Joint Reasoning for Multi-Faceted Commonsense Knowledge

1 . Introduction . Motivation and problem . Commonsense knowledge ( CSK ) is a potentially important asset towards building versatile AI applications , such as visual understanding for describing images ( e.g. , ( Agrawal et al. , 2017 ; Karpathy and Fei-Fei , 2017 ; Shuster et al. , 2019 ) ) or conversational agents like chatbots ( e.g. , ( Young et al. , 2018 ; Rajani et al. , 2019 ; Yang et al. , 2019 ) ) . In delineation from encyclopedic knowledge on entities like Trump , Sydney , or FC Liverpool , CSK refers to properties , traits and relations of everyday concepts , such as elephants , coffee mugs or school buses . For example , when seeing scenes of an elephant juggling a few coffee mugs with its trunk , or with school kids pushing an elephant into a bus , an AI agent with CSK should realize the absurdity of these scenes and should generate adequate captions for image description or utterances in a conversation . Encyclopedic knowledge bases ( KBs ) received much attention , with projects such as DBpedia , Wikidata , Yago or NELL and large knowledge graphs at Amazon , Baidu , Google , Microsoft etc . supporting entity-centric search and other services . In contrast , approaches to acquire CSK have been few and limited . Projects like ConceptNet ( Speer and Havasi , 2012 ) , WebChild ( Tandon et al. , 2014 ) , TupleKB ( Mishra et al. , 2017 ) and Quasimodo ( Romero et al. , 2019 ) have compiled millions of concept : property or subject-predicate-object ( SPO ) statements , but still suffer from sparsity or noise . ConceptNet has only a single nontaxonomic/non-lexical statement about hyenas , namely , hyenas : laugh a lot 1 , and WebChild 1. http : //conceptnet.io/c/en/hyena lists overly general and contradictory properties such as small , large , demonic and fair for hyenas2 . Another limitation of existing CSK collections is that they rank statements solely by confidence scores . There is no information about which properties are typical , which ones are possible but rare , and which ones are salient from a human perspective . For example , the statement that hyenas drink milk ( as all mammals when they are cubs ) is valid , but it is not typical . Hyenas eating meat is typical , but it is not salient in the sense that humans would spontaneously name this as a key trait of hyenas . In contrast , hyenas eating carcasses is remarkable as it sets hyenas apart from other African predators ( like lions or leopards ) , and many humans would list this as a salient property . Prior works on building Web-scale CSK collections largely missed out on this more refined perspective ( see Section 2 for more discussion ) . Approach and contribution . We present Dice ( Diverse Commonsense Knowledge ) , a reasoning-based method for advancing CSK collections to a more expressive stage of multifaceted knowledge . Dice is based on two novel ideas : • We introduce four facets of concept properties : ( i ) Plausibility indicates whether a statement makes sense at all ( like the established but overloaded notion of confidence scores ) . ( ii ) Typicality indicates whether a property holds for most instances of a concept ( e.g. , not only for cubs ) . ( iii ) Remarkability expresses that a property distinguishes the concept from closely related concepts ( like siblings in a taxonomy ) . ( iv ) salience reflects that a property is characteristic for the concept , in the sense that most humans would list it as a key trait of the concept . • We identify inter-related concepts by their neighborhoods in a concept hierarchy or via embeddings , and devise a set of weighted soft constraints that allows us to jointly reason over the four dimensions for sets of candidate statements . We cast this approach into an integer linear program ( ILP ) , and harness the theory of reduced cost ( Bertsimas and Tsitsiklis , 1997 ) for LP relaxations to compute rankings for each of the facets . As an example , consider the concepts lions , leopards , cheetahs and hyenas . The first three are coupled by being taxonomic siblings under their hypernym big cats , and the last one is highly related by word-level embeddings ( as all live in the African savannah ) . Our constraint system includes logical clauses such as Plausible ( s1 , p ) ∧ Related ( s1 , s2 ) ∧ ¬Plausible ( s2 , p ) ∧ . . . ⇒ Remarkable ( s1 , p ) where . . . refers to enumerating all siblings of s1 , or highly related concepts . The constraint itself is weighted by the degree of relatedness ; so it is a soft constraint that does allow exceptions . This way we can infer that remarkable ( and also salient ) statements include lions : live in prides , leopards : climb trees , cheetahs : run fast and hyenas : eat carcasses . Experiments with inputs from ConceptNet , TupleKB and Quasimodo show that Dice achieves high precision and good coverage for its multi-faceted output . The resulting datasets contain 1.6 million statements for 74K concepts , available at https : //tinyurl.com/y6hygoh8 . 