BertNet: Harvesting Knowledge Graphs from Pretrained Language Models
Authors: Shibo Hao
Bowen Tan
Kaiwen Tang
Bin Ni
Hengzhe Zhang
Eric Xing
Zhiting Hu
Venue: Submitted to ICLR 2023 (2023)
Keywords: knowledge graph
pretrained language models

Abstract: Symbolic knowledge graphs (KGs) have been constructed either by expensive human crowdsourcing or with complex text mining pipelines. The emerging large pretrained language models (LMs), such as BERT, have shown to implicitly encode massive knowledge which can be queried with properly designed prompts. However, compared to the explicit KGs, the implict knowledge in the black-box LMs
is often difficult to access or edit and lacks explainability. In this work, we aim at harvesting symbolic KGs from the LMs, and propose a new framework for automatic KG construction empowered by the neural LMs’ flexibility and scalability. Compared to prior works that often rely on large human annotated data or existing massive KGs, our approach requires only the minimal definition of relations as inputs, and hence is suitable for extracting knowledge of rich new relations that are instantly assigned and not available before. The framework automatically generates diverse prompts, and performs efficient knowledge search within a given LM for consistent outputs. The knowledge harvested with our approach shows competitive quality, diversity, and novelty. As a result, we derive from diverse LMs a family of new KGs (e.g., BERTNET and ROBERTANET) that contain a richer set of relations, including some complex ones (e.g., "A is capable of but not good at B") that cannot be extracted with previous methods. Besides, the resulting KGs also serve as a vehicle to interpret the respective source LMs, leading to new insights into the varying knowledge capability of different LMs.


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BERTNET: HARVESTING KNOWLEDGE GRAPHS FROM PRETRAINED LANGUAGE MODELS
Anonymous authors Paper under double-blind review
ABSTRACT
Symbolic knowledge graphs (KGs) have been constructed either by expensive human crowdsourcing or with complex text mining pipelines. The emerging large pretrained language models (LMs), such as BERT, have shown to implicitly encode massive knowledge which can be queried with properly designed prompts. How- ever, compared to the explicit KGs, the implict knowledge in the black-box LMs is often difficult to access or edit and lacks explainability. In this work, we aim at harvesting symbolic KGs from the LMs, and propose a new framework for auto- matic KG construction empowered by the neural LMs' flexibility and scalability. Compared to prior works that often rely on large human annotated data or existing massive KGs, approach requires only the minimal definition of relations inputs, and hence is suitable for extracting knowledge of rich new relations that are instantly assigned and not available before. The framework automatically generates diverse prompts, and performs efficient knowledge search within a given LM for consistent outputs. The knowledge harvested with our approach shows competitive quality, diversity, and novelty. As a result, we derive from diverse LMs a family of new KGs (e.g., BERTNET and RoB ERBANTE) that contain richer set of relations, including some complex ones (e.g., "A is capable of but not good at B") that cannot be extracted with previous methods. Besides, the resulting KGs also serve vehicle to interpret the respective source LMs, leading to new insights into the varying knowledge capability of different LMs.
INTRODUCTION
Symbolic knowledge graphs (KGs) encode rich knowledge about entities and their relationships, and have been one of the major means for organizing commonsense or Noomain-specini information
to empower various applications, including search engines (Xiong et al.; 2017] Google 2012). recommendation systems (Wang et al. 2219a; 2018 2019b), chatbots (Moon et al. 2019 Liu et al.)
2019b), healthcare (Li et al. 2019: Mohamed al. 2020 Lin et al. 2020), etc. The common practice for constructing KG crowdsourcing (such ConceptiNe et (Speer al. (017). WordNet (Fellbaum 2000), and ATOMIC (Sap et al. 2019)). which is accurate but often has limited coverage due to the extreme cost of manual annotation (e.g., ConceptNet covers only 34 types of commonsense relations). Prior work has also built text mining pipelines to automatically extract knowledge from unstructured text, including domain-specifie knowledge (Wang et al. 2022b) and commonsense knowledge (Zhang ct al. 22020 Romero et al., 2019 Nguyen et al.) [022] Those systems, however, often involve complex set of components (e.g., entity recognition, coreference resolution, relation extraction, etc.), and applicable only to subset of all the knowledge, which is explicitly stated in the text.
On the other hand. the emerging large language models (LMs) pretrained on massive text corpora, such as BERT (Devlin et al., 2019). ROBERTA (Liu et al. 2019a). and GPT-3 (Brown et al.) 2020). have been shown to encode a large amount of knowledge implicitly in their parameters. This has inspired the interest in using the LMs as knowledge bases. For example, recent work has focused on manually or automatically crafted prompts (e.g., "Obama was born in
") to query the
LMs for answers (e.g.. "Hawaii" i ") (Petroni al. 2019: Jiang et al. 2020 Shin et al. 2020 Zhong
et al. 2021). Such probing also serves as a way to interpret the black-box box LMs (Swamy et al. 2021 }.
and inspires further fine-tuning improve knowledge quality (3Newman et al. 2021 Fichtel et al.
202T). However, the black-bol box LMs, where knowledge is only implicitly encoded, fall short of the
many nice properties of explicit KGs (AlKhamissi et al. 2022). such the easiness of browsing

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Method
Knowledge source
Outcome
Arbitrary relation
Text mining
text corpus
KG
X
Factual probing
LMs
tail entity
COMET
LMs Existing Knowledge GPT-3 (+ existing KGs)
entity
Symbolic KD
KG
BertNet (Ours)
LMs
KG
Table 1: Categorization of works on automated knowledge graph construction. Compared with others, our framework is more flexible as relies LMs knowledge source, generates full KG, applies to arbitrary relations. The details presented Section
the knowledge or even making updates (Zhu et al. 2020 Cao et al. 2021) and the explainability for trustworthy use by domain experts. Can we automatically harvest KGs from the LMs, and hence combine the best of both worlds, namely the flexibility and scalability from the neural LMs, and the access, editability, and explainability in the symbolic form?
This paper makes step towards this end- given pretrained LM (e.g., BERT, ROBERTA), automatic framework extracts a KG from it in an efficient and scalable way this results in a family of new KGs (referred to as, e.g., BERTNET, ROBERTANET) that permit a new extendable set of knowledge beyond the existing hand-annotated KGs such as ConceptNet. As Table [ suggests, our framework is more flexible compared with previous works construct knowledge graphs automatically.
The new goal of extracting KGs purely from any LMs poses unique challenges. First, is difficult to extract knowledge reliably from LMs, as the LMs have shown to generate inconsistent outputs given prompts of slightly different wording (e.g.. " originall.
aired in " vs premiered on ") (Elazar et al. [022 Newman et al. 2021). Automatically learning Eisner (021) typically requires 2021
the optimal prompts (Lester al., Z et al.
many existing entity pairs as training data, which is often not available especially for new relations that are instantly assigned. While West et al. (202T) extracted commonsense knowledge of high quality from GPT-3, this method doesn't i apply other LMs, since it relies on the extreme few-shot learning ability and the large capacity of GPT-3 model. To this end, we apply an unsupervised method that automatically paraphrases an initial prompt and create diverse set of alternative prompts with varying confidence weights. We then search for entity pairs that consistently satisfy the diverse prompts. The second challenge lies in the search phase due to the large space of entity (one or multiple tokens) tuples. We devise an efficient search-and-rescoring strategy that strikes the balance between knowledge accuracy and coverage.
The minimal dependence on other sources besides the powerful LM itself allows maximal flexibility of our framework to extract novel knowledge, such as those about complex relations like "A capable of, but not good at, B" that expresses sophisticated meaning and "A can do B at c" that involves multiple entities. Besides, the resulting KGs can readily serve as symbolic interpretation of the respective black-box LMs, for users to browse and understand their knowledge storage and capability.
We apply the framework to harvest KGs from wide range of popular LMs, including RoBERTA, BERT, and DERT..BERT, of varying model sizes, respectively. The experiments show our approach can harvest large-scale KGs of diverse concepts, and performs well on user-defined complex relations.
