
| "end": 190, |
| "text": "Waguih and Berti-Equille, 2014;", |
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| "start": 191, |
| "end": 209, |
| "text": "Dong et al., 2009;", |
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| }, |
| { |
| "start": 210, |
| "end": 231, |
| "text": "Galland et al., 2010;", |
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| }, |
| { |
| "start": 232, |
| "end": 250, |
| "text": "Xiao et al., 2016;", |
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| }, |
| { |
| "start": 251, |
| "end": 269, |
| "text": "Yin and Tan, 2011)", |
| "ref_id": "BIBREF22" |
| }, |
| { |
| "start": 355, |
| "end": 374, |
| "text": "(Xiao et al., 2015;", |
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| }, |
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| "start": 815, |
| "end": 836, |
| "text": "(Zhang et al., 2018b)", |
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| "section": "Truth Discovery", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "Temporal fact extraction is to extract (entity, attribute name, attribute value)-tuples along with their time conditions from text corpora (Sil and Cucerzan, 2014; Hoang-Vu et al., 2016; Chekol, 2017; Zhang et al., 2018a; Shang et al., 2018; Zeng et al., 2019; . Textual patterns have been proposed to extract structured data from unstructured text data in an unsupervised way, such as E-A patterns (Gupta et al., 2014) , parsing patterns (Nakashole et al., 2012) , and meta patterns (Jiang et al., 2017) . However, patterns are of different reliability and extractions are sometimes conflicting. In order to get reliable temporal fact, we addressed this problem using truth discovery.", |
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| { |
| "start": 139, |
| "end": 163, |
| "text": "(Sil and Cucerzan, 2014;", |
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| }, |
| { |
| "start": 164, |
| "end": 186, |
| "text": "Hoang-Vu et al., 2016;", |
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| }, |
| { |
| "start": 187, |
| "end": 200, |
| "text": "Chekol, 2017;", |
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| }, |
| { |
| "start": 201, |
| "end": 221, |
| "text": "Zhang et al., 2018a;", |
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| }, |
| { |
| "start": 222, |
| "end": 241, |
| "text": "Shang et al., 2018;", |
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| }, |
| { |
| "start": 242, |
| "end": 260, |
| "text": "Zeng et al., 2019;", |
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| }, |
| { |
| "start": 399, |
| "end": 419, |
| "text": "(Gupta et al., 2014)", |
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| }, |
| { |
| "start": 439, |
| "end": 463, |
| "text": "(Nakashole et al., 2012)", |
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| }, |
| { |
| "start": 484, |
| "end": 504, |
| "text": "(Jiang et al., 2017)", |
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| "ref_spans": [], |
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| "section": "Temporal Fact Extraction", |
| "sec_num": "5.2" |
| }, |
| { |
| "text": "Though the proposed approach show effectiveness in experiments, it and/or the study has several limitations. First of all, because collecting temporal factual truth for a variety of attributes is very expensive, in this study, we only studied a single relation type. In future work, we will apply the approach to other types of temporal-facts if correct constraints can be defined, such as sports team's players and spouse relationship. Second, though the patterns were generated by automated mining technologies such as Meta Patterns (Jiang et al., 2017 ) (in other words, they are not hand-crafted), the pattern mining as a preprocessing step is needed. The approach is not end-to-end.", |
| "cite_spans": [ |
| { |
| "start": 535, |
| "end": 554, |
| "text": "(Jiang et al., 2017", |
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| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Limitations and Future Work", |
| "sec_num": "6" |
| }, |
| { |
| "text": "In this work, we proposed a probabilistic graphical model for inferring true facts and pattern reliability. It had two novel designs for temporal facts:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "7" |
| }, |
| { |
| "text": "(1) it modeled pattern reliability on temporal tag in text and text generation time;", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "7" |
| }, |
| { |
