The Need to Move beyond Triples
Almost all major knowledge bases are concerned mainly with binary relationships between entities. In this vision paper, we argue that it is time to broaden this view: first to relations of higher arity, complex objects, and events, and then also to knowledge about knowledge: We should be able to represent why something is true, that something is not true, that something happened before something else, or that something is mainly believed. While this idea is as old as Artificial Intelligence itself, we argue that only now we have the tools to achieve it: a better understanding of our use cases and large amounts of data. We survey relevant approaches, and point out avenues of research. 1 Motivation In the past decade, information extraction has made huge progress: We can now extract facts from Web documents at large scale. The resulting knowledge bases (KBs) such as YAGO, KnowItAll, DBpedia, NELL, BabelNet, and Wikidata, contain many millions of entities, and hundreds of millions of facts about them. And yet, all of these KBs operate on an extremely reduced fraction of knowledge: They essentially focus on binary relations between a subject and an object. For example, a KB can know that type(autism, developmentalDisorder) or that vaccinates(MmrVaccine, measles) This knowledge representation model is called RDF. The problem is that RDF can express barely anything from the Wikipedia article about vaccines. Take for example this text about the supposed link between vaccines and autism: In February 1998, Andrew Wakefield published a paper in the medical journal The Lancet, which reported on twelve children with developmental disorders. The parents were said to have linked the start of behavioral symptoms to vaccination. The resulting controversy became the biggest science story of 2002. As a result, vaccination rates dropped sharply. In 2011, the BMJ detailed how Wakefield had faked some of the data behind the 1998 Lancet article. We could use non symbolic methods (such as distributional methods or deep learning approaches) to decide whether Wakefield's paper is trustworthy or not. But suppose that we want to decide whether there is a causal link between autism and vaccination; why we see a lower vaccination rate; or with which arguments another blog post supports the anti vaccine movement. For this, we need a more detailed understanding of the text. The machine would have to understand: The fact that something was asserted (which does not make it true) The fact that something is not true The fact that someone believes something Copyright c by the paper's authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) In: R. Campos, A. Jorge, A. Jatowt, S. Bhatia (eds. Proceedings of the Text2Story'20 Workshop, Lisbon, Portugal, 14 April 2020, published at http:/ceur ws.org The fact that something happened before something else The fact that some group of facts forms an event The fact that one event is the reason for another event The applications of such knowledge go far beyond our example. It is indispensable for tasks such as the following: The analysis of fake news: understand an article about a controversial topic, and allow reasoning on it (who said what when and why, what is the evidence, how is the perception of the claims by others) with the goal to support journalists in the fact checking of a story. The modeling of controversies: detect a controversial topic on the Web (e.g. in blogs, forums, or Twitter posts) extract opinions, and model di|erent views. The analysis of the e reputation of a company: map out cases of controversy or beliefs of valuations, together with their reasons, and their support among journalists, clients, and the general public. This can include the analysis of product reviews to identify fine grained praise or complaints. The flagging of potentially fraudulent activity: detect claims that are in contradiction with established knowledge, or violations of rules. The modeling of processes: Summaries of technical interventions often contain sequences of actions performed, causal relationships, and suggestions, which could be extracted and analyzed. The development of smarter chatbots: allow dialogues that go beyond single shot questions, reason on a mental model of the user and their beliefs. Legal text understanding analyze a law, a regulation, or a contract, and derive what is permitted and what is obligatory for which party. Current mainstream methods cannot model, extract, or let alone reason on such information. As we will argue in this vision paper, the problem is not a lack of reasoning formalisms. On the contrary, the dream of reasoning about events and reasoning about knowledge is as old as Artificial Intelligence (AI) itself. The problem is also not a lack of data. On the contrary, we are drowning in data, and modern knowledge harvesting methods are increasingly good at structuring it. Rather, the problem seems to be that these two worlds (that of complex reasoning formalisms and that of knowledge harvesting) have not been brought together. This vision paper argues that if we want to pave the way for smarter AI applications, we have to combine the idealism of the early reasoning formalisms with the pragmatism of modern large scale data extraction. 