Compared with other KGs trained with existing knowledge bases or human annotations, the outcome KGs of our framework, with solely a LM the knowledge source, shows competitive quality, diversity and novelty. The further analysis illustrates the better balance of knowledge accuracy and coverage than baselines. Comparison between the resulting KGs from different LMs offers insights into their knowledge capacities due to different factors, such as model sizes, pretraining strategies, and distillation.
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2 RELATED WORK
Knowledge graph construction Popular knowledge bases or KGs are usually constructed with heavy human labor. For example, WordNet (Fellbaum) 2000) is lexical database that links words into semantic relations; ConceptNet (Speer et al. 201 7) is a large commonsense knowledge graph in the conventional KG format that consists of (head entity, relation, tail entity) triples; ATOMIC (Sap et al.) 2019) is social commonsense KG by crowdsourcing consisting of if-then statements. Automatic Knowledge Graph Construction has been focused by academics. We summarize different categories of works in Table | Text mining based works aim to extract knowledge from text. A typical information extraction system (Angeli et al., 2015) decomposes the task to a set of sub-tasks, such as co-reference resolution, named entity recogmition, and relationship extraction. There are also works on commonsense knowledge extraction, like WebChild (Tandon et al. 2014), TransOMCS (Zhang et al., 2020). DISCOS (Fang et al., (02Tb), Quasimod o(Romero et al., (019), ASCENT (guuyen et al. 2021). These extraction pipelines are based on linguistic pattern, and involve complex engineering such as corpus selection, term aggregation, filtering and so on. Recently there are attempts to make use of language models. Wang et al. (202aa) finetuned LMs to predict missed links in KGs. COMET (Bosselut et al. 2019) is a finetuned generative LM trained to generate tail entities given head entities and relations. Symbolic knowledge distillation (KD) (West et al. 202I) distil the knowledge in GPT-3
generative L.M. As a intermediate product, they got ATOMIC by prompting GPT-3 (Brown et al. 2020) with examples. However, this method only applies to GPT-3 for the need of strong few-shot learning ability. Factual probing (Petroni al. 2019 AIKhamissi itt al. 2022 Razniewski et al. 202T) measures the amount of knowledge in LMs by the accuracy when filling blank in a prompt, which is human- human-written, from text mining, or learned with large amount of existing knowledge. We summarize their difference to our work in the caption of Table [] To the best of our knowledge, our framework is the first to construct knowledge graph via extracting knowledge purely from LMs (with minimal definition of relations as input).
LMs as knowledge bases It is easy to query knowledge in knowledge bases, while the implicit knowledge in LMs are difficult to access. The retrieval of knowledge in LMs often requires finetuning
(Da et al. 2021; Wallat et al.| 2020) or prompt tuning (Qin & Eisner 2021 Jiang et al. 2020 Adolphs al. 2021; Davison et al. 22199] Petroni al. 2019) with existing knowledge, limiting their methods to query new relations without existing tuples. Together with the success of pretrained
LMs, recent work has developed various ways to understand their internal mechanisms, such as
analyzing the internal states of LMs, and extracting structured linguistic patterns (Tenney et al. 2019 Hewitt & Manning. 2019). Factual probing aims to quantify the factual knowledge in pretrained language models, which is usually implemented by prompting methods and leveraging the masked language model pretraining task. Specifically, the amount of factual knowledge is estimated by set of human-written cloze-style prompts, e.g., "Dante was born in of the model prediction on the blank represents a lower bound of the amount of knowledge in the model. LAMA (Petroni et al. 2019) collects a set of human-written prompts to detect the amount of factual information that a masked language model encodes. LPAQQ (Jiang et al., (020) proposes to use text mining and paraphrasing to find and select prompts to optimize the prediction of single or a few correct tail entitics, instead of extensively predicting all the valid entity pairs like in our framework. AutoPrompt (Shin et al., 2020) and OPTIPrompt (Zhong et al. 2021 search prompts automatically. Though making prompts unreadable, they achieve higher accuracy on the knowledge probing tasks. Instead of measuring knowledge inside an LM as accuracy number, our framework explicitly harvests a KG from the LM. Consistency is significant challenge in knowledge probing and extraction, which refers model that should not have predictions contradicting each other. Basically, models should behave invariantly under inputs with different surface forms but the same meaning, e.g., paraphrased sentences or prompts. Several benchmarks are proposed to study consistency in LMs (Elazar et al. 2021 Hase et al. (021). Elazar et al.) (202T) analyzes the consistency of pretrained language models with respect to the factual knowledge. They show that the consistency of all LMs is poor in their experiment. In our framework, the extracted entity pairs for each relation would consistently satisfy a diverse set of prompts. Unlike traditional KGs, it is also non-trivial to edit knowledge in LMs, though there is some preliminary research editing facts in LMs (Zhu et al.) 2020 Cao et al., 2021) 1). via forcing the model to change predictions on some data points while keeping the same for other data points. There is also another line of works that utilize LMs to score entity pairs (Feldman et al. 2019; Bouraoui et al., 22200] Fang et al., 202 a) However, these works only use LMs to filter candidate knowledge tuples that are collected either
" The accuracy
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BertNet!
Input
povential risk
(eating candy, tooth decay) (playing game, fail exam)
The potential risk of is
0.43
0.85
(smoking, cancari
fiihing. fish bite)
0.11
(speeding, cresh)
Seed entity pairs
Initial prompt
Ranked Entity Pairs
scorsciingg
Initializa
Hair polentially cause infection.
The potential risk eating candy tooth decay.
Sample
P1: The potendal risk of A B.
(hair, infection) (alcohol, suicide! (food, obesity!
The potential risk elcohol suicide.
Paraphrase
Candy can potentially cause tooth decay.
P2: A may lead to B.
Food may leed to obesity
Candidate Entity Pairs
F3: If you A you may be at risk for B.
Extract
Add
...
Prompt
Search w/ LM
can potentially cause B.
weighting
Prompt Set
Weighted Prompt
Prompt Creation
Entity Pair Search
Figure I: An overview the knowledge harvesting framework. Given minimal definition of the relation as input (an initial prompt and a few shot of seed entity pairs), the approach first automatically creates set of prompts expressing the relation in a diverse ways ($3.1). The prompts are weighted with confidence scores. We then use the LM to search large collection of candidate entity pairs, followed by re-scoring/ranking that yiclds the top entity pairs as the output knowledge ($3.2).
from the Internet or existing KGs, but they don't direclty extract knowledge inside the LMs as our framework.
3 HARVESTING K GS FROM LMS
This section presents the proposed framework for extracting a relational KG from a given pretrained LM, where the LM can be arbitrary fill-in-the-blank models such as BERT (Devlin et al. 2019).
ROBERTA (Liu et al. 2019a), BART (Lewis et al. 2020), or GPT-3 (with appropriate instructions)
(Brown al. 2020). The KG consists of a set of knowledge tuples in the form <head entity
(h), relatiiiooo (r), tail entity (t) >. Specifically, we automatically harvest from the LM all appropriate entity pairs (h, for any given relation r. This presents substantially more challenging problem than the popular LMM probing tasks (Petroni et al. 2019 Jiang et al. [020 Shin ct al. 2020 Zhong et al. 2021) which predict particular tail entity given both the head entity and relation. In contrast, given only the relation, we need to search for hundreds thousands of head/tail entity pairs both accurately and extensively in an efficient manner.