| "text": "(2) it modeled commonsense constraints as observable variables.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "7" |
| }, |
| { |
| "text": "Experimental results demonstrated that our model outperformed existing methods.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "7" |
| }, |
| { |
| "text": "This work was done when the first author was a visiting undergraduate student at Notre Dame.", |
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| "text": "This work was supported by NSF Grant IIS-1849816 and CCF-1901059. ", |
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| "section": "Acknowledgements", |
| "sec_num": null |
| } |
| ], |
| "bib_entries": { |
| "BIBREF0": { |
| "ref_id": "b0", |
| "title": "Data veracity estimation with ensembling truth discovery methods", |
| "authors": [ |
| { |
| "first": "Laure", |
| "middle": [], |
| "last": "Berti-Equille", |
| "suffix": "" |
| } |
| ], |
| "year": 2015, |
| "venue": "2015 IEEE International Conference on", |
| "volume": "", |
| "issue": "", |
| "pages": "2628--2636", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Laure Berti-Equille. 2015. Data veracity estimation with ensembling truth discovery methods. In Big Data (Big Data), 2015 IEEE International Confer- ence on, pages 2628-2636. IEEE.", |
| "links": null |
| }, |
| "BIBREF1": { |
| "ref_id": "b1", |
| "title": "Scaling probabilistic temporal query evaluation", |
| "authors": [ |
| { |
| "first": "Chekol", |
| "middle": [], |
| "last": "Melisachew Wudage", |
| "suffix": "" |
| } |
| ], |
| "year": 2017, |
| "venue": "Proceedings of the 2017 ACM on Conference on Information and Knowledge Management", |
| "volume": "", |
| "issue": "", |
| "pages": "697--706", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Melisachew Wudage Chekol. 2017. Scaling probabilis- tic temporal query evaluation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 697-706. ACM.", |
| "links": null |
| }, |
| "BIBREF2": { |
| "ref_id": "b2", |
| "title": "Finet: Context-aware fine-grained named entity typing", |
| "authors": [ |
| { |
| "first": "Luciano", |
| "middle": [], |
| "last": "Del Corro", |
| "suffix": "" |
| }, |
| { |
| "first": "Abdalghani", |
| "middle": [], |
| "last": "Abujabal", |
| "suffix": "" |
| }, |
| { |
| "first": "Rainer", |
| "middle": [], |
| "last": "Gemulla", |
| "suffix": "" |
| }, |
| { |
| "first": "Gerhard", |
| "middle": [], |
| "last": "Weikum", |
| "suffix": "" |
| } |
| ], |
| "year": 2015, |
| "venue": "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing", |
| "volume": "", |
| "issue": "", |
| "pages": "868--878", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Luciano Del Corro, Abdalghani Abujabal, Rainer Gemulla, and Gerhard Weikum. 2015. Finet: Context-aware fine-grained named entity typing. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 868-878.", |
| "links": null |
| }, |
| "BIBREF3": { |
| "ref_id": "b3", |
| "title": "Integrating conflicting data: the role of source dependence", |
| "authors": [ |
| { |
| "first": "Laure", |
| "middle": [], |
| "last": "Xin Luna Dong", |
| "suffix": "" |
| }, |
| { |
| "first": "Divesh", |
| "middle": [], |
| "last": "Berti-Equille", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Srivastava", |
| "suffix": "" |
| } |
| ], |
| "year": 2009, |
| "venue": "Proceedings of the VLDB Endowment", |
| "volume": "2", |
| "issue": "", |
| "pages": "550--561", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Xin Luna Dong, Laure Berti-Equille, and Divesh Sri- vastava. 2009. Integrating conflicting data: the role of source dependence. Proceedings of the VLDB En- dowment, 2(1):550-561.", |
| "links": null |
| }, |
| "BIBREF4": { |
| "ref_id": "b4", |
| "title": "Corroborating information from disagreeing views", |
| "authors": [ |
| { |
| "first": "Alban", |
| "middle": [], |
| "last": "Galland", |
| "suffix": "" |
| }, |
| { |
| "first": "Serge", |
| "middle": [], |
| "last": "Abiteboul", |
| "suffix": "" |
| }, |
| { |
| "first": "Am\u00e9lie", |
| "middle": [], |
| "last": "Marian", |
| "suffix": "" |
| }, |
| { |
| "first": "Pierre", |
| "middle": [], |
| "last": "Senellart", |
| "suffix": "" |
| } |
| ], |
| "year": 2010, |
| "venue": "Proceedings of the third ACM international conference on Web search and data mining", |
| "volume": "", |
| "issue": "", |
| "pages": "131--140", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Alban Galland, Serge Abiteboul, Am\u00e9lie Marian, and Pierre Senellart. 2010. Corroborating information from disagreeing views. In Proceedings of the third ACM international conference on Web search and data mining, pages 131-140. ACM.", |
