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WebChild 2.0 : Fine-Grained Commonsense Knowledge Distillation



Despite important progress in the area of intelligent systems, most such systems still lack commonsense knowledge that appears crucial for enabling smarter, more human-like decisions. In this paper, we present a system based on a series of algorithms to distill fine-grained disambiguated commonsense knowledge from massive amounts of text. Our WebChild 2.0 knowledge base is one of the largest commonsense knowledge bases available, describing over 2 million disambiguated concepts and activities, connected by over 18 million assertions.

1 Introduction

With the continued advances in natural language processing and artificial intelligence, the general public is increasingly coming to expect that systems exhibit what may be considered intelligent behavior. While machine learning allows us to learn models exploiting increasingly subtle patterns in data, our systems still lack more abstract, generic forms of commonsense knowledge. Examples of such knowledge include the fact that fire causes heat, the property of ice being cold, as well as relationships such as that a bicycle is generally slower than a car. Previous work in this area has mostly relied on handcrafted or crowdsourced data, consisting of ambiguous assertions, and lacking multimodal data. The seminal work on Con-ceptNet (Havasi et al., 2007) , for instance, relied on crowdsourcing to obtain an important collection of commonsense data. However, it conflates different senses of ambiguous words (e.g., "hot" in the sense of temperature vs. "hot" in the sense of being trendy). It also lacks fine-grained details such as specific kinds of properties, comparisons between objects, and detailed knowledge of activities. We attempt to fill these significant gaps. This paper presents automated methods targeting the acquisition of large-scale, semantically organized commonsense knowledge. This goal poses challenges because commonsense knowledge is: (i) implicit and sparse, as humans tend not to explicitly express the obvious, (ii) multimodal, as it is spread across textual and visual sources, (iii) affected by reporting bias, as uncommon facts are reported disproportionally, and (iv) context dependent, which implies, among other things, that at an abstract level it is perhaps best described as merely holding with a certain confidence. Prior state-of-the-art methods to acquire commonsense are either not automated or based on shallow representations. Thus, they cannot automatically produce large-scale, semantically organized commonsense knowledge.

To achieve this challenging goal, we divide the problem space into three research directions.

• Properties of objects: acquisition of properties like hasSize, hasShape, etc. We develop a transductive approach to compile semantically organized properties.

• Relationships between objects: acquisition of relations like largerThan, partOf, memberOf, etc. We develop a linearprogramming based method to compile comparative relations, and, we further develop a method based on statistical and logical inference to compile part-whole relations.

• Their interactions: acquisition of knowledge about activities such as drive a car, park a car, etc., with expressive frame based representation including temporal and spatial attributes. For this, our Knowlywood approach is based on semantic parsing and probabilistic graphical models to compile activity knowledge.

Together, these methods result in the construction of a large, clean and semantically organized Commonsense Knowledge Base that we call the WebChild 2.0 knowledge base.

2 Web-Scale Fact Extraction

Our framework consists of two parts. First, we rely on massive amounts of text to extract substantial amounts of raw extractions. Subsequently, in Section 3, we will present a series of algorithms to distill fine-grained disambiguated knowledge from raw extractions of this sort. For details on the input dataset leading to raw extractions, refer to the original publications, cited through each subsection in Section 3.

Pattern-Based Information Extraction. For knowledge acquisition, it is well-known that one can attempt to induce patterns based on matches of seed facts, and then use pattern matches to mine new knowledge. Unfortunately, this bootstrapping approach suffers from significant noise a) when using a very large number of seeds, and b) when applied to large Web-scale data (Tandon et al., 2011 ), which appears necessary to mine adequate amounts of training data. Specifically, for many of our extractions, Google's large Web N-Gram dataset, which provides Web-scale data. Additionally, many of our experiments use a large subset of ConceptNet as seed facts, resulting in a seed set that is many orders of magnitude larger than the typical set of perhaps 10 seeds used in most other bootstrapped information extraction systems. This problem is exacerbated by the fact that we are aiming at commonsense knowledge, which is not typically expressed using explicit relational phrases. We thus devise a custom bootstrapping approach designed to minimize noise when applied to Webscale data.

We assume that we have a set of relations R, a set of seed facts S(r) for a given r ∈ R, as well as a domain(r) and range(r), specified manually to provide the domain and range of a given r as its type signature. For pattern induction, we look for co-occurrences of words in the seed facts within the n-grams data (for n = 3,4,5). Any match is converted into a pattern based on the words between the two occurrences, e.g. "that apple is red" would become " is ".

Pattern Scoring. The acquired patterns are still rather noisy and moreover very numerous, due to our large set of seeds. To score the reliability of patterns, we invoke a ranking function that rewards patterns with high distinct seed support but also discounts patterns that occur across multiple dissimilar relations (Tandon et al., 2011) . The intuition is that a good pattern should match many of the seed facts (reward function), but must be discounted at matching too many relations (discount function). As, e.g., the pattern " and " is unreliable because it matches seeds from too many relations.