To extract knowledge tuples of particular relation (e.g., "potential risk" illustrated in Figure D our approach requires only minimal input information that defines the relation of interest, namely initial prompt (e.g., The potential risk of is B) together with few shot of seed entity pairs (e.g., <eating candy, tooth decay>). The prompt offers the overall semantics of the relation, while the seed entity pairs serve to eliminate any possible ambiguities. For new relations not included in existing KGs, it is impractical to require a large set (e.g., hundreds) of seed entity pairs as in previous knowledge probing or prompt optimization methods (Petroni et al. 2019 Jiang et al. 2020 Shi et al. 2019 Zhong et al. 2021). Instead, our approach needs only very small set, such as 5 example pairs in our experiments, which can easily be collected or written by users.
key challenge of directly feeding the initial prompt and asking the LM to generate head/tail entities is that the outputs are often inconsistent: for example, slightly changing the wording of the prompt (while keeping the same semantic) can easily lead to different irrelevant LM outputs (Blazar et al. 2 Hase et al. 2021). The inconsistency renders the extracted knowledge unreliable and often inaccurate (as also shown in our experiments). Inspired by and generalizing the prior knowledge probing work (Petroni al.) 2019 Jiang al.] 2020: Shin et al.] 2020; Zhong et al.) 202T), we overcome the challenge by automatically creating diverse weighted prompts describing the same relation of interest, and searching for those entity pairs that are consistently favored by the
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multiple prompts under the LM. Interestingly, found that those automatically created prompts, though stemming from the initial prompt (often written by a human), are often better alternatives than the initial prompt itself for drawing more accurate knowledge from the LMs, studied in our experiments.
In the following sections, we describe the core components of our approach, namely the automatic creation of diverse prompts with confidence weights ($3.1) and the efficient search to discover consistent entity pairs ($3.2) that compose the desired KGs. Figure illustrate the overall framework.
3.1 AUTOMATIC CREATION OF DIVERSE WEIGHTED PROMPTS
Given the input information of a relation, namely the initial prompt and a few shots of seed entity pairs, we automatically paraphrase the initial prompt large set of prompts that are linguistically diverse but semantically describe the same relation. Each of the prompts is further associated with a confidence weight for an accurate measure of knowledge consistency the next section.
Specifically, starting with the initial prompt, we randomly sample entity pair from the seed set and insert into the prompt to form complete sentence. We then use an off-the-shelf text paraphrase model (see $4.1 for more details) to produce multiple paraphrased sentences of the same meaning. By removing the entity names, each paraphrased sentence results in a new prompt that describes the desired relation. To ensure diverse expressions of the relation, keep those prompts that sufficienlly different from each other in terms of edit distance. We repeat this process by continuing the paraphrasing of the newly created prompts, until we collect least 10 prompts for the relation.
The automatic creation of prompts is inevitably noisy. Some of the resulting prompts are less precise in expressing the exact meaning of the desired relation. we define a reweighting score for each prompt to calibrate its effect in the next knowledge search step. Specifically, we measure the compatibility of the new prompts with the seed entity pairs, and derive the prompt weights with the compatibility scores. Intuitively, given an entity pair (h, t), compatible prompt should induce high likelihood under the LM for each single entity plugged into the prompt. That is, the minimum likelihood of h and should be high. Besides, we also expect a high joint likelihood of h and t. Formally, the compatibility score is written as:
(I) where the first term is the joint nog-liieldoood under the LM distribution PLM, the second term is the minimum individual log-likelihood given the prompt (and the other entity), and a is a balancing factor for which we set = 2/3 in experiments.
[nm((h, ().p) = a log PI. (h, Up) + (1 - a)) min flog (hlp), log PLAS (4pp44))1)) 4)}
We compute the average compatibility score of each created prompt over all seed entity pairs. The weight of the prompt is then defined as the softmax-normalized score across all prompts.
3.2 EFFICIENT SEARCH FOR CONSISTENT KNOWLEDGE TUPLES
Given the above set of prompts with confidence weights, we next harvest entity pairs that consis- tently satisfy all the prompts. Each entity consists of one or more tokens. We reuse the above prompt/entty-pair compatibility function (Eq[I) and intuitively define the consistency of a new entity pair (hrew (new) as the weighted average of its compatibility with the different prompts:
(new)) consistency ((h (hrew) =,, 1p JIM (iaM(("eeu peew),
(2)
where wp is the prompt weight and the sum is over automatically created prompts as above. Therefore, an entity pair that is compatible with all prompts are considered to be consistent.
Based on the consistency criterion, we devise an efficient search strategy for consistent entity pairs. straightforward approach is to simply enumerate all possible pairs of entities, measure the respective consistency scores as above, and pick the top-K entity pairs with highest scores as the resulting knowledge for the KG. The approach can be prohibitively slow given the large vocabulary size V and the high time complexity of the enumeration (e.g., ( assuming each entity consists of only one token).
To this end, we an appropriate approximation that leads to a more efficient search and re-scoring method. More concretely, first use the minimum individual log-likelihoods (namely, the second
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term in the compatibility score Eq weighted averaged across different prompts (similar as in Eq to propose a large set of candidate entity pairs. The use of the minimum individual log- ligeliiil allows to apply a pruning strategy, described more in the appendix. Once we collect large number of proposals, we re-rank them with the full consistency score in Eq and pick the top-K instances the output knowledge.
We describe more nuanced handling in the search procedure (e.g., the processing of multi-token entities) in the appendix.
Generalization to complex relations Most existing KGs knowledge bases include relations that are predicates connecting two entities, e.g., "A is capable of B". Yet, many relations in the real life can be more complex. Our approach is flexible and easily extensible extract knowledge about those complex relations. We demonstrate in our experiments with two cases: (1) highly customized relations that have specific and sophisticated meaning, such as "A is capable of, but not good at, B". Such sophisticated knowledge is often hard for human to manually write down massively. Our automatic approach naturally supports harvesting such knowledge given only an initial prompt and few seed entities; (2) n-ary relations involving more than 2 entities, such as "A can do at C". Our approach can straightforwardly be extended to deal with n-ary relations by generalizing the above compatibility score and search strategy accordingly to accommodate more than two entities.
Symbolic interpretation of neural LMs The harvested knowledge tuples, as consistently recog- nized by the LM cross varying prompts, be seen the underlying "beliefs" (Stich) 1979 Hase et al. 202 I of the LM about the world. The interpretable symbolic tuples allow us to easily browse those beliefs, analyze the knowledge capability of the black-box LM, and make intrinsic comparisons between different LMs to understand the effect of diverse configurations, such as model sizes and pretraining strategies, as we showcased in the experiments.
4 EXPERIMENTS
We validate the effectiveness of the proposed knowledge harvesting framework with extensive experiments. We conduct human evaluation of our outcome KGs, and illustrate the effectiveness of the automatically created prompts our framework. Besides, using our framework as a tool to interpret the LM knowledge storage, we make interesting observations about several knowledge- related questions on the black-box LMs.
We will release the whole family of extracted KGs, as well as the code implementation of our framework (included the supplementary materials), upon acceptance.
4.1 SETUP
Relations We extract knowledge about large diverse set of relations. We collect relations from two existing knowledge repositories, and also test on set of new relations not included in existing KGs. Specifically,
relations include (1) ConceptNet (Speer et al., 2017) is one of the most popular commonsense KGs and is widely used for evaluating knowledge extraction. Following Li al. 2016), we filter the KG and subsample set of 20 common relations (e.g. has_subevent, mot sedivateddby_gooot
The initial prompts for the relations are from the ConceptNet repository, and we randomly sample seed entity pairs from the ConceptNet KG for each relation. (2) LAMA (Petroni et al.) 2019) is popular benchmark for factual probing, containing knowledge tuples from multiple sources. Following the recent work (Jiang et al., 2020, Shin et al.) 2020; Zhong et al., 2021 we use the T-REx split 41 Wikipedia relations, such as cap ttallloss of, member_ _f). For each relation, the human-wrilten prompt provided in Petroni et al. (2019) is used as the initial prompt and we randomly sample seed entity pairs for each relation. (3) New Relations: To test the flexibility of our framework for harvesting novel knowledge, we write 14 new relations of interests that can hardly be found in any existing KGs, and manually write initial prompts and seed entity pairs for them. The resulting relations include complex relations as described in $3.2 More details, e.g., initial prompts, and seed entities of relations, can be found in the appendix.