2 Existing Approaches 2.1 Knowledge Representation Let us look at common knowledge representations, and their associated reasoning formalisms. We will see that these were either not designed for dealing with large data, or that they do not actually cover our use cases. Frames [Min75, BS85, BW77] and scripts [SA79] were some of the first approaches to structure data. They stipulate that all objects of a certain class have a certain set of attributes (people have a birth date, a name, and optionally a spouse; cities have geographical coordinates and a mayor, etc. These attributes are inherited by subclasses. This inspired modern object oriented programming languages, where objects are instances of classes that have declared attributes. FrameNet [BFL98] is the contemporary successor of these models. Frames were explicitly designed to represent some of the knowledge that we aim at: events, precedence, and causation. However, frames were designed primarily to represent knowledge, not to reason on it. We cannot say that an event A happened before an event B, and that B happened before C, and that, therefore, A happened before C. Complex objects, nested relations, and object relational databases in general [AF89] take the idea of frames further: objects can be literals, atomic entities, or arrays or structures of other objects. These formalisms can be seen as a precursor to JSON, and newer work [BB+11] allows representing almost any type of semi structured data. While these approaches can represent what we want to model, they are, like frames, primarily a knowledge representation formalism, not a reasoning mechanism. We cannot say that Mary's Twitter followers believe everything she tweets, and then deduce that they believe that vaccines cause autism. Description Logics [BCM+03] were designed to give a decidable reasoning capability to frames. They can say, e.g. that every person has exactly two parents, and deduce that a KB where three people have the same child is inconsistent. At the same time, classical description logics cannot express knowledge about knowledge. RDF. The Semantic Web community takes inspiration from frames and description logics. However, it has since left frames behind, because it becomes tedious or impossible to specify possible and obligatory attributes of classes under the Open World Assumption. Hence, in the Semantic Web, any object can have any relation with any other object (and the class membership is inferred a posteriori by domain and range constraints, if necessary) Indeed, the Resource Description Framework (RDF) [MM04] models only binary relations between entities. Through event entities, RDF can also express relations of higher arity [RN06] By help of named graphs [CBHS05] or RDF* [Har17a, Har17b] RDF can make statements about other statements. These statements are always part of the KB they cannot be hypothetical [Har17a, Section 2.4] To model hypothetical statements, one can use RDF reification [MM04, Section 4.3] All of these tools are knowledge representation mechanisms, which do not come with appreciable reasoning capabilities. To reason on RDF data, we can use RDFS and OWL [W3C09] RDFS allows reasoning on classes, and OWL extends this to Turing complete reasoning. Di|erent flavors of OWL (sitting on di|erent description logics) reduce this expressivity to decidable subsets. However, these decidable subsets cannot express statements about statements mainly because this would invite undecidability. Constraint languages such as SHACL [KK17] and ShEx [SB+15] can say, e.g. that every person must have one (and at most one) birthdate, but they cannot express statements about statements, causation, or beliefs. Rule based KBs such as Cyc [LG89] and SUMO [NP01] have, likewise, freed themselves from frames, and use the Knowledge Interchange Format [Gen91] (KIF) a powerful knowledge representation language that can easily describe events, facts about facts, causation, or beliefs. The problem is that this formalism is so powerful that it is undecidable, as we have elaborated earlier [BS06] Context logics were initially proposed by John McCarthy [McC93] as a way to take into account di|erent meanings of a statement depending on the circumstances. The initial white paper has given rise to a number of concrete context logics [BM93, Buv96, Nos03, KGB11, GS94] However, these logics either remain limited to propositional logic [BM93, Nos03] (with no way to quantify over individuals or contexts) are undecidable [Buv96] or disallow contexts as relation objects [KGB11, GS94] (i.e. they cannot express "The publication of the article
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