Knowledge Graphs meet Moral Values (2020)

Abstract: Operationalizing morality is crucial for understanding multiple aspects of society that have moral values at their core – such as riots, mobilizing movements, public debates, etc. Moral Foundations Theory (MFT) has become one of the most adopted theories of morality partly due to its accompanying lexicon, the Moral Foundation Dictionary (MFD), which offers a base for computationally dealing with morality. In this work, we exploit the MFD in a novel direction by investigating how well moral values are captured by KGs. We explore three widely used KGs, and provide concept-level analogues for the MFD. Furthermore, we propose several Personalized PageRank variations in order to score all the concepts and entities in the KGs with respect to their relevance to the different moral values. Our promising results help to progress the operationalization of morality in both NLP and KG communities.

## introduction many of the choices that we make in daily life, such as political stance or position in debates on ideological topics, are influenced by our moral values @xcite @xcite . besides, moral values and moral judgments are central to decision making and cultural cohesion @xcite . the last years have seen an increasing interest in operationalizing the concept of morality as defined by psychologists, particularly from the nlp, social media, and communication communities, into an effort of extracting the latent moral dimension of texts. tweets @xcite , newspaper articles @xcite , as well as scientific articles @xcite this work is licensed under a creative commons attribution 4.0 international license. license details: http: //creativecommons.org/licenses/by/4.0/ . one of the most widely adopted theories of morality is the moral foundations theory (mft) @xcite . mft proposes at least five moral foundations, each one consisting on the one side of virtues and on the other side of vices: (1) the care/harm foundation which deals with the sensitivity towards the suffering of others; (2) the fairness/cheating foundation covering aspects of reciprocity and motivations to be fair; (3) the loyalty / betrayal foundation covering aspects of in-group cooperation, and the intuition of being loyal to one's group; (4) the authority/subversion foundation which is related to the innate intuition of endorsing hierarchies that we find just; (5) the purity / degradation foundation which deals with our innate drive of preferring cleanliness of body and soul over hedonism. the moral foundations dictionary (mfd) @xcite has been proposed as a lexicon to guide the assessment of the moral foundations of the mft in texts. it consists of a set of words and lemmas for each vice and virtue of each foundation and has become an essential resource for operationalizing moral values. nevertheless, being a word-level lexicon, this resource comes with several limitations. first, the natural ambiguity of language means that some of the words and lemmas provided can have other meanings than the ones related to the moral foundations. for instance, the stem subver* covers the ambiguous word subversion, which besides the meaning related to the authority / subversion foundation, it is used with a completely different meaning in software development. second, it contains a limited set of lemmas and words, particularly focusing on those whose main meaning is the one related to the corresponding moral foundation. this, on the one side, means that more ambiguous synonyms are not contained, and on the other side, it means that very specific rarely used words are contained. these effects lead to a high precision with poor recall, which is not necessarily the preferred strategy in many scenarios. moreover, it only contains uni-grams, and the entries are associated with either vice or virtue, without a score for the strength of the association. however, this lexicon has been widely exploited lately, and several approaches have been proposed to overcome these weaknesses, for instance, by extending it or by its projection into continuous spaces. in this work, we investigate a new direction, that of projecting the mfd lexicon on knowledge graphs (kgs) with the purpose of scoring all entities and concepts therein with respect to their relevance for each moral foundation. we envision multiple benefits from this endeavor. first, it overcomes the ambiguity and incompleteness limitations. second, entities and concepts in kgs oftentimes strongly relate to moral values. for example, rebecca reichmann tavares is a un diplomat promoting race relations and human rights, a position highly related to the fairness/cheating moral foundation. similarly, the concept history of human rights is also highly related to the same moral foundation. such concepts and entities are not part of the mfd, but their mentioning can help the detection of moral foundations expressed by the texts. third, the usefulness of kgs for many tasks resides in the fact that they provide factual knowledge such as relations between entities. still, an important drawback of current kgs is their weak representation of common sense knowledge. for example, the fact that the concept of "crime" is generally bad and undesirable cannot be derived from current kgs although such common sense knowledge is crucial, for example, for understand-ing arguments: to understand that an argument claiming that racism leads to crime is an argument against racism, it is necessary to understand that crime is bad or a vice. in this work, we propose a means of adding a moral dimension to kgs, and hence extend them with common-sense "intuition" of morality foot_0 . ## related work the main target of this work is to investigate how the moral foundations characterized by the mfd are captured in kgs, with the purpose of scoring each entity and concept in the kgs with respect to their relevance to the moral foundations. therefore, we are particularly interested in the way previous literature uses the mfd. some works @xcite @xcite simply use mfd counts either on their own or as features in supervised classifiers for determining the moral values expressed by text. works that try to overcome the issues related to the simple counts of lexicon hits embed the moral values in continuous spaces. @xcite and @xcite generate vectors for words based on a latent