| "links": null |
| }, |
| "BIBREF5": { |
| "ref_id": "b5", |
| "title": "Learning binary relations using weighted majority voting", |
| "authors": [ |
| { |
| "first": "A", |
| "middle": [], |
| "last": "Sally", |
| "suffix": "" |
| }, |
| { |
| "first": "Manfred", |
| "middle": [ |
| "K" |
| ], |
| "last": "Goldman", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Warmuth", |
| "suffix": "" |
| } |
| ], |
| "year": 1995, |
| "venue": "Machine Learning", |
| "volume": "20", |
| "issue": "3", |
| "pages": "245--271", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Sally A Goldman and Manfred K Warmuth. 1995. Learning binary relations using weighted majority voting. Machine Learning, 20(3):245-271.", |
| "links": null |
| }, |
| "BIBREF6": { |
| "ref_id": "b6", |
| "title": "Biperpedia: An ontology for search applications", |
| "authors": [ |
| { |
| "first": "Rahul", |
| "middle": [], |
| "last": "Gupta", |
| "suffix": "" |
| }, |
| { |
| "first": "Alon", |
| "middle": [], |
| "last": "Halevy", |
| "suffix": "" |
| }, |
| { |
| "first": "Xuezhi", |
| "middle": [], |
| "last": "Wang", |
| "suffix": "" |
| }, |
| { |
| "first": "Steven", |
| "middle": [ |
| "Euijong" |
| ], |
| "last": "Whang", |
| "suffix": "" |
| }, |
| { |
| "first": "Fei", |
| "middle": [], |
| "last": "Wu", |
| "suffix": "" |
| } |
| ], |
| "year": 2014, |
| "venue": "Proceedings of the VLDB Endowment", |
| "volume": "7", |
| "issue": "", |
| "pages": "505--516", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Rahul Gupta, Alon Halevy, Xuezhi Wang, Steven Eu- ijong Whang, and Fei Wu. 2014. Biperpedia: An ontology for search applications. Proceedings of the VLDB Endowment, 7(7):505-516.", |
| "links": null |
| }, |
| "BIBREF7": { |
| "ref_id": "b7", |
| "title": "A unified index for spatio-temporal keyword queries", |
| "authors": [ |
| { |
| "first": "Tuan-Anh", |
| "middle": [], |
| "last": "Hoang-Vu", |
| "suffix": "" |
| }, |
| { |
| "first": "T", |
| "middle": [], |
| "last": "Huy", |
| "suffix": "" |
| }, |
| { |
| "first": "Juliana", |
| "middle": [], |
| "last": "Vo", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Freire", |
| "suffix": "" |
| } |
| ], |
| "year": 2016, |
| "venue": "Proceedings of the 25th ACM International on Conference on Information and Knowledge Management", |
| "volume": "", |
| "issue": "", |
| "pages": "135--144", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Tuan-Anh Hoang-Vu, Huy T Vo, and Juliana Freire. 2016. A unified index for spatio-temporal keyword queries. In Proceedings of the 25th ACM Interna- tional on Conference on Information and Knowl- edge Management, pages 135-144. ACM.", |
| "links": null |
| }, |
| "BIBREF8": { |
| "ref_id": "b8", |
| "title": "Metapad: Meta pattern discovery from massive text corpora", |
| "authors": [ |
| { |
| "first": "Meng", |
| "middle": [], |
| "last": "Jiang", |
| "suffix": "" |
| }, |
| { |
| "first": "Jingbo", |
| "middle": [], |
| "last": "Shang", |
| "suffix": "" |
| }, |
| { |
| "first": "Taylor", |
| "middle": [], |
| "last": "Cassidy", |
| "suffix": "" |
| }, |
| { |
| "first": "Xiang", |
| "middle": [], |
| "last": "Ren", |
| "suffix": "" |
| }, |
| { |
| "first": "M", |
| "middle": [], |
| "last": "Lance", |
| "suffix": "" |
| }, |
| { |
| "first": "Timothy", |
| "middle": [ |
| "P" |
| ], |
| "last": "Kaplan", |
| "suffix": "" |
| }, |
| { |
| "first": "Jiawei", |
| "middle": [], |
| "last": "Hanratty", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Han", |
| "suffix": "" |
| } |
| ], |
| "year": 2017, |
| "venue": "Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining", |
| "volume": "", |
| "issue": "", |
| "pages": "877--886", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Meng Jiang, Jingbo Shang, Taylor Cassidy, Xiang Ren, Lance M Kaplan, Timothy P Hanratty, and Jiawei Han. 2017. Metapad: Meta pattern discovery from massive text corpora. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowl- edge Discovery and Data Mining, pages 877-886. ACM.", |
| "links": null |
| }, |
| "BIBREF9": { |
| "ref_id": "b9", |
| "title": "The role of\" condition\" a novel scientific knowledge graph representation and construction model", |
| "authors": [ |
| { |
| "first": "Tianwen", |
| "middle": [], |
| "last": "Jiang", |
| "suffix": "" |
| }, |
| { |
| "first": "Tong", |
| "middle": [], |
| "last": "Zhao", |
| "suffix": "" |
| }, |