2. https : //gate.d5.mpi-inf.mpg.de/webchild2/ ? x=hyena % 23n % 231 2 . Related Work . Manually compiled CSK . The Cyc project ( Lenat , 1995 ) project pursued the goal of compiling a comprehensive machine-readable collection of human knowledge into logical assertions . The result comprised both encyclopedic and commonsense knowledge . The WordNet project ( Miller , 1995 ) organized word senses into lexical relations like synonymy , antonymy , and hypernymy/hyponymy ( i.e. , subsumption ) . WordNet can serve as a taxonomic backbone for CSK , but there are also more recent alternatives such as WebIsALOD ( Hertling and Paulheim , 2017 ) derived from Web contents . ConceptNet extended the Cyc and WordNet approaches by collecting CSK triples from crowdworkers , for about 20 broad predicates ( Speer and Havasi , 2012 ) . It is the state of the art for large-scale CSK . Atomic ( Sap et al. , 2018 ) is a crowdsourcing project compiling knowledge on human activities ; it is more refined than ConceptNet but even sparser . The most popular knowledge base today , Wikidata ( Vrandečić and Krötzsch , 2014 ) , contains both encyclopedic knowledge about notable entities and some CSK cast into RDF triples . However , the focus is on individual entities , and CSK is very sparse . Web-extracted CSK . The reliance on human input limits the scale and scope of the above mentioned projects . Automatic extraction from Web contents can potentially achieve much higher coverage . The WebChild project ( Tandon et al. , 2014 , 2017 ) extracted ca . 10 million SPO statements from books and image tags . Its rationale was to capture many plausible properties that hold for some instances of a concept , with a long tail of noisy , puzzling or invalid statements . Its approach was to identify typical statements by thresholding on confidence scores . However , these scores reflect different aspects that can not be disentangled by mere thresholding ; most critically , they are affected by the reporting bias of Web sources ( Gordon and Durme , 2013 ) . Therefore , a high-confidence statement could still be atypical ( e.g. , snakes attack humans ) . TupleKB ( Mishra et al. , 2017 ) , part of the Mosaic project , contains ca . 280k statements on 8th-grade elementary science towards tackling the school exam challenge ( Schoenick et al. , 2017 ) . It builds on similar sources as WebChild , but prioritizes precision over recall by supervised cleaning steps . On the downside , its coverage is much lower . Quasimodo ( Romero et al. , 2019 ) is a recent CSK collection , built by extraction from QA forums and web query logs , with ca . 4.6 million statements . It combines multiple cues into a regression-based corroboration model for ranking and aims to identify salient statements , but the model is limited by its one-dimensional scoring . Common to these projects is that their quantitative scoring of CSK statements is based on extraction confidence only . Simply thresholding on these scores can not bring out the refined facets of typicality and salience . Modalities of knowledge . Classical works on epistemic and modal logics ( see , e.g. , ( Fagin et al. , 1995 ; Gabbay et al. , 2003 ) ) , already proposed to add modalities for statements that hold always or sometimes . Early work on knowledge acquisition from a computational linguistics perspective ( Schubert , 2002 ; Schubert and Tong , 2003 ) refined these considerations by extracting general versus possible propositions from text corpora . ( Gordon and Schubert , 2010 ) formalized this into logical modalities like all-or-most , some etc . ( Zhang et al. , 2017 ) developed a regression model for acquiring graded modes of CSK statements , covering the spectrum of very likely , likely , plausible , technically possible and impossible . The model was applied to sentences for textual entailment and natural language inference ( cf . ( Bowman et al. , 2015 ) ) . The resulting dataset JOCI is organized in the form of 40K sentence pairs , rather than grouped by concepts and systematically covering their properties . All these efforts were relatively small-scale by modern standards . Our approach follows up on this inspiring line of works but extends their scope and scale , by including humanperceived salience , by leveraging concept taxonomies to enhance coverage and accuracy , and by devising a methodology that can process millions of CSK statements . 