This discount must be softened when the pattern matches related relations. To allow for this, we first define a relatedness score between relations. We can either provide these scores manually, or consider Jaccard overlap statistics computed directly from the seed assertion data. Let p be a candidate pattern and r ∈ R be the relation under consideration. We define |S(r, p)| as the number of distinct seeds s ∈ S(r) under the relation r that p matches. We then define the discount score of the pattern p for relation r as:

φ(r, p) = r ∈R,r =r |S(r, p)| |S(r)| −(1−sim(r, r )) |S(r , p)| |S(r )|

where sim(r, r ) is the similarity between relations r and r . The final score is a combination of the discount score φ(r, p) and the judiciously calculated reward score. At the end, we choose the topk ranked patterns as the relevant patterns for the extraction phase.

Assertion Extraction. We apply the chosen patterns to find new occurrences in our (Google Web N-grams) data. For instance, " is " could match "the sun is bright", yielding ("sun", "bright") as an assertion for the hasProperty relation. To filter out noise from these candidate assertions, we check if the extracted words match the required domain and range specification for the relation, using WordNet's hypernym taxonomy. Finally, we rank the candidate assertions analogously to the candidate patterns, but treating the patterns as seeds.

3 Distilling Fine-Grained Knowledge

The techniques described above provide a sizable set of extractions that serve as the the basis for WebChild 2.0. Next, we consider a family of algorithms that take raw extractions of this form and exploit them to distill detailed fine-grained knowledge.

3.1 Fine-Grained Properties

The first algorithm we consider (Tandon et al., 2014a) aims at compiling a large and clean set of fine-grained commonsense properties, connecting noun senses with adjective senses by a variety of relations. In contrast to prior work that only dealt with a generic hasProperty relation, we use 19 different (sub-)relations such as hasShape, hasSize, hasTaste, hasAbility, evokesEmotion, etc. This list is systematically derived from WordNet based on its attribute information.

Moreover, our goal is to distinguish the specific senses (e.g. green 2

a , or green#a#2 refers to the second sense of WordNet adjective green) of the arguments of these relations as well. For example, for plant hasProperty green , there are two competing interpretations with very different meanings: industrial-plant hasQuality greenenvironmental vs. botanical-plant hasColor green-color .

We start out by constructing the range and domain of the property relations with a small set of seed examples. Such seeds would normally be gathered manually, but in our work, we observed that an ensemble of two very different, automated, and noisy sources can also produce highquality seeds. We construct a graph where the nodes are words and word senses and the edge weights are computed based on taxonomic and distributional similarities (these edge weights are depicted in Figure 1) . We then use a judiciously designed form of label propagation (Talukdar and Crammer, 2009) to learn the domain set, the range set, and the extension of such relations, at large scale and in terms of specific word senses. An example of a range graph is given in Figure 1 . The highlighted seed nodes mark specific senses of words as pertaining to a specific relation (e.g., hasTemperature). Via label propagation, we can infer such information for additional nodes, such as the "pleasantly cold" sense of "crisp" (for the range of the relation), but not other irrelevant senses of "crisp". The same label propagation technique can then also be applied to infer entire relation tuples.

Figure 1: Inferring range of hasTemperature. Violet nodes indicate noisy candidate range. Starting with seeds (yellow single outlined), the algorithm enforces that similar nodes have similar labels, and infers range (yellow double outlined). For details on edge weights, see (Tandon et al., 2014a).

Our graph-based semi-supervised approach is generic enough to extract any type of fine-grained sub-property or attribute, for which we need only a few seeds to begin.

3.2 Comparative Knowledge

The second algorithm (Tandon et al., 2014b) aims at extracting and organizing large-scale comparative commonsense knowledge. Prior to our work, semantically organized comparative commonsense knowledge had not been studied or compiled before.

We first gather a set of new raw extractions using patterns targeting comparisons. These include the word "to be" followed by comparative forms of adjectives (e.g. "smaller than", "more educated than"). While we again follow a Web-scale extraction strategy as in Section 2, note that these patterns are generic in the sense that they cover all words identified as adjectives. Thus, this constitutes a form of open information extraction for comparative knowledge.

The next step is to refine and extend these extractions. The constituents of a comparative assertion are strongly related (e.g., car, fast, and bike in car faster than bike ); our method builds upon this observation to jointly disambiguate and connect these assertions, while inferring additional ones. Disambiguation is important because relationships such as "richer than" could refer to financial assets or to calories. The joint algorithm is also necessary to exploit dependencies and transitivity between extractions (e.g., "is smaller than", "is larger than", "is not bigger than"), with the goal of validating input extractions as well as inferring new relationships. This is achieved via a custom Integer Linear Program with constraints accounting for the coherence and logical consistency of the interpretation

3.3 Detailed Part-Whole Relationships

The third algorithm focuses on extracting and organizing largescale part-whole commonsense knowledge. In contrast to prior work, our algorithm distinguishes physicalPartOf (e.g., wheel physicalPartOf bike ), memberOf (e.g., cyclist memberOf team ), and substanceOf (e.g., rubber substanceOf wheel ), and the arguments are disambiguated. We also estimate cardinality, describing the typical number of parts in a whole, and visibility information, i.e., whether the part can be perceived visually.