| { |
| "first": "Bing", |
| "middle": [], |
| "last": "Qin", |
| "suffix": "" |
| }, |
| { |
| "first": "Ting", |
| "middle": [], |
| "last": "Liu", |
| "suffix": "" |
| }, |
| { |
| "first": "V", |
| "middle": [], |
| "last": "Nitesh", |
| "suffix": "" |
| }, |
| { |
| "first": "Meng", |
| "middle": [], |
| "last": "Chawla", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Jiang", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining", |
| "volume": "", |
| "issue": "", |
| "pages": "1634--1642", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V Chawla, and Meng Jiang. 2019. The role of\" condition\" a novel scientific knowledge graph representation and construction model. In Proceed- ings of the 25th ACM SIGKDD International Con- ference on Knowledge Discovery & Data Mining, pages 1634-1642.", |
| "links": null |
| }, |
| "BIBREF10": { |
| "ref_id": "b10", |
| "title": "Truepie: Discovering reliable patterns in pattern-based information extraction", |
| "authors": [ |
| { |
| "first": "Qi", |
| "middle": [], |
| "last": "Li", |
| "suffix": "" |
| }, |
| { |
| "first": "Meng", |
| "middle": [], |
| "last": "Jiang", |
| "suffix": "" |
| }, |
| { |
| "first": "Xikun", |
| "middle": [], |
| "last": "Zhang", |
| "suffix": "" |
| }, |
| { |
| "first": "Meng", |
| "middle": [], |
| "last": "Qu", |
| "suffix": "" |
| }, |
| { |
| "first": "Timothy", |
| "middle": [ |
| "P" |
| ], |
| "last": "Hanratty", |
| "suffix": "" |
| }, |
| { |
| "first": "Jing", |
| "middle": [], |
| "last": "Gao", |
| "suffix": "" |
| }, |
| { |
| "first": "Jiawei", |
| "middle": [], |
| "last": "Han", |
| "suffix": "" |
| } |
| ], |
| "year": 2018, |
| "venue": "Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining", |
| "volume": "", |
| "issue": "", |
| "pages": "1675--1684", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Qi Li, Meng Jiang, Xikun Zhang, Meng Qu, Timothy P Hanratty, Jing Gao, and Jiawei Han. 2018. Truepie: Discovering reliable patterns in pattern-based infor- mation extraction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1675-1684. ACM.", |
| "links": null |
| }, |
| "BIBREF11": { |
| "ref_id": "b11", |
| "title": "On the discovery of evolving truth", |
| "authors": [ |
| { |
| "first": "Yaliang", |
| "middle": [], |
| "last": "Li", |
| "suffix": "" |
| }, |
| { |
| "first": "Qi", |
| "middle": [], |
| "last": "Li", |
| "suffix": "" |
| }, |
| { |
| "first": "Jing", |
| "middle": [], |
| "last": "Gao", |
| "suffix": "" |
| }, |
| { |
| "first": "Lu", |
| "middle": [], |
| "last": "Su", |
| "suffix": "" |
| }, |
| { |
| "first": "Bo", |
| "middle": [], |
| "last": "Zhao", |
| "suffix": "" |
| }, |
| { |
| "first": "Wei", |
| "middle": [], |
| "last": "Fan", |
| "suffix": "" |
| }, |
| { |
| "first": "Jiawei", |
| "middle": [], |
| "last": "Han", |
| "suffix": "" |
| } |
| ], |
| "year": 2015, |
| "venue": "Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining", |
| "volume": "", |
| "issue": "", |
| "pages": "675--684", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Yaliang Li, Qi Li, Jing Gao, Lu Su, Bo Zhao, Wei Fan, and Jiawei Han. 2015. On the discovery of evolv- ing truth. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 675-684. ACM.", |
| "links": null |
| }, |
| "BIBREF12": { |
| "ref_id": "b12", |
| "title": "Patty: a taxonomy of relational patterns with semantic types", |
| "authors": [ |
| { |
| "first": "Ndapandula", |
| "middle": [], |
| "last": "Nakashole", |
| "suffix": "" |
| }, |
| { |
| "first": "Gerhard", |
| "middle": [], |
| "last": "Weikum", |
| "suffix": "" |
| }, |
| { |
| "first": "Fabian", |
| "middle": [], |
| "last": "Suchanek", |
| "suffix": "" |
| } |
| ], |
| "year": 2012, |
| "venue": "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning", |
| "volume": "", |
| "issue": "", |
| "pages": "1135--1145", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Ndapandula Nakashole, Gerhard Weikum, and Fabian Suchanek. 2012. Patty: a taxonomy of relational patterns with semantic types. In Proceedings of the 2012 Joint Conference on Empirical Methods in Nat- ural Language Processing and Computational Natu- ral Language Learning, pages 1135-1145. Associa- tion for Computational Linguistics.", |
| "links": null |
| }, |
| "BIBREF13": { |
| "ref_id": "b13", |
| "title": "Temporal anchoring of events for the timebank corpus", |
| "authors": [ |
| { |
| "first": "Nils", |
| "middle": [], |
| "last": "Reimers", |
| "suffix": "" |
| }, |
| { |
| "first": "Nazanin", |
| "middle": [], |