semantic analysis (lsa) @xcite methodology. then, for each moral value, a vector in the same space is obtained by adding up all the vectors of the modeled lexicon words. @xcite , @xcite and @xcite use word2vec @xcite or glove @xcite models to embed the words of the lexicon, and then either aggregate these vectors to produce a vector representation of each moral value, or they keep each of these vectors separate and use different strategies for determining the moral values expressed by novel embedded texts (such as k-nearest neighbor). other works that go into a direction more related to ours extend the mfd lexicon by using word-net @xcite , and then manually curate the results with human annotators @xcite or extend the annotations with values for valence and arousal @xcite . however, the methods differ from our approach as the results of these works are still word-level lexicons, while our moral value relevance annotation is done at a synset rather than word level. for example, moralstrength @xcite is built by manually expanding the mfd with new words from the same wordnet synsets. the only work that acknowledges that entities from wikipedia/dbpedia also carry a moral load is that of @xcite . they use wikipedia abstracts and dbpedia properties in order to generate features of the entities mentioned in text and use these features together with the textual features in order to classify the texts based on their moral values. however, they do not provide a moral value score of the specified entities, and they do not look into linking the mfd to the knowledge base. with respect to wordnet, this work is related to another research direction, that of automatically creating sense-level lexicons starting from wordlevel lexicons. two commonly used such lexicons are sentiwordnet @xcite and +/-effectwordnet @xcite . the first one is a sense-level lexicon providing wordnet synsets with prior sentiment polarity annotations. +/-effectwordnet provides annotations for synsets that express negative or positive effects over entities. our work of annotating wordnet synsets as well as other kg concepts with scores reflecting their relevance to moral values is complementary to the previously mentioned lexicons, as they bring in the dimension of morality. in our work, we investigate how moral values are captured in kgs such as dbpedia, wordnet, and conceptnet. we use the same intuition across all three kgs: if terms in the mfd can be linked to corresponding concepts in the kg, then the semantic relations contained in the kgs can be exploited to score all the other concepts with respect to their relevance to the moral values. these relevance scores can subsequently be used, for example, for expanding the lexicon. alternatively, they can be used directly as features in applications aiming at classifying texts based on moral values. another important motivation for our work is that kgs have the benefit of providing structured knowledge, but most of the time, this knowledge is factual (e.g., dbpedia) or lexical (e.g., wordnet). most kgs, conceptnet being one exception, lack in common sense knowledge. and knowledge such as what is generally accepted as morally good or bad is also missing in conceptnet. this work is a step in the direction of enriching kgs with such common-sense knowledge. ## approach the core idea of our approach is to map the mfd lexicon to a kg, and subsequently score all entities in the kg with respect to their relevance to moral values. we are taking a layered approach to the moral foundations, as we are interested in scoring the relevance on three levels: (1) the moral trait level, in which we are interested in the relevance of concepts with respect to each moral trait (virtue / vice). hence, each concept obtains 10 scores, two (virtue and vice) for each moral foundation. as a measure of relevance in kgs, we use personalized pagerank @xcite (ppr). our work consists of two main steps: first, we manually link the mfd entries to entries in the kgs. then, we use these kg entries as seeds for running ppr. we now describe both steps in more detail. ## experiments and discussion we evaluate the prediction on each level independently. similarly to @xcite , we use a leave-one-out evaluation. specifically, for each seed concept of a class, we run an additional ppr process for that class when the targeted seed concept is left out of the teleport vector. then, we check the relevance score the targeted seed concept obtains in this ppr process and compare it to its relevance scores for the other classes. to measure the accuracy of the prediction, we take a very straightforward approach and consider a hit when the targeted seed concept achieves the maximum relevance score for the class to which it belongs when it is left out. therefore, we compute the accuracy for a class c as the percent of seed concepts of c that obtained the maximum score across all classes for class c, when their score for class c is computed as their ppr score when left out of the seed set. ## conclusion in this work, we investigated how moral traits, foundations, and polarity based on the mft are captured in three widely used kgs. our analysis reveals big differences between the three explored kgs, both quantitatively and qualitatively. con-ceptnet achieves high accuracies at predicting the class of seeds in a leave-one-out evaluation. the results on wordnet are well aligned with results obtained by related work in a similar evaluation. lastly, the seed class prediction accuracy of dbpedia scores last among the three datasets, but still significantly higher than random, and it comes with the advantage of dealing with entities such as people and organizations. all kgs manage to accurately discriminate between virtues and vices, which is already a great step towards automatically telling the good from the bad. the more complex problem of predicting the foundation and the granular trait can still undergo substantial improvements, particularly on wordnet and dbpedia. however, given that our method is completely unsupervised, using just personalized pagerank, we conclude that there is great potential in bringing morality common-sense into knowledge graphs. as future work, we are committed to further analyzing our approach, particularly on applications such as classification of texts with respect to moral values.