3 . Multi-Faceted CSK Model . We consider simple CSK statements of the form ( s , p ) , where s is a concept and p is a property of this concept . We refer to s as the subject of the statement . Often , but not necessarily , s is a single noun , such as hyenas , but multi-word phrases do occur , such as African elephants . p values include many verb or noun phrases , such as laugh a lot or ( are ) endangered species . Unlike prior works , we do not adopt the SPO triple model , and instead combine P and O into property , for three reasons : ( i ) The split between P and O is often arbitrary . For lions : live in prides , we could either consider live or live in as P and the rest as O , or we view live in prides as P without O . ( ii ) Unlike encyclopedic KBs , CSK is so diverse that it is virtually impossible to agree on canonicalized predicate names beyond a small set of basic properties . ConceptNet and WebChild cover a fixed set of ca . 20 pre-specified predicates . When discounting taxonomic and lexical relations ( e.g. , type-of , synonymy ) , this boils down to a few broad predicates : used for , capable of , location , part of and the generic has property . ( iii ) In contrast , TupleKB and Quasimodo have more than 1000 distinct predicates . The simpler model of concept : property pairs makes all these CSK collections better comparable by unifying their highly varying granularities . 3.1 CSK Dimensions . We organize concept-property pairs along four dimensions : plausibility ( Tandon et al. , 2014 ; Mishra et al. , 2017 ) , typicality ( Speer and Havasi , 2012 ; Zhang et al. , 2017 ) , salience ( Romero et al. , 2019 ) , and remarkability ( information theory ) . These are meta-properties : each ( s , p ) pair can have any of these labels and multiple labels are possible . The first three modalities together cover the foci of most prior works on large CSK collections , whereas each of these works emphasizes only one or two of the modalities . Remarkability is a new dimension , motivated by our novel way of leveraging class taxonomies . For each statement and dimension label , we compute a score and can thus rank statements for a concept four times . • Plausibility : Is the property valid at least for some instances of the concept , for at least some spatial , temporal or socio-cultural contexts ? For example , lions drink milk at some time in their lives , and some lions attack humans . • Typicality : Does the property hold for most ( or ideally all ) instances of the concept , for most contexts ? For example , most lions eat meat , regardless of whether they live in Africa or in a zoo . • Remarkability : What are specific properties of a concept that sets the concept apart from highly related concepts , like taxonomic generalizations ( hypernyms in a concept hierarchy ) ? For example , lions live in prides but not other big cats do this , and hyenas eat carcasses but hardly any other African predator does this . • Salience : When humans are asked about a concept , such as lions , bicycles or rap songs , would a property be listed among the concept ’ s most notable traits , by most people ? For example , lions hunt in packs , bicycles have two wheels , rap songs have interesting lyrics and beat ( but no real melody ) . Examples . Refining CSK by the four dimensions is useful for various application areas , including language understanding for chatbots , as illustrated by the following examples : - Plausibility helps to detect absurd statements and react adequately . - Typicality helps a chatbot to infer missing context . For example , when the human talks about “ a documentary which showed the feeding frenzy of a pack of hyenas ” , the chatbot could ask “ what kind of carcass did they feed on ? ” - Remarkability can be a signal for the chatbot to infer which concept the human is talking about . For example , a user utterance “ In the zoo , the kids where fascinated by a spotted dog that was laughing at them ” could lead to chatbot to detect that this is about hyenas . - Salience enables the chatbot to focus on key properties . For example , when talking about lions in the zoo , the bot could ask “ Did you hear the lion roar ? ” , or “ How many lionesses were in the king ’ s harem ? ”

Summary: This paper attempts to classify different scoring mechanisms introduced in widely used KB resources into four facets:  plausibility, typicality, remarkability, and salience. To model these scores, the authors introduce logical dependencies between these scores and use an ILP-based reasoning model (enhanced by vector-space similarity models) to learn these scores. The additional nuance of these measures is an advance in commonsense knowledge and the underlying model provides a compact and convincing set of constraints behind these scores, but some of the methodological details are missing or could be improved to allow a stronger impact.