| "last": "Dehghani", |
| "suffix": "" |
| }, |
| { |
| "first": "Iryna", |
| "middle": [], |
| "last": "Gurevych", |
| "suffix": "" |
| } |
| ], |
| "year": 2016, |
| "venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics", |
| "volume": "1", |
| "issue": "", |
| "pages": "2195--2204", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Nils Reimers, Nazanin Dehghani, and Iryna Gurevych. 2016. Temporal anchoring of events for the time- bank corpus. In Proceedings of the 54th Annual Meeting of the Association for Computational Lin- guistics (Volume 1: Long Papers), volume 1, pages 2195-2204.", |
| "links": null |
| }, |
| "BIBREF14": { |
| "ref_id": "b14", |
| "title": "Automated phrase mining from massive text corpora", |
| "authors": [ |
| { |
| "first": "Jingbo", |
| "middle": [], |
| "last": "Shang", |
| "suffix": "" |
| }, |
| { |
| "first": "Jialu", |
| "middle": [], |
| "last": "Liu", |
| "suffix": "" |
| }, |
| { |
| "first": "Meng", |
| "middle": [], |
| "last": "Jiang", |
| "suffix": "" |
| }, |
| { |
| "first": "Xiang", |
| "middle": [], |
| "last": "Ren", |
| "suffix": "" |
| }, |
| { |
| "first": "Clare", |
| "middle": [ |
| "R" |
| ], |
| "last": "Voss", |
| "suffix": "" |
| }, |
| { |
| "first": "Jiawei", |
| "middle": [], |
| "last": "Han", |
| "suffix": "" |
| } |
| ], |
| "year": 2018, |
| "venue": "IEEE Transactions on Knowledge and Data Engineering", |
| "volume": "30", |
| "issue": "10", |
| "pages": "1825--1837", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Jingbo Shang, Jialu Liu, Meng Jiang, Xiang Ren, Clare R Voss, and Jiawei Han. 2018. Automated phrase mining from massive text corpora. IEEE Transactions on Knowledge and Data Engineering, 30(10):1825-1837.", |
| "links": null |
| }, |
| "BIBREF15": { |
| "ref_id": "b15", |
| "title": "Towards temporal scoping of relational facts based on wikipedia data", |
| "authors": [ |
| { |
| "first": "Avirup", |
| "middle": [], |
| "last": "Sil", |
| "suffix": "" |
| }, |
| { |
| "first": "Silviu-Petru", |
| "middle": [], |
| "last": "Cucerzan", |
| "suffix": "" |
| } |
| ], |
| "year": 2014, |
| "venue": "Proceedings of the Eighteenth Conference on Computational Natural Language Learning", |
| "volume": "", |
| "issue": "", |
| "pages": "109--118", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Avirup Sil and Silviu-Petru Cucerzan. 2014. To- wards temporal scoping of relational facts based on wikipedia data. In Proceedings of the Eigh- teenth Conference on Computational Natural Lan- guage Learning, pages 109-118.", |
| "links": null |
| }, |
| "BIBREF16": { |
| "ref_id": "b16", |
| "title": "Content-driven trust propagation framework", |
| "authors": [ |
| { |
| "first": "Chengxiang", |
| "middle": [], |
| "last": "Vg Vydiswaran", |
| "suffix": "" |
| }, |
| { |
| "first": "Dan", |
| "middle": [], |
| "last": "Zhai", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Roth", |
| "suffix": "" |
| } |
| ], |
| "year": 2011, |
| "venue": "Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining", |
| "volume": "", |
| "issue": "", |
| "pages": "974--982", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "VG Vydiswaran, ChengXiang Zhai, and Dan Roth. 2011. Content-driven trust propagation framework. In Proceedings of the 17th ACM SIGKDD interna- tional conference on Knowledge discovery and data mining, pages 974-982. ACM.", |
| "links": null |
| }, |
| "BIBREF17": { |
| "ref_id": "b17", |
| "title": "Truth discovery algorithms: An experimental evaluation", |
| "authors": [ |
| { |
| "first": "Dalia", |
| "middle": [], |
| "last": "Attia Waguih", |
| "suffix": "" |
| }, |
| { |
| "first": "Laure", |
| "middle": [], |
| "last": "Berti-Equille", |
| "suffix": "" |
| } |
| ], |
| "year": 2014, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": { |
| "arXiv": [ |
| "arXiv:1409.6428" |
| ] |
| }, |
| "num": null, |
| "urls": [], |
| "raw_text": "Dalia Attia Waguih and Laure Berti-Equille. 2014. Truth discovery algorithms: An experimental eval- uation. arXiv:1409.6428.", |
| "links": null |
| }, |
| "BIBREF18": { |
| "ref_id": "b18", |
| "title": "A novel unsupervised approach for precise temporal slot filling from incomplete and noisy temporal contexts", |
| "authors": [ |
| { |
| "first": "Xueying", |
| "middle": [], |
| "last": "Wang", |
| "suffix": "" |
| }, |
| { |
| "first": "Haiqiao", |
| "middle": [], |
| "last": "Zhang", |
| "suffix": "" |
| }, |
| { |
| "first": "Qi", |
| "middle": [], |
| "last": "Li", |
| "suffix": "" |
| }, |
| { |
| "first": "Yiyu", |