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Head entity
Relation
Tall entity
Head entity
Relation
Tall entity
humidity
prevent
temprature
potential risk
transmission
prevent
harm
prolonged sleep
potential risk
sleep disorders
can help
village
offence
potential risk
therapist
help
client
electricity
ingredient
electric lamp
lake
place
picnic tables
ingredient
soup
studios
place
live shows
ingredient
butter
apple
not good
wood
locomotive
but good
speed trainng
Relation: do (people, communicate,,
Relation: needs Isingers, eccompeniment, dance)
Relation: do
(adult couples, manry, marriage)
Relation: needs
(human survival, flourish)
Relation: do
(skiar, ski downhill, mountain)
Relation: needs
dialogue, portray characters)
Figure Examples of knowledge tuples harvested from DISTILLBERT (Randomly sampled).
Hyperparameters We use seed entity pairs for each relation. To automatically collect prompts (.3.1) we use GPT-3 with the instruction "par "paraphrase" (sentence] ) the off-the-shelf paraphraser. In the entity pair searching ($3.2). we restrict every entity to appear no more than 10 times to improve the diversity of generated knowledge and search out at most 50,000 entity tuples for each relation. We finally use various scoring thresholds to get the outcome KGs in different scales, including (1) 50 % taking a half of all searched-out entities with higher consistency (Eq. 2 (2) base-k: Naturally there are different numbers of valid tuples for different relations (e.g. tuples of CAPITALOF should exceed 200 that is the number of all the countries in the world). We design a relation-specific thresholding method, that is to set 10% of the k-nd consistency as the threshold (i.e.. 0.1 consistency v), and retain all tuples with consistency above the threshold. We name the settings base-10 and base-100 when k is 10 and 100, respectively.
4.2 EVALUATION
4.2.1
EVALUATING OUTCOME KGs
We apply our framework extract knowledge graph of ConceptNet (CN) re- lations and New relations from language models, and then conduct human eval- uation on the accuracy of the extracted knowledge with Amazon MTurk. The correctness of each ex- tracted knowledge tuple is labeled by 3 annotators with Leee//aas/Unnnuugeeeee tuple would be "accepted"" (acc) at least 2 annota- tors think it is true knowl- edge, and "rejected" (rej) if at least annotators rate it as false. We provide more details of human evaluation the appendix.
Method
Tuple
Diversity
Novelty %
Acc%
WebChild
4,649.477
82.0"
ASCENT
8,600,000
79,2*
TransOMCS
18,481,607
100,659
98.3
$6.0*
COMETCN
base-10
6,741
4,342
35.5
92.0
COMETCA
230,028
55,350
72.4
66.6
ROBERTANET CN
6,741
6,107
64.4
88.0
base-10
CN ROBERTAN NT
base-100
24,375
12.762
68.8
81.6
C.N ROBERTA NET 50%
230,028
80,525
87.0
55.0
ROBERTA NET baxe-=10 10
New
2,180
3,137
81.8
ROBERIANET base-100
7,329
6.559
68.6
ROBERTANET 50%
23,666
16,089
58.6
Table 2: Statistics of KGs constructed with different methods. Diversity refers to the number of unique entities in KG, and Novelry refers the proportion of entities that don't appear ConceptNet. The acceptance rate with are from original papers and subject different evaluation protocol. Given COMET can only predict the tail entity given a source entity and a relation, we generate KGs with COMET by feeding it the head entity and relation of our RobertaNet. The first blocks of this table corresponds the first blocks Table which includes three popular text mining methods: WebChild [Tandon all 2014) ASCENT [guyen et al 2021 and TransOMCS (Zhang et al. 2020
The statistics of the gener-
ated KGs from our frame- work (ROBERTANET) and
other knowledge extraction methods are mentioned Table 2 and the samples of the extracted KG be found in Figure The acceptance rate, indicating the precision of KGs, in Table 2 are not comparable to each other, and only serves for a rough sense. From those statistics,
from DISTILLBERT
that with solely the LM the knowledge source, and without any training data,
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AutsPrompt
0.9
huterrmmpt
0.8
LPA04
COMET
numar-rritenn Promps
Top-1 Prompt countt
0.8
Mulipic Prompts tours)
Tap-1 Promet
Precision
0.7
Multidle Prompis (oun)
Precision
0.6
0.6
0.5
0.5
0.2
0.4
1.0
0.0
0.2
0.4
0.6
0.8
1.0
Recall
Recall
Figure 4: Precision-recall curve on LAMA rela-
Figure Precision-recall on ConceptNet
tions.
Methods
Acc
Rej
Source LMs
Acc
Rej
AUTOPROMPT
0.33
0.47
DISTIL BERT
0.67
0.24
HUMAN PROMPT
0.60
0.27
BERT-BASE
0.63
0.26
TOP-1 PROMPT (Ours)
0.69
0.23
BERT-LARGE
0.70
0.22
MULTI PROMPTS (Ours)
0.73
0.20
ROBERTA- BASE
0.70
0.22
ROBERTA-LARGE 0.73
0.20
The portions of accepted and rejected ples human evaluation across settings, with the ROBERTA-LARGE as the LM.
Table 3:
Table 4: The portions of accepted and rejected tuples in human evaluation across different LMs, using the MULTI-PROME PROMPTS approach.
our framework extracts KGs with competitive accuracy. Compared to COMET, our precision is a little lower, which is expected due the big compromise make in the setting, and our searching constraints ensure better diversity and novelty of ROBERTANET over COMET.
4.2.2 EVALUATING AUTOMATIC PROMPT CREATION
To evaluate the effect of our automatic creation of prompts, we compare the generated KGs under these settings of prompts on the human-wrine wwrtteen new relations: (1) Multi-Promptss refers to the the full framework described in } which use the automatically created diverse prompts in knowledge search. (2) Top-1 Prompt: To ablate the effect of ensembling multiple prompts, we evaluate the variant that uses only the prompt with largest weight ($3.T) for knowledge extraction. (3) Human Prompt: To further understand the effectiveness of the automatically created prompts, we assess the variant that uses the initial prompt (lypically written by human) of each relation. (4) AutoPrompt (Shin et al. 2020). which was proposed to learn prompts by optimizing the likelihood of tail entity prediction on the training set. To fit in our setting, we adapt it to optimize the compatibility score [ on the seed entity pairs. We omit other prompt tuning work (e.g.. g...Znngg al. 2222 Qin & Eisner 2021 because they either are difficult to fit in our problem or require more training data and fail with only the few shot of seed entity pairs in setting.
The human-annotated accuracy is shown in Table 3 Our TOP-1 PROMPT significantly improves the accuracy up to 9% over the HUMAN PROMPT, indicating our prompt searching algorithm can produce high quality prompts. MULTI-PROMPTS further improves the accuracy by around 4%, which means the combination of diverse prompts better capture the semantics of a relation. The method using the optimized prompt by AUTOPROMPt gives a lower accuracy than the one with the human or searched prompt, because the small set of seed knowledge tuples are insufficient for learning effective prompts for the desired relations.
Based on the results above, we move a step forward to see how the created prompts influence the subsequent module in the framework. Specifically, we evaluate if the automatically created prompts (3.11) bring the consistency scoring 33.2 better balance knowledge accuracy (precision) and coverage (recall), by comparing various scoring methods on existing terms in ConceptNet and LAMA dataset. To be more detailed, we use the knowledge tuples from ConceptNet and LAMA as positive samples I). and synthesize the same amount of negative samples with the same strategy in Lil jet al. (0166) by random replacing entities or relations the true knowledge. Each evaluated method estimates a score of each sample being positive (i.e., true knowledge), and ranks the samples based
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on the scores. We can then compute both the precision and recall of positive samples at different cut-off points along the ranking, and plot the precision rercision-recall curves for each method.
The automatic evaluation setting given knowledge terms also enables to adapt existing prevalent
works, c.g., KG completion and factual probing (Table [T). for comparison with our approach: (1)
COMET (Bosselut al. 2019) is transformer-basee KG completion model trained to predict the tail entity conditioning on the head entity and relation (h, r) on ATOMIC (Sap et al. 2019). The model has encoded rich commonsense knowledge. We thus test it on the ConceptNet commonsense data, and use its log-likelihood log P(t),, r) as the score for each given knowledge tuple. (2) LPAQA (Jiang al., 2020) collects set of prompts on LAMA with text mining and paraphrasing, and ensembles them to optimize the factual probing task (and thus cannot extract new knowledge like ours). We report the best performance of its three variants.
