Unsupervised Fact Checking by Counter-Weighted Positive and Negative Evidential Paths in A Knowledge Graph (2020)

Abstract: Misinformation spreads across media, community, and knowledge graphs in the Web by not only human agents but also information extraction algorithms that extract factual statements from unstructured textual data to populate the existing knowledge graphs. Traditional fact checking by experts or crowds is increasingly difficult to keep pace with the volume of newly created misinformation in the Web. Therefore, it is important and necessary to enhance the computational ability to determine whether a given factual statement is truthful or not. We view this problem as a truth scoring task in a knowledge graph. We present a novel rule-based approach that finds positive and negative evidential paths in a knowledge graph for a given factual statement and calculates a truth score for the given statement by unsupervised ensemble of the found positive and negative evidential paths. For example, we can determine the factual statement “United States is the birth place of Barack Obama” as truthful if there is the positive evidential path (Barack Obama, birthPlace, Hawaii) ∧ (Hawaii, country, United States) in a knowledge graph. For another example, we can determine the factual statement “Canada is the nationality of Barack Obama” as untruthful if there is the negative evidential path (Barack Obama, nationality, United States) ∧ (United States, ≠, Canada) in a knowledge graph. For evaluating on a real-world situation, we constructed an evaluation dataset by labeling truth or untruth label on factual statements that were extracted from Wikipedia texts by using the state-of-the-art BERT-based information extraction system. Our evaluation results show that our approach outperforms the state-of-the-art unsupervised approaches significantly by up to 0.12 AUC-ROC and even outperforms the supervised approach by up to 0.05 AUC-ROC not only in our dataset but also in the two different standard datasets.

## introduction 1 misinformation in the web creates a situation in which false statements compete for attention to true statement necessary for users and applications. misinformation in media and community makes difficult for users to search the information they need and misinformation in knowledge graphs makes difficult for applications to get the outputs they expect. this problem is common and getting worse in modern digital society. although a lot of information in the web is a good resource, there is certainly no guarantee that a given factual statement is truth or not. in order not to be fooled by false statements, it is necessary to separate truth from untruth by assessing truthfulness of factual statements. we represent a factual statement as a triple (subject, predicate, object) where subject and object are entities that have a relationship between them as indicated by predicate. for example, "leonardo da vinci is known for mona lisa" can be represented as (leonardo da vinci, knownfor, mona lisa). a set of such triples is called a knowledge graph where nodes represent the entities and directed edges represent the predicates. different predicates can be represented by edge types. if a knowledge graph was complete to know all the facts, the fact checking would be as easy as checking whether a given factual statement is contained in a knowledge graph or not. in reality, a knowledge graph has limited and sparse information. some information extraction algorithms @xcite try to populate incomplete knowledge graphs by finding missing facts from unstructured textual data in the web. however, as the information extraction task is challenging and an accuracy of such algorithms is not yet complete, they often produce incorrect outputs which result in corrupting a knowledge graph with false statements. our dataset presented in this paper indicates that 83.51% of the factual statements extracted from wikipedia texts by the state-of-the-art bert-based information extractor are actually false statements. our goal is to determine truthfulness of factual statements which can serve as a key clue to separate true statements from false ones. we view this problem as a truth scoring task in a knowledge graph which is to assign a truth score ranging from 0.0 (untruthful) to 1.0 (truthful) to a given factual statement based on supporting evidential paths that can be found in a knowledge graph. in this paper, we present a novel rule-based unsupervised approach that uses positive and negative rules to find positive and negative evidential paths to calculate a truth score for a given factual statement. our contributions are as follows: (1) an unsupervised fact checking approach that outperforms the stateof-the-art unsupervised approaches significantly by up to 0.12 auc-roc and even outperforms the supervised approach by up to 0.05 auc-roc in the three different datasets. (2) a novel counter-weight measure for a rule, which considers not only correct examples but also counter examples for calculating a weight, and is more effective for a truth scoring task by up to 0.2 auc-roc than the existing weight measures. we believe that this is the key factor for outperforming the existing supervised approach. (3) a novel negative sampling method based on distant local closed world assumption (d-lcwa), which is more effective for a truth scoring task by up to 0.05 auc-roc than the existing negative sampling methods. (4) a novel evaluation dataset for a fact checking problem, which comprises the factual statements missing in a knowledge graph. it makes our dataset more suitable than the existing datasets for evaluating the ability to validate additional facts missing in a knowledge graph. ## related work approaches for truth scoring can be broadly classified into two types: (1) approaches that use unstructured textual data to find supporting evidential sentences for a given statement @xcite @xcite , and (2) approaches that use a knowledge graph to find supporting evidential paths for a given statement @xcite @xcite . the latter approaches are more relevant to our approach presented herein. there are mainly three sub-types of work using a knowledge graph for truth scoring: (2-1) the first type of works uses only positive evidential paths for truth scoring. for example, the factual statement (leonardo da vinci, knownfor, mona lisa) will be truthful if there is the supporting positive evidential fact (leonardo da vinci, author, mona lisa) in a knowledge graph. @xcite proposed a network flow-based unsupervised approach, called knowledge stream (kstream) and relational knowledge linker (klinker), which use a stream of knowledge to find positive evidential paths supporting that a given statement is true. @xcite proposed a meta path-based unsupervised approach, called coppal, which