| "middle": [], |
| "last": "Shi", |
| "suffix": "" |
| }, |
| { |
| "first": "Meng", |
| "middle": [], |
| "last": "Jiang", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "The World Wide Web Conference", |
| "volume": "", |
| "issue": "", |
| "pages": "3328--3334", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Xueying Wang, Haiqiao Zhang, Qi Li, Yiyu Shi, and Meng Jiang. 2019. A novel unsupervised approach for precise temporal slot filling from incomplete and noisy temporal contexts. In The World Wide Web Conference, pages 3328-3334. ACM.", |
| "links": null |
| }, |
| "BIBREF19": { |
| "ref_id": "b19", |
| "title": "Towards confidence in the truth: A bootstrapping based truth discovery approach", |
| "authors": [ |
| { |
| "first": "Houping", |
| "middle": [], |
| "last": "Xiao", |
| "suffix": "" |
| }, |
| { |
| "first": "Jing", |
| "middle": [], |
| "last": "Gao", |
| "suffix": "" |
| }, |
| { |
| "first": "Qi", |
| "middle": [], |
| "last": "Li", |
| "suffix": "" |
| }, |
| { |
| "first": "Fenglong", |
| "middle": [], |
| "last": "Ma", |
| "suffix": "" |
| }, |
| { |
| "first": "Lu", |
| "middle": [], |
| "last": "Su", |
| "suffix": "" |
| }, |
| { |
| "first": "Yunlong", |
| "middle": [], |
| "last": "Feng", |
| "suffix": "" |
| }, |
| { |
| "first": "Aidong", |
| "middle": [], |
| "last": "Zhang", |
| "suffix": "" |
| } |
| ], |
| "year": 2016, |
| "venue": "Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining", |
| "volume": "", |
| "issue": "", |
| "pages": "1935--1944", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Houping Xiao, Jing Gao, Qi Li, Fenglong Ma, Lu Su, Yunlong Feng, and Aidong Zhang. 2016. Towards confidence in the truth: A bootstrapping based truth discovery approach. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowl- edge Discovery and Data Mining, pages 1935-1944. ACM.", |
| "links": null |
| }, |
| "BIBREF20": { |
| "ref_id": "b20", |
| "title": "Believe it today or tomorrow? detecting untrustworthy information from dynamic multi-source data", |
| "authors": [ |
| { |
| "first": "Houping", |
| "middle": [], |
| "last": "Xiao", |
| "suffix": "" |
| }, |
| { |
| "first": "Yaliang", |
| "middle": [], |
| "last": "Li", |
| "suffix": "" |
| }, |
| { |
| "first": "Jing", |
| "middle": [], |
| "last": "Gao", |
| "suffix": "" |
| }, |
| { |
| "first": "Fei", |
| "middle": [], |
| "last": "Wang", |
| "suffix": "" |
| }, |
| { |
| "first": "Liang", |
| "middle": [], |
| "last": "Ge", |
| "suffix": "" |
| }, |
| { |
| "first": "Wei", |
| "middle": [], |
| "last": "Fan", |
| "suffix": "" |
| }, |
| { |
| "first": "H", |
| "middle": [], |
| "last": "Long", |
| "suffix": "" |
| }, |
| { |
| "first": "Deepak S", |
| "middle": [], |
| "last": "Vu", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Turaga", |
| "suffix": "" |
| } |
| ], |
| "year": 2015, |
| "venue": "Proceedings of the 2015 SIAM International Conference on Data Mining", |
| "volume": "", |
| "issue": "", |
| "pages": "397--405", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Houping Xiao, Yaliang Li, Jing Gao, Fei Wang, Liang Ge, Wei Fan, Long H Vu, and Deepak S Turaga. 2015. Believe it today or tomorrow? detecting un- trustworthy information from dynamic multi-source data. In Proceedings of the 2015 SIAM International Conference on Data Mining, pages 397-405. SIAM.", |
| "links": null |
| }, |
| "BIBREF21": { |
| "ref_id": "b21", |
| "title": "Truth discovery with multiple conflicting information providers on the web", |
| "authors": [ |
| { |
| "first": "Xiaoxin", |
| "middle": [], |
| "last": "Yin", |
| "suffix": "" |
| }, |
| { |
| "first": "Jiawei", |
| "middle": [], |
| "last": "Han", |
| "suffix": "" |
| }, |
| { |
| "first": "S Yu", |
| "middle": [], |
| "last": "Philip", |
| "suffix": "" |
| } |
| ], |
| "year": 2008, |
| "venue": "IEEE Transactions on Knowledge and Data Engineering", |
| "volume": "20", |
| "issue": "6", |
| "pages": "796--808", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Xiaoxin Yin, Jiawei Han, and S Yu Philip. 2008. Truth discovery with multiple conflicting informa- tion providers on the web. IEEE Transactions on Knowledge and Data Engineering, 20(6):796-808.", |
| "links": null |
| }, |
| "BIBREF22": { |
| "ref_id": "b22", |
| "title": "Semi-supervised truth discovery", |
| "authors": [ |
| { |
| "first": "Xiaoxin", |
| "middle": [], |
| "last": "Yin", |
| "suffix": "" |
| }, |
| { |
| "first": "Wenzhao", |
| "middle": [], |
| "last": "Tan", |
| "suffix": "" |
| } |
| ], |
| "year": 2011, |