The resulting precision-recall curves on ConceptNet and LAMA knowledge are shown in Figure 3 and Figure [4 respectively. The variant of our TOP- 1 PROMPT from our automatically-collecde prompt set is significantly better than the HUMAN PROMPT. This is because the human-writter prompts often fail to effectively account for possible ambiguities in the expression, which are eliminated in our approach by leveraging the seed entity pairs with compatibility measure. Increasing the number of prompts PROMMTS) provides further improvements, showing the multiple automatically created prompts can represent the desired relations more accurately and better guide the LMs for consistency. Our approach also substantially outperforms other baselines, like COMET on ConceptNet and LPAQA on LAMA, indicating better knowledge correctness (precision) and coverage (recall) of our framework in the context. On both datasets, AUTOPROMPT, with only 5 seed entity pairs as its training data, gives inferior performance than other approaches, indicating that the current prompt optimization is not feasible for discovering knowledge of new relations without large training data.
4.3 ANALYSIS OF KNOWLEDGE IN DIFFERENT LMS
As discussed in 3 the outcome knowledge graphs can be treated as a fully symbolic interpretation of pretrained language models. We use MULTI-PROMPTS to harvest KGs from 5 different LMs and evaluate them with human annotation. The results are shown in Table 4 which sheds some new light on several knowledge-relate questions regarding the LMs' knowledge capacity.
Does a larger LM encode better knowledge? For BERT (and RoBERTa), the large version and the base version share the same pretraining corpus and tasks, respectively, while the large version has a larger model architecture than the base version in terms of layers (24 v.s. 12), attention heads (16 v.s. 12), and the number of parameters (340M v.s. 110M). We can see that BertNet-large and RoBERTaNet-large are around 7% and 3% higher than their base version, separately, so the large models indeed encoded better knowledge than the base models.
Does better pretraining bring better knowledge? RoBERTa uses the same architecture as BERT but with better pretraining strategies, like dynamic masking, larger batch size, etc. In their extracted knowledge graphs from our framework, RoBERTaNet-large performs better than BertNet-large (0.73 v.s. 0.70), and RoBERTaNet-bass is also better than BertNet-base (0.70 v.s. 0.63), which indicates the better pretraining indeed bring the better knowledge learning and storage.
Is knowledge really kept in the knowledge distillation process? DistilBERT is trained by distilling BERT-base, and reduces 40% parameters from it. Interestingly, the knowledge distillation process instead improves around 4% of accuracy in the result knowledge graph. This might be because the knowledge distillation is able to remove some noisy information from the teacher model.
5 CONCLUSION
We have developed an automatic framework that extracts a KG from a pretrained LM (c.g, BERT, ROBERTA), in an efficient and scalable way, resulting in family of new KGs, which we refer to as BERTNET, ROBERTANET, etc. Our framework is capable of extracting knowledge of arbitrary new relation types and entities, without being restricted by pre-existing knowledge or corpora. The resulting KGs also serve interpretation to the source LMs, bringing new insights of the knowledge capability in various LMs. Our current design and experimental studies are limited on LMs in the generic domain, and are not yet been studied in specific domains such as extracting healthcare knowledge from relevant neural models. We leave the exciting work of harvesting knowledge from various kinds of neural networks across applications and domains in the future work.

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Ethical considerations In this work, the harvested knowledge is automatically generated by LMs. We would like to note that the language models could possibly generate unethical knowledge tuples, same with the risks of other applications using language models for generation. We hope that the knowledge extraction study could offer techniques to better interpret and understand the language models, and in turn foster the future research of language model ethics. Since the knowledge graph only consists simple phrases, we think filtering sensitive words would be effective. No foresecable negative societal impacts are caused by the method itself.
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A APPENDIX
A.1 COMPUTE RESOURCE
All of our experiments are running single Nvidia GTX1080Ti GPU. Harvesting knowledge graph of one relation with Roberta-large takes about one hour.
A.2 THE LICENSE OF THE ASSETS
All the data we used in this paper, including datasets, relation definitions, and seed entity pairs, etc., are officially public resources.
A.3 PREPROCESSING OF CONCEPTNET
We filter out some linguistic relations (e.g.
derived from) and some trivial relations (e.g. relatee lateed to). We only consider the tuples with confidence higher than 1, and filter out relations comprising less than 1000 eligible tuples. We don't 't directly take the test set from Li et al. (2016) because they reserve a lot of tuples for training, resulting in a small and unbalanced test set.
A.4 RELATION DEFINITIONS
The initial prompts of ConceptNet are from its repository For LAMA relations, we use the human written "template" for every relation as the initial prompt, which are all included in the LAMA dataset. All the New relations, together with the generated prompts, are shown in Table and the list below.
A.5 PROCESSING OF MULTI-TOKEN ENTITIES
Each entity may take more than one BPE tokens when conducting the mask mask-filling task. In our entity searching step, before searching the tokens to fill the mask, we enumerate the number of masks for every entity and in our setting, each entity has one or two BPE tokens. showease likelihood calculation of multiple tokens in Figure 7
In the candidate entity pairs proposal step, we use the minimum individual log-likelihoods (shorted as MLL) instead of the full Equation which allows to apply pruning strategies. For example, when we are searching for 100 entity tuples, we maintain a minimum heap to keep track of the MLL of the existing entity pair set. The maximum size of this heap is 100, and the heap top be used as a threshold for future search because it's the 100-th largest MLL: When we are searching for can
new entity tuple, once find the log likelihood at any time step is lower than the threshold, prune the continuous searching immediately, because this means the MLL of the this tuple will never surpass any existing tuples in the heap. If a new entity tuple searched out without being pruned, we will pop the heap and push the MLL of the new tuple. Intuitively. the pruning process makes sure that the generated part of the tuple in searching is reasonable for the given prompt.
A.6 HUMAN EVALUATION
The wage $0.15 for each three questions. The average time for answering a question is about 15 seconds, so the estimated wage of an hour is S12 per hour. The total cost on human annotation
http://ttteeemmmmmmmmmmccmmeeett (the "Deseription"
"DDestitpi" column.)
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Insiructions (click to expandicellapue)
You have the access to formal HIT becouse you per form the Quallication Good job!
The instructions the same for every HIT the task. Feel free to skip it you have read this.
You will be given statement the form of relation, B", and you are expeced to decide statement is True/false
Examples:
Tokyo, capital ef, Japan True
human, capable of, fly False
Gandhi, repretentativn figure of, India >> True (When comes to spocific personfloraaion etc., please google it if you are not sure about the statement)
representative figure, well (Sometimes statement be weird, "Talse"
good foorball Unjudeable (Recause
phone, location, bag True (When statement is general, true as long makes sense))
phone, at location, fridge False (Tiss docen't usually happen)
Small gramimatical orres should be ignored (r human name not capitalized, noun the plural form...
Somatimes the relation name not informative anoug", and you may chack the definition and examples of the relation by clicking
burton nearby.
There are total of questions in one HIT. Please leave any message the infibbxx if there still something undlar you.
Recommended time for one question: approx. 20 seconds.
There will he about 10 relations toral, After familiar with rhem, most of the statements choulds be quite to jusge and you can ance, Sometimes, google making choica.
requires speafic knowledge which gight don't know: that
Figure 5: The instruction to annotators
Question 1
Evoluete the
Stent 11 Strckatio_11 1
betwicel Faa hold vebtoon "eroGrion
staioretts
Steampl, haadd Sowator.1f 1) Steampa, (all
Sovaingl heart traaston 11 Shnarpple
S/veepgl 1. Luredd Stmastne 1) Shwarpis 1,3
Question 2
Evefuete the statemont:
s/ent
Stritaton 2), Nant_2.3
Ser 1Andd J.Ml
2 fowator_)) 2.paa)pla___.aa)
Sjowmpge 2. Uiwe Stwadon 2.144)
Figure 6: The questions to annotators
is $1580. We present the screenshot of the instruction in Figure and question in Figure 6
The
inter-annotator agreement (Krippendorff's Alpha) 0.27, showing fair agreement.