We again rely on raw assertions extracted from text, but distill these via statistical scoring combined with logical inference to account for tran- Figure 1 : Inferring range of hasTemperature. Violet nodes indicate noisy candidate range. Starting with seeds (yellow single outlined), the algorithm enforces that similar nodes have similar labels, and infers range (yellow double outlined). For details on edge weights, see (Tandon et al., 2014a) . sitivity and inheritance of assertions (via Word-Net's hypernym hierarchy). To estimate visibility, we verify the assertions in images (we call this quasi-visual verification). Quasi-visual verification leverages the best of text-only verification (which is inaccurate due to reporting bias), and visual-only verification (which is inaccurate due to the object detectors' inaccuracies).

Our method generalizes to any relation with finer-grained sub-relations.

This specific multimodal approach generalizes to any commonsense relation that has multiple subrelations and is verifiable in images, e.g., hasLocation relation (with sub-relations hasLocationAbove/Below, etc.).

3.4 Activity Knowledge

The fourth algorithm (Tandon et al., 2015 ) is a novel method for a new task to extract and organize semantic frames of human activities, together with their visual content. The Knowlywood pipeline, illustrated in Figure 2 , distills such knowledge from raw text, rather than starting with the extractions from Section 2. In par-ticular, we acquire knowledge about human activities from narrative text, focusing in particular on movie scripts, which are structured in terms of scenes, and provide descriptions of scene settings/locations, speakers, etc. Moreover, when scripts come with representative images or time points in the movie, it is possible to align a scene description with the actual visual contents of the movie. The main difficulty, however, is that all this rich contents in movie scripts is merely in textual form -still far from structured KB representation. Our method considers joint semantic role labeling and word sense disambiguation for parsing these scripts to generate candidate items for the activity frames. In particular, we rely on a semantic parsing like approach that fills the slot values of such frames (activity type, location, participants, etc.), while also jointly disambiguating the verb and the slot fillers with respect to WordNet. This is achieved by computing strong priors that are fed into an integer linear program. In the example in Table 1 , we see how this process also jointly relies on information from VerbNet. For the WordNet verb sense number 2: shoot#2 (killing), VerbNet provides a role restriction Agent.animate V Patient.animate PP Instrument.solid, where animate refers to living beings, as opposed to inanimate objects. This allows us to infer that this shoot#2 sense is not compatible with the argument "the video", which is not animate. This way, we can disqualify the incorrect interpretation of "shoot".

Figure 2: Knowlywood pipeline to extract activity frames.
Table 1: Semantic Parse Example

We then perform inference using probabilistic graphical models that can encode joint dependencies among different candidate activity frames. Unlike the previous contribution, this method goes beyond disambiguation of the arguments of an assertion; and, additionally assign roles to these arguments. A final taxonomy construction step groups together similar activity frames and forms a hierarchy of activities. For movie scripts with aligned movie data, we associate the correspond video key frames with our activities. Figure 3 provides an example of the resulting activity frames.

Figure 3: Example of an activity frame

4 Results And Demonstration

Together, these methods have been used to create the WebChild 2.0 KB, which is one of the largest commonsense knowledge bases available, describing over 2 million disambiguated concepts and activities, connected by over 18 million assertions.

Among this data, we highlight the Knowlywood pipeline that produced 964,758 unique activity instances, grouped into 505,788 activity synsets. In addition to the edges mentioned above, we also obtain 581,438 location, 71,346 time, and 5,196,156 participant attribute entries over all activities. This is much larger than other commonsense KBs such as ConceptNet, refer Table 2. The WebChild 2.0 KB is bigger and richer than any other automatically constructed commonsense KB. It can also be viewed as an extended Word-Net (comprising not just words, but also activi- Figure 4 : WebChild 2.0 browser results for mountain. It presents semantic knowledge for concepts, comparisons, and activities. For more examples, visit gate.d5.mpi-inf.mpg.de/webchild ties and other concepts expressed via multi-word expressions), with an orders of magnitude denser relationship graph (connecting the concepts with novel relations such as comparatives), and with additional multimodal content.

Figure 4: WebChild 2.0 browser results for mountain. It presents semantic knowledge for concepts, comparisons, and activities. For more examples, visit gate.d5.mpi-inf.mpg.de/webchild
Table 2: Knowlywood Coverage and Precision: manual assessments over a sample of 100 activity frames

The WebChild 2.0 browser provides a user interface to semantically browse the current commonsense database, combining the knowledge from all of the above algorithms, refer to Figure 4 .

5 Conclusion

From a resource perspective, people looking for commonsense knowledge bases had few options available before our construction of the WebChild 2.0 knowledge base. The available alternatives do not offer the same level of size, richness and semantic rigor over multiple modalities. In ongoing work, we are developing improved algorithms to prune noisy extractions, and computing the weights for the inference steps to distill cleaner knowledge.

WebChild 2.0 has already been effective in providing background knowledge to applications such as visual question answering (Wang et al., 2016) and neural relation prediction (Chen et al., 2016) . The WebChild 2.0 data is freely downloadable at http://www.mpi-inf.mpg. de/yago-naga/webchild/, and browsable at https://gate.d5.mpi-inf.mpg.de/ webchild/.