| "venue": "Proceedings of the 20th international conference on World wide web", |
| "volume": "", |
| "issue": "", |
| "pages": "217--226", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Xiaoxin Yin and Wenzhao Tan. 2011. Semi-supervised truth discovery. In Proceedings of the 20th interna- tional conference on World wide web, pages 217- 226. ACM.", |
| "links": null |
| }, |
| "BIBREF23": { |
| "ref_id": "b23", |
| "title": "Faceted hierarchy: A new graph type to organize scientific concepts and a construction method", |
| "authors": [ |
| { |
| "first": "Qingkai", |
| "middle": [], |
| "last": "Zeng", |
| "suffix": "" |
| }, |
| { |
| "first": "Mengxia", |
| "middle": [], |
| "last": "Yu", |
| "suffix": "" |
| }, |
| { |
| "first": "Wenhao", |
| "middle": [], |
| "last": "Yu", |
| "suffix": "" |
| }, |
| { |
| "first": "Jinjun", |
| "middle": [], |
| "last": "Xiong", |
| "suffix": "" |
| }, |
| { |
| "first": "Yiyu", |
| "middle": [], |
| "last": "Shi", |
| "suffix": "" |
| }, |
| { |
| "first": "Meng", |
| "middle": [], |
| "last": "Jiang", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)", |
| "volume": "", |
| "issue": "", |
| "pages": "140--150", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Qingkai Zeng, Mengxia Yu, Wenhao Yu, Jinjun Xiong, Yiyu Shi, and Meng Jiang. 2019. Faceted hierarchy: A new graph type to organize scientific concepts and a construction method. In Proceedings of the Thir- teenth Workshop on Graph-Based Methods for Nat- ural Language Processing (TextGraphs-13), pages 140-150.", |
| "links": null |
| }, |
| "BIBREF24": { |
| "ref_id": "b24", |
| "title": "Taxogen: Constructing topical concept taxonomy by adaptive term embedding and clustering", |
| "authors": [ |
| { |
| "first": "Chao", |
| "middle": [], |
| "last": "Zhang", |
| "suffix": "" |
| }, |
| { |
| "first": "Fangbo", |
| "middle": [], |
| "last": "Tao", |
| "suffix": "" |
| }, |
| { |
| "first": "Xiusi", |
| "middle": [], |
| "last": "Chen", |
| "suffix": "" |
| }, |
| { |
| "first": "Jiaming", |
| "middle": [], |
| "last": "Shen", |
| "suffix": "" |
| }, |
| { |
| "first": "Meng", |
| "middle": [], |
| "last": "Jiang", |
| "suffix": "" |
| }, |
| { |
| "first": "Brian", |
| "middle": [], |
| "last": "Sadler", |
| "suffix": "" |
| }, |
| { |
| "first": "Michelle", |
| "middle": [], |
| "last": "Vanni", |
| "suffix": "" |
| }, |
| { |
| "first": "Jiawei", |
| "middle": [], |
| "last": "Han", |
| "suffix": "" |
| } |
| ], |
| "year": 2018, |
| "venue": "Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Chao Zhang, Fangbo Tao, Xiusi Chen, Jiaming Shen, Meng Jiang, Brian Sadler, Michelle Vanni, and Ji- awei Han. 2018a. Taxogen: Constructing topi- cal concept taxonomy by adaptive term embedding and clustering. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.", |
| "links": null |
| }, |
| "BIBREF25": { |
| "ref_id": "b25", |
| "title": "Texttruth: an unsupervised approach to discover trustworthy information from multi-sourced text data", |
| "authors": [ |
| { |
| "first": "Hengtong", |
| "middle": [], |
| "last": "Zhang", |
| "suffix": "" |
| }, |
| { |
| "first": "Yaliang", |
| "middle": [], |
| "last": "Li", |
| "suffix": "" |
| }, |
| { |
| "first": "Fenglong", |
| "middle": [], |
| "last": "Ma", |
| "suffix": "" |
| }, |
| { |
| "first": "Jing", |
| "middle": [], |
| "last": "Gao", |
| "suffix": "" |
| }, |
| { |
| "first": "Lu", |
| "middle": [], |
| "last": "Su", |
| "suffix": "" |
| } |
| ], |
| "year": 2018, |
| "venue": "Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining", |
| "volume": "", |
| "issue": "", |
| "pages": "2729--2737", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Hengtong Zhang, Yaliang Li, Fenglong Ma, Jing Gao, and Lu Su. 2018b. Texttruth: an unsupervised approach to discover trustworthy information from multi-sourced text data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowl- edge Discovery & Data Mining, pages 2729-2737. ACM.", |
| "links": null |
| }, |
| "BIBREF26": { |
| "ref_id": "b26", |
| "title": "A probabilistic model for estimating real-valued truth from conflicting sources", |
| "authors": [ |
| { |
| "first": "Bo", |
| "middle": [], |
| "last": "Zhao", |
| "suffix": "" |
| }, |
| { |
| "first": "Jiawei", |
| "middle": [], |
| "last": "Han", |
| "suffix": "" |
| } |
| ], |
| "year": 2012, |
| "venue": "Proc. of QDB", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Bo Zhao and Jiawei Han. 2012. A probabilistic model for estimating real-valued truth from conflict- ing sources. Proc. of QDB.", |