Relation
Initial prompts
Seed entity pairs
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(people, work out, gym) (bird, fly, sky) (student, study, classroom) (player, play, ground) (sodier, fight, battlegroond)
somebody do something at ENTo can ENT; in ENT;
(mask, virus) (exercise, discase)
prevent
ENTo prevents ENT;
(study hard, fail the exam) (reading, stupid) (insurance, bankruptcy)
(doctor, patient) (teacher, student)
help
ENT. can help ENT;
(housekeeper, housewife)
(teaching assistant, professor)
(police, victim)
(gym, exercise)
(classroom, study)
place
ENTo is place ENT,
(office, work)
(hosipital, medical treatment)
(mart, shopping)
(fat, thin)
(happy, sad)
antonym
ENTo is the opposite of ENT,
(north, south)
(woman, man)
(pass, fail)
(China, Japan)
(Australia, New Zealand)
separated by the ocean
ENT, and ENT separated by the
(United Kingdom, the Continent)
(United State, Cuba) (Spain, Morocco) (flour, cake)
(beef, hamburger) (potato, chip)
ingredient for source
ENT, is ingredient for ENT,
(fried rice, rice)
(stargazer pic, pilchard)
(wool, woollen sweater)
(milk, yogurt)
ENT is source ENT:
(sand, silicon)
(iron mine, iron)
(crude oil, fucl) (Nissan, car) (Apple, laptop)
business
ENT sells ENT,
(Shell, oil)
(Nvidia, GPU)
(McDonald's hamburger)
(egg, cheap. food)
(boa, long, snake)
featured thing
EN a very ENT, ENT.
(Messi, skillful, football player)
(Russia, large, country) (Amazon, rich, company)
(developer, computer, code)
(people, social media, connect)
need sth do sth
ENT: needs ENT; ENT:
(pig, food, grow)
(people, money, live)
(interm good performance, return offer) (chicken, fly)
(dog, swim)
can but good
ENT. can ENT; but not good at
(long- (long-distance runner, sprint) (skater, ski) (researcher, teach)
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(team, wim)
(student, pass exam)
worth celebrating
It's worth celebrating for ENT to ENTi
(researcher, publish paper) (people, money) (parents, get baby) (playing game, fail the exam) (sleap insufficiency, heart discase)
potential risk
A potential risk of ENT, is ENT;
(candy, tooth decay) (flight, plane crash) (investment, lose money)
Table 5: prompt and entity pairs of new relations
The generated prompts are listed below:
somebody do something at: at the 2, ENT, ENT; ENTo can ENT, in the ENTz the ENT, is place where ENT, ENT, the ENT, can choose to ENT, in the ENT; or online
ENT, can learn effectively by ENT,ing in ENT, setting looking up at the ENT2, it's easy to see that ENIos can ENT; the ENT,, EN can ENT; together and learn from each other a ENT, can ENT; in ENT, by themselves with group of friends the E.T2,, ENT, can use the machines ENT, or they can take classes at the ENT,, ENT, can ENT; by using the various machines and equipment available there are many facilities available for ENTo who want to stay in shape and ENT,, one of which is the ENT,
2. prevent:
ENT, prevents ENT;
if you ENT, you will not ENT,
ENT, provides a safety net against ENT; if you ENT., you less likely ENT, a ENT will protect you from getting the ENTi if you want avoid being ENTi, start ENTo more if you don't "on't want to be ENT, you should ENT, often ENTss are effective at preventing the spread ENT,es this is a paraphrase of the saying ENT prevents ENT,ity. ENT, is an important part of staying healthy and preventing ENT, regular ENT, has been shown to be one of the most effective ways to prevent ENT;
ENT, protects people from having to declare ENT; in the event of an accident or emer- gency
ENTos prevent the spread of ENTies es by trapping droplets that are released when the user talks, coughs, sneezes it could be said that ENT, prevents ENT, as it provides a safety net for people in the event of unexpected setback
regular ENT, has various health benefits and is often prescribed by doctors as preventative measure against developing various ENT,
3. help:
a ENT, can help ENT;
the ENT, can help with the ENT, 's chores
a ENT, can assist ENT, with her duties
if you are a ENT, a crime
the ENT, can help you
the ENT, can help the ENT; with their medical needs
ENT, can help ENT, by providing guidance and support a ENT, can help take care of the household duties for a ENT;
E.T,, officer can help E.NT, by providing them with protection and assistance
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ENTo can help with the chores around the house, giving the ENT; more free time a EN can help out a ENT, by doing things like cleaning, cooking, and running errands ENTo can help ENT, in many ways, from grading papers leading discussion sections a EN can help a ENT; by providing guidance, answering questions, and offering feed- back
a ENTo can help a ENTI with their workload by taking on some of the teaching responsi- bilities
a ENT, can help ENT, by grading papers, leading discussion sections, and providing office hours
the ENTo can help ENTis of crime by investigating the incident and taking statements from witnesses
4. place for:
the ENT, is the place for ENT; there's 's no place like E for ENT, EN is great place to do some ENT, the EN there's s no better place to ENTI than in an ENTo the ENT, the best place to get some ENT, done the ENTo provides an environment for people to ENT; if you want to do some ENNT, ing, you can go to the ENT, if you'r re looking to do some ENT, EN is the place for you if you're re looking to some ENT, then you should head on over to ENTo if you'te 're looking to get fit and ENT, the ENT, is the perfect place do so there's 's no place like the ENTo for ENT; because you have access to all the equipment you need to get good workout
place where people go ENT,
5. antonym:
ENTo is not ENT,
the antonym of ENTo is ENT, ENT, is the opposite of ENT, when you'l re ENT,, you're 're not ENT, day
ENT, is to ENT, as night ENT, and ENTi are opposite concepts if you ENT, it means you didn't 't ENTo when person is ENT, they are not ENT; the two words " "ENTo" and "ENTi" are antonyms ENT, is the direction that is opposite of ENT. to be ENT, is to have small amount of body ENT, when something EN,bes, is successful, and when something ENTis, s, it is not successful if you ENT something, you have succeeded, and if you ENT; something, you have not succeeded
this means that simply because someone does not ENT, something does not mean they have ENTied
6. separated by the ocean:
the ocean separates ENT, from the ENT, there is an ocean separating ENT, and ENT, the pacific ocean lies between ENT, and ENT, ENT, and E.N,, separated by the tasman there is a distance of about 1 1,500 miles between ENT, and the ENTos the distance between ENT; and ENT, vast, with an ocean between them ENT, and ENT, are two different countries located on different continents
ENT, and ENT, are two countries that are close to each other but are separated by the ocean
there large body of water, known as the atlantic ocean, which separates the two land masses of ENT; and the ENT, while ENT, and E.T,, are both located in the oceania region of the world, the two countries separated by the tasman sea
7. ingredient for: ENT, needs ENT, ingredient
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Under review as a conference paper at ICLR 2023
way to use ENTTes is to make ENTs ENT, is a key ingredient in making a ENT, one of the ingredients for making ENT, is ENTo if you want to make a ENT, you'll need some F.NTo
F.NTo is ingredient that is used to make ENNTs
ENTo is a dish that contains ENT; as one of its ingredients
ENTo is popular dish made with ENT, as the main ingredient
ENTo is type of fish pie that is typically made with E
ENT, is dish made from ENT, that has been fried in a wok or a pan
8. source of:
ENT, comes from ENTo
ENT, is used to make ENT; most ENT, is made from ENT EN is major source of ENT, the main source of ENT; is ENT, the EN is what turns into ENTT ENT, is most commonly found in ENTos the ENT, the place where ENT; is mined many ENT1s contain ENT, as a major ingredient ENTos are where ENT, is pulled from the ground ENT; is dairy product that is made from ENTo EN is the primary ingredient the production of ENT, the main source of ENT; is quartz, which is type of ENTo ENT is the main source of ENT; used in vehicles and other machinery
ENT, is used to produce gasoline, diesel, and other petroleum-based products, which are widely used as ENT; for cars, trucks,
9. business:
ENTo sells ENTis
ENT, is an ENT; company
ENT, is known for selling ENT,s
ENT, is company that sells ENT,
ENT, is an international ENT, and gas company
ENT, is a ENT, company that manufactures and sells vehicles
ENT, sells ENTis through its online store and retail locations
ENT, is a company that sells graphics processing units (ENTis)
ENT, is popular fast food chain that is known for selling ENTis
ENT, is jjapaneseF.NNT manufacturing company that is headquartered in yokohama,
japan
ENT, is a fast food company that specializes in ENTis, fried chicken, and soft drinks
ENT, is company that specializes in the production of ENTis, or graphies processing units
ENT, is a japanese ENT; company that sells wide variety of vehicles, from small to large, and from economy to luxury
E. describes themselves as "the worldu2019, largest information technology company by revenue," and they sell many products, including ENTis
10. featured thing:
the ENT, ENT,, is very ENT, EN are a very ENT, ENT,
ENT, is definitely ENT, ENT.