uses a corroborative meta path to find the positive evidential paths supporting that a given statement is true. although positive evidence is a good clue for determining truthfulness of a statement, in some cases, negative evidence is necessary because of the incompleteness of a knowledge graph. for example, if there are the facts @xcite and @xcite in a knowledge graph and the knowledge graph does not have any more information about the two people, we can only determine the given statement (barack obama, child, ann dunham) as false by the negative evidential path @xcite @xcite that means "the birth year of barack obama is later than that of ann dunham", which implies ann dunham cannot be a child of barack obama. on the other side, (2-2) the second type of works uses only negative evidential paths for truth scoring. this type of approach suffers from the afore-mentioned same issue with the first type of approach. @xcite proposed a rule-based unsupervised approach, called rudik, which uses negative rules to find the negative evidential paths supporting that a given statement is false. rudik uses a negative sampling based on extended local closed world assumption (e-lcwa) for generating negative examples used for learning negative rules. the e-lcwa-based negative sampling suffers from generating false negatives that are actually not false but true. for example, at least 47.54% of the negative examples for relatives generated by the e-lcwa-based negative sampling are actually true according to our analysis. (2-3) the last type of works uses both types of evidential paths for truth scoring. this type of approach suffers less from the afore-mentioned issue than the first and second type of approach. our ablation study shows that using both types of evidence is more effective for a truth scoring task by up to 0.25 auc-roc than only using a single type of evidence. @xcite proposed a predicate path-based supervised approach, called predpath, which uses a discriminative predicate path to find positive and negative evidential paths in a knowledge graph supporting that a given statement is true or false. predpath weights a discriminative predicate path by only considering the correct examples to be covered by the path and ignoring counter examples not to be covered by the path. a discriminative predicate path they proposed is a kind of rule, and a rule can only be properly weighted by considering whether a rule covers correct examples as well as considering whether a rule does not cover counter examples. our ablation study shows that the counter-weight for a rule, which considers correct examples as well as counter examples is more effective for a truth scoring task by up to 0.2 auc-roc than the weight for a rule which only considers correct examples. ## problem statement in this paper, we address the following problem: given a knowledge graph 𝐺 and a factual statement (𝑠, 𝑝, 𝑜), compute a truth score ranging from 0.0 (untruthful) to 1.0 (truthful) for the given factual statement. a truth score gets closer to 1.0 if the given statement becomes more truthful. our approach finds positive evidential paths supporting a given statement is true as well as negative evidential paths supporting a given statement is false to calculate a truth score for a given factual statement. to find such evidential paths, our approach uses positive and negative rules. a positive rule comprises a positive evidential path in a body part and a concluding statement in a head part. for example, (x, nationality, y) ← (x, birthplace, z) ∧ (z, country, y) is the positive rule which means the statement (x, nationality, y) is truthful if there is the positive evidential path (x, birthplace, z) ∧ (z, country, y) in a knowledge graph. a negative rule comprises a negative evidential path in a body part and a concluding statement in a head part. for example, ¬(x, nationality, y) ← (x, nationality, z) ∧ (z, ≠, y) is the negative rule that means the statement (x, nationality, y) is untruthful if there is the negative evidential path (x, nationality, z) ∧ (z, ≠, y) in a knowledge graph. ## proposed approach our approach is a pipeline that mainly consists of three steps: (1) generation of examples for training, (2) learning positive and negative rules, (3) evidence finding and truth scoring. figure 1 shows the overall workflow of the proposed approach. in the first step, we generate positive and negative examples used for learning positive and negative rules. a positive example is a true factual statement and a negative example is a false factual statement. as a knowledge graph is a set of true statements, we use a knowledge graph itself as positive examples for training. on the other hand, in most cases, a knowledge graph is not intended to contain false statements. therefore, we generate false statements from true statements in a knowledge graph by negative sampling and use them as negative examples for training. in the following step, positive and negative rules are learned by using the examples generated in the first step. when learning positive rules, positive examples are used as correct examples and negative examples are used as counter examples. on the contrary, when learning a negative rule, negative examples are used as correct examples and positive examples are used as counter examples. the rule learning step generates a set of rules from input correct examples and weights each of the generated rules by the evidential strength of paths found by the rules. for example, the rule (x, country, y) ← (x, nationality, y) has more strong evidential power than the rule (x, nationality, y) ← (x, resident, y). in the last step, given a factual statement to validate, we find positive and negative evidential paths in a knowledge graph for the given statement by learned positive and negative rules, and calculate a final truth score by unsupervised ensemble of the found positive and negative evidential paths. ## conclusion we presented a rule-based unsupervised approach for a truth scoring task in a knowledge graph, based on 1) unsupervised ensemble of positive and negative evidence found by 2) positive and negative rules which are learned from the learning examples generated by 3) d-lcwa-based negative sampling and are weighted by 4) the counter-weight considering both correct and counter examples. we validated our approach on the fact checking dataset first presented in this paper as well as on the two different standard datasets. the result showed that our unsupervised approach significantly outperforms the state-of-theart unsupervised approaches by up to 12.68% (auc-roc) and even outperforms the supervised approach by up to 5.57% (auc-roc) in the three different datasets. our approach is fully unsupervised and can be easily extended to a wide range of predicates in a knowledge graph.