| "links": null |
| }, |
| "BIBREF27": { |
| "ref_id": "b27", |
| "title": "A bayesian approach to discovering truth from conflicting sources for data integration", |
| "authors": [ |
| { |
| "first": "Bo", |
| "middle": [], |
| "last": "Zhao", |
| "suffix": "" |
| }, |
| { |
| "first": "Jim", |
| "middle": [], |
| "last": "Benjamin Ip Rubinstein", |
| "suffix": "" |
| }, |
| { |
| "first": "Jiawei", |
| "middle": [], |
| "last": "Gemmell", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Han", |
| "suffix": "" |
| } |
| ], |
| "year": 2012, |
| "venue": "Proceedings of the VLDB Endowment", |
| "volume": "5", |
| "issue": "", |
| "pages": "550--561", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Bo Zhao, Benjamin IP Rubinstein, Jim Gemmell, and Jiawei Han. 2012. A bayesian approach to dis- covering truth from conflicting sources for data in- tegration. Proceedings of the VLDB Endowment, 5(6):550-561.", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
| "FIGREF0": { |
| "num": null, |
| "type_str": "figure", |
| "text": "The former French [Country: France] president Jacques Chirac [Person], a self-styled affable rogue who was head of state from 1995 [temporal tag] to 2007 ...\" (posted on Sept. 26, 2019 [text generation time]) 2) \"... Emmanuel Macron [Person], now President of France [Country], graduated from ENA in 2004 [temporal tag] ...\" (posted on Sept. 19, 2019 [text generation time])", |
| "uris": null |
| }, |
| "FIGREF1": { |
| "num": null, |
| "type_str": "figure", |
| "text": "Probabilistic Graphic Model with Commonsense Constraint {C 1(e,t)\u22121v , C 1(v)\u22121e }", |
| "uris": null |
| }, |
| "TABREF0": { |
| "num": null, |
| "type_str": "table", |
| "content": "<table><tr><td>Symbol</td><td>Description</td></tr><tr><td>\u03b8 f</td><td>[0, 1], trustworthiness of tem-</td></tr><tr><td/><td>poral fact tuple f</td></tr><tr><td>l f</td><td>Boolean: label of temporal</td></tr><tr><td/><td>fact f</td></tr><tr><td>o e</td><td/></tr></table>", |
| "html": null, |
| "text": "Symbols and their descriptions." |
| }, |
| "TABREF1": { |
| "num": null, |
| "type_str": "table", |
| "content": "<table><tr><td/><td/><td>Constraints</td><td/><td colspan=\"3\">Evaluation Setting</td></tr><tr><td>Method</td><td colspan=\"2\">e: Country; v: Person; t: year</td><td colspan=\"2\">On (e,v,t)</td><td colspan=\"2\">On (e,v,[t min ,t max ])</td></tr><tr><td/><td>C 1v\u22121e</td><td>C 1(e,t)\u22121v</td><td>AUC</td><td>F1</td><td>AUC</td><td>F1</td></tr><tr><td>TRUTHFINDER</td><td/><td/><td colspan=\"4\">0.0006 0.0012 0.0006 0.0012</td></tr><tr><td>LTM</td><td/><td/><td colspan=\"4\">0.1319 0.0199 0.2030 0.0218</td></tr><tr><td>LTM</td><td/><td/><td colspan=\"4\">0.0212 0.0505 0.0407 0.0793</td></tr><tr><td>TRUEPIE</td><td/><td/><td colspan=\"4\">0.0587 0.1430 0.0587 0.1430</td></tr><tr><td>MAJVOTE</td><td/><td/><td colspan=\"4\">0.3336 0.4318 0.4958 0.5927</td></tr><tr><td>TFWIN</td><td/><td/><td colspan=\"4\">0.4746 0.6361 0.5523 0.6489</td></tr><tr><td>Ours (PGMCC)</td><td/><td/><td colspan=\"4\">0.4840 0.6502 0.6006 0.7254</td></tr><tr><td>Ours (PGMCC)</td><td/><td/><td colspan=\"4\">0.4987 0.6634 0.6075 0.7316</td></tr></table>", |
| "html": null, |
| "text": "Our proposed model performs better than baseline methods on finding temporal facts." |
| }, |
| "TABREF2": { |
| "num": null, |
| "type_str": "table", |
| "content": "<table><tr><td>Textual Pattern p</td><td colspan=\"2\">r p (post) r p (tag)</td></tr><tr><td>president Person of Country</td><td>0.920</td><td>0.870</td></tr><tr><td>Country's current president Person,</td><td>0.978</td><td>0.250</td></tr><tr><td colspan=\"2\">Country's newly elected president , Person , 0.970</td><td>0.030</td></tr><tr><td>Person, now president of Country,</td><td>0.750</td><td>0.110</td></tr><tr><td>Person, who has ruled Country</td><td>0.438</td><td>0.994</td></tr><tr><td>$COUNTRY's former president Person</td><td>0.113</td><td>0.994</td></tr><tr><td>Person, who ruled Country</td><td>0.607</td><td>0.758</td></tr><tr><td>Country president Person signed</td><td>0.553</td><td>0.327</td></tr><tr><td>Country premier Person</td><td>0.012</td><td>0.010</td></tr><tr><td>Country foreign minister Person</td><td>0</td><td>0</td></tr><tr><td>Country golfer Person</td><td>0</td><td>0</td></tr><tr><td>4.4 Pattern Source Reliability Analysis</td><td/><td/></tr></table>", |
| "html": null, |
| "text": "Pattern's reliability for country's presidency." |
| } |
| } |
| } |
| } |