ENT, is a very ENT, and successful ENT, you can hardly find ENT,er ENT, than ENT, there is no doubt that ENT, is very ENT, ENT, it covers lot of ground, ENT, is a very ENT, ENT, a single ENT, is a very ENT, and affordable ENT, item
OS are type of E.NT, that can grow to be very ENT, although ENTo is not the world's ENT, ENT, it is still very wealthy
11. need sth to do sth:
ENT, need ENT, ENT,
18

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Under review as a conference paper at ICLR 2023
in order to ENT,, ENT, need ENT; there is a need for ENT, to ENT, ENT, ENT, requires ENT, in order to ENT, EN feel the need to FNTT2 through ENT, E. requires E.NT, to E.NT2 with each other EN feel that they need ENTT i in order to ENT; ENT, will only ENT, if has enough ENTi eat without ENT,, ENT, would have a harder time E.NTying with others it is important for ENT, to be able to ENT, with each other through ENT;
12. can but not good:
ES are not good at ENTT
EN can ENT, but they aren't very good at it the average ENTo is not especially good at ENT,ing the ENT, skilled at ing, but not particularly good at it Es are not good at ENNTinng because they are not built for it E.NT,ing ing requires explosive speed and power, which ENTos typically lack ENTos aren't t particularly good ENT mers, but they can do it if they need to while ENoos are capable of ming, they are not particularly proficient at ENTos are not good at ENTT ing because they lack the proper equipment and training while E are typically excellent at their jobs, [ is not usually a strong suit
although ENT,s are theoretically able to ENT, they are not very good at it and usually stay on the ground
while FN are able E. they not instinctively good at it and may need some help or encouragement to do so
13. worth celebrating:
it's good when ENT, ENT it's great for ENT to ENT; it's great when group of ENT ENT it's great when E' ENTis as a group it's worth celebrating when a ENT, ENT; it's always good when ENT, ENT, is doing well when group of ENT, ENT, it worth celebrating there is value in celebrating when group of EN ENT, it's always good when EN ENNis regardless of the topic it is seen significant accomplishment for ENTo when they ENT
14. potential risk: ENT, may lead ENT;
ENT, can potentially cause ENT; one potential risk of ENTo is ENT, eating too much ENT. can cause ENT, if ENT1es es, it is potential risk of ENT, if you have ENT, you may be at risk for ENT,
ENT, has been linked to an increased risk of ENT;
E.NT, always entails some risk2014you could ENT, on it ENT,, can lead to ENT, if it is not eaten in moderation an ENT, always comes with the potential to make or ENT; if ENT, is not properly executed, it can cause a ENT; if ENT, is not well-planned or executed, may lead to a ENT; a new study has found that ENT, is linked to an increased risk of ENT; if you have a ENT, you are a greater risk for cavities if you eat ENT, there is a strong correlation between ENT, and an increased risk of airEs a recent study has found that there is a correlation between eating ENT,, and an increased risk of ENT
A.7 DETAILED RESULTS OF HUMAN EVALUATION
We show the detailed results of human evaluation in Table d
19

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Under review as a conference paper at ICLR 2023
Table Detailed result of human evaluation. The numhers indicate the portions of accepted and rejected tuples. Ro-1, DB, B-b, B-I, Ro-h are short for Roberta-large, DistilBert, Bert-large, Bert-hase, Roberta- coberta-hase. Human, Auto. Top-1 and Multi stand for methods that use Human Prompt, Autoprompt, Top-1 Prompt (Ours) and Multi Prompts (Ours).
Model
Ro-1
Ro-1
Ro-1
Ro-1
DB
B-1
Ro-b
Prompt
Human
Auto
Top-1
Multi
Multi
Multi
Multi
Multi
BUSINESS
0.600.32
0.76/0.13
0.75/0.16
0.880007
0.54/0.27
0.64/0.23
0.7640.1 13
0.74/0.19
HELP
0.73/0.12
0.52/0.34
0.92/0.03
0.87/0.05
0,91/0.04
0.81/0.04
0.88/0.06
0.88/0.06
INGREDIENT FOR
0.59/0.33
0.33/0.59
0.73/0.20
0.71/0.24
0.70/0.26
0.55/0.40
0.72/0.23
0,51/0.40
PLACE FOR
0.760..10
0.41/0.36
0.63/0.32
0.89/0.07
0.84/0.14
0.78/0.18
0,8740.11
0.88/0099
PREVENT
0.42/0.42
0.18/0.67
0.60V0.25
0.40/0.45
0.6000.32
0.44/0.39
0.62/0.25
0.68/0.25
SOURCE OF 0.76/0.17
0.21/0.67
0.52/0.44
0.60/0.33
0.63/0.36
0.65/0.32
0.75/0.24
0,55/0.37
SEPARATED BY
0,48/0.38
0.16/0.48
0.56/0.35
0.55/0.40
0.51/0.24
0.57/0.26
0.44/0.46
0,44/0.49
ANTONYM 0.50/0.41
0,10/0.83
0.5000.48
0.55/0.44
0,38/0.56
0.41/0.56
0.52A1.42
0.75/0.22
FEATURED THING
0.85/0.12
0,38/0.40
0.88/0.06
0.89/0.10
0.37/0.44
0.44/0.40
0.46/0.44
0.65/0.20
A CAN BUT NOT GOOD AT
0.71/0.18
0.62/0.21
0.66/0.22
0.79/0.10
0.83/0.12
0.62/0.25
0.65/0.18
0.72/0.17
0.52/0.34
0.29/0.42
1161//011
0.44/0.21
0,51/0.31
0.60/0.21
0.64A0.22
0.39/0.35
WORTH CELEBRATING
0.47/0.29
0.23/0.51
0.81/0.05
0.85/0.08
0.79/0.12
0.74/0.14
0.84/0.10
0.83/0.10
0.40/0.23
0.31/0.45
0.70/0.21
0.76/0.19
0.87/0.05
0.66/0.22
0.72/0.16
0.79/0.08
A DO B AT
0.56/0.33
0.14/0.55
0.79/0.14
0.97/0.03
0.93/.077
0.93/0.05
0.94/1.06
0.94/0.06
AVERAGE
0.6000.27
0.33/0.47
0.69/0.22
0.73/0.20
0.67/0.24
0.63/0.26
0.70/0.22
0.700.22
(h
(an(O:
(Multiple tokens)
BERT
BERT
Pax PowChalp.h
[MASK] the place [MASK]
[MASK] [MASK] place (MASK]
Pax (t)
BERT
BERT
Library is the place for [MASK]
Study (MASK) is the place for [MASK]
We demonstrate the calculation with example where p = two figures shows how we calculate PLar (fip) and PL. pp h). this example, =libbrary" when we set both head and tail entities to have one single token. The right block shows how we calculate the conditional probability of multiple-token entities by decomposing it into two steps. In this example, the first token of the head entity h =stuudy"
Figure 7:
PLACE FOR B". The left
20


Reviews:
[{"review_id": "zomGsXHDdtD", "reviewer": "Reviewer_pDgC", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper proposes a pre-trained LMs based KG construction model aiming at identifying new entities for a single query relation. The proposed model is a bootstrapping strategy that starts from a query prompt with a few seed entity pairs and iteratively generates new pairs by means of new prompts generated by a GPT-based paraphrase model. Further, an LM-based reweight function is employed to weigh the new prompts and new pairs. The experiments conducted on crowdsourcing-based evaluation and case study to show the model is competitive to SOTA baselines. ", "summary_of_the_review": "1. The proposed framework is a kind of bootstrapping-based knowledge graph construction approach, which, reminisce of the Hearst patterns for hypernym relation. My major concern is how to effectively handle the semantic drift, i.e., the semantics of new patterns and instance harvest may change drastically along with iteration or paraphrasing. But authors did not discuss this problem in-depth or provide a method with a plausible bound. What we have in the model is the LM-based paraphrasing and weighting, however, the key point is, LM does not necessarily guarantee the semantics.\n\n2. For the diversity of paraphrase (second paragraph of Section 3.1), it is not clear (1) why to use a textual measurement (i.e., edit distance) to ensure diversity of paraphrases (second paragraph of Section 3.1). It is a bit weird that the whole paper is deep learning based, except for the paraphrase generation, which adopts an old-fashioned textual similarity ED, which, is obviously out of the deep learning fashion. (2) how this ED constraint works. As ED works for a text pair, do all generated prompt pairs need to keep a certain distance?\n\n3. Some important technical details are missing:\n  (1) Efficient generation candidate entity pair generation of Section 3.2. Appendix A.5 has a few sentences talking about \"mask-filling\" but I cannot find the details of how to generate multi-token entities and how to use \"thresholding and pruning\" to reduce the candidate set.\n  (2) For computing Eq.1, it is not clear (a) how to generate the query text. Is the query text formed by inserting a specific entity (e.g., insert h into p for P(h|p))? If so, what is the difference between Plm(t|p,h) and Plm(h,t|p)? (b) how to compute the probability of Plm(h,t|p), Plm(h|p), and Plm(t|p,h). Is the probability computed by the generative probability of the LM?\n\n\n4. In addition to the manual evaluation (crowdsourcing rating), the objective evaluation based on evaluation datasets should be given. For example, the authors can select datasets adopted by relevant tasks, e.g., knowledge graph completion, such as WRNN, FB15K-237, etc. for evaluation.\n\n5. The baseline methods should be introduced in a few sentences. \n\n6. The layout of Table 2 should be fixed.\n", "strength_and_weaknesses": "Strength:\n1. The problem of automatically constructing KG from pre-trained LMs is well-motivated.\n2. It is interesting to combine the iterative KG construction into the current pre-trained LMs fashion.\n\nWeakness:\n1. The framework of iterative KG construction (i.e., bootstrapping) is not novel.\n2. Some important technical details, e.g., how to generate multi-token entities and how to reduce the candidate set, are missing.\n3. The experiment is not comprehensive. There is no objective evaluation.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "YYlLyS_z9M", "reviewer": "Reviewer_dt4V", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "Authors proposed a method to extract knowledge graph relations from a pretrained LM through automatic prompt creation and leveraging the pretrained LM to score the candidate entity pairs. Compared to previous works that usually rely on human annotated data or existing massive KGs, the authors' approach requires only the minimal definition of relations as inputs.", "summary_of_the_review": "Overall, the idea proposed in this work is simple, easy to implement and it shows promising results compared to the baselines. However, the evaluation currently lacks analysis about the coverage and quality of the extracted relations.", "strength_and_weaknesses": "Strength:\nThe proposed idea is simple and seems straightforward to implement. The proposed method needs minimal human input (only an initial prompt and 5 candidate entity pairs for each relation). It effectively leverages GPT3 to rephrase and generate better alternative prompts used for extracting new entity pairs. Overall, the paper is well written with detailed examples to illustrate the main idea.\n\nWeaknesses:\nAlthough the model shows better results than the baseline (COMET), it still lacks more analysis in the evaluation to convince the effectiveness of the proposed model. \n\nFirst, the result in Table 2 shows the precision of the prediction but not the recall. For example, the recall can be estimated on the known set of factual relations. The P-R curves in Figures 3 and 4 seem to partly answer this question. However, looking at the 0.8 level of precision, the recall is only less than 0.2, which raises another question about the overall effectiveness of the extraction method.\n\nThe baseline WebChild in Table 2 seems to extract a much higher number of tuples at 4.6M, while the proposed model ROBERTANET only extract 6741 tuples. It can be WebChild having much higher coverage, but this difference was not discussed in the paper.\n\nSecond, the quality of the extraction heavily depends on the compatibility scoring function (Eq. 1). It would be better to evaluate more on the design choice of this formula and explain why the proposed formula is optional for this purpose.\n\nThird, Looking at the extracted relations, it looks like some relations are more non-trivial than others. It would be useful to analyze more on the factual relations (e.g., the relations between named entities such as 'ceo_of', 'invent') because these relations will be more useful for downstream applications like QnA. The LAMA dataset has more of these relations so authors can extract and analyze more case studies from this dataset.\n\nAnother suggestion, in the GPT3 prompt: \"paraphrase: {sentence}\" used to extend the prompt set, would it be better to include example of all 5 seed entity pairs into the prompt to help eliminating the ambiguity?\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "IxdYYn8CCjx", "reviewer": "Reviewer_XQg6", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "By probing pre-trained language models with prompts, the paper presents a novel and practical framework for automatically constructing knowledge graphs. The framework's input consists of relation definitions and a small set of seed entity pairs. For each relation, the framework generates and then paraphrases prompts. The extracted entity pairs of each relation are then evaluated using the proposed consistency score. Finally, it reranks these pairs and outputs the top-K results. Experiments show that the resulting KG is diverse and accurate. The proposed automatic prompt generation method is also effective. The resulting KG can serve as a symbolic interpretation\nof pre-trained language models.", "summary_of_the_review": "Overall, I think the paper has both advantages (a practical KG construction pipeline) and disadvantages (some small issues). It can be further improved. ", "strength_and_weaknesses": "Pros:\n\n* The work's most significant strength is its practical pipeline for automatically constructing knowledge graphs (KGs) from pre-trained language models (LMs). Previous research on \"LMs as KGs\" or \"knowledge probing from LMs\" has primarily focused on prompt generation and does not cover the entire KG construction pipeline.\n\nCons:\n\n* One potential shortcoming of the work is that its technical contribution may not be sufficient for a research paper.\n\n* The paper also has a few minor flaws, and the writing could be improved, as listed below:\n\nMinor issues:\n\n* The multi-token issue in entity names. The authors state in Sect. 3.2 that \"Each entity consists of one or more tokens\". How to resolve the multi-token issue in the likelihood computation? Or, for example, how to compute P_{LM}(t|p,h) if the name of entity t has more than one tokens? In Appx. A.5, the authors state that \"we enumerate the number of masks for every entity\". Is there a maximum value for such a number? Is it a hyper-parameter? \n\n* Sect. 3.2 says that \"The use of the minimum individual log-likelihoods allows us to apply rich thresholding and pruning strategies (e.g., thresholding on log PLM(h|p) for proposing the head entities), as described more in the appendix.\" However, it seems that there is no related content in the appendix. Or, is it Appx. A.5?\n\n* It would be preferable to include a subsection in Section 4.2 that simply introduces the baselines used in the experiment.\n\n* In Sect. 4.2, the authors state that \"we restrict every entity to appear no more than 10 times to improve the diversity of generated knowledge\". This, in my opinion, is not reasonable because it would result in a sparse KG. Table 2 also demonstrates this problem.\n\nTypos:\n\n* \"Those systems, however, often involves a complex set of components\" -> Those systems, however, often involve a complex set of components.\n\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject