Language Models are Open Knowledge Graphs
Authors: Chenguang Wang
Xiao Liu
Dawn Song
Venue:  (2021)
Keywords: Language Models
Knowledge Graphs
Information Extraction

Abstract: This paper shows how to construct knowledge graphs (KGs) from pre-trained language models (e.g., BERT, GPT-2/3), without human supervision. Popular KGs (e.g, Wikidata, NELL) are built in either a supervised or semi-supervised manner, requiring humans to create knowledge. Recent deep language models automatically acquire knowledge from large-scale corpora via pre-training. The stored knowledge has enabled the language models to improve downstream NLP tasks, e.g., answering questions, and writing code and articles. In this paper, we propose an unsupervised method to cast the knowledge contained within language models into KGs. We show that KGs are constructed with a single forward pass of the pre-trained language models (without fine-tuning) over the corpora. We demonstrate the quality of the constructed KGs by comparing to two KGs (Wikidata, TAC KBP) created by humans. Our KGs also provide open factual knowledge that is new in the existing KGs. Our code and KGs will be made publicly available.

Under review as a conference paper at ICLR 2021
LANGUAGE MODELS ARE OPEN KNOWLEDGE GRAPHS
Anonymous authors Paper under double-blind review
ABSTRACT
This paper shows how to construct knowledge graphs (KGs) from pre-trained lan- guage models (e.g.. BERT, GPT-2/3), without human supervision. Popular KGs (e.g, Wikidata, NELL) are built in either supervised or sumervipe manner, requiring humans to create knowledge. Recent deep language models automat- ically acquire knowledge from large-scale corpora via pre-training. The stored knowledge has enabled the language models to improve downstream NLP tasks, e.g., answering questions, and writing code and articles. In this paper, we propose an unsupervised method to cast the knowledge contained within language models into KGs. We show that KGs are constructed with single forward pass of the pre- trained language models (without fine-tuning) over the corpora. We demonstrate the quality of the constructed KGs by comparing KBP) created by humans. Our KGs also provide open factual knowledge that is new in the existing KGs. Our code and KGs will be made publicly available.
two KGs (Wikidata, TAC
INTRODUCTION
Knowledge graphs (KGs) are important resource for both humans and machines. Factual knowl- edge in KGs is injected into Al applications to imitate important skills possessed by humans, e.g., reasoning and understanding. KG construction is mainly supervised, requiring humans handwrite every fact, such as Freebase (Bollacker et al., (008) and Wikidata. KGs can also be constructed in semi-supervised way, in which a semi- automatic extractor is used to obtain the facts from web cor- pora (e-g., NELL (Carlson et al. 20T0) and Knowledge Vault (Dong et al. 2014)). Humans however still need to interact with the extractor to improve the quality of the discovered facts. Therefore, human supervision, which is often expensive, is required in constructing KGs.
Recent progress in language models (LMs), such as BERT (Devlin al. 2018) and GPT-2/3 (Rad- Ford et al. 2019 Brown et al. 2020). has led to superior results even outperforming humans in wide range of tasks, e.g., sentence classification (Wang et al. 2018), question answering (Brown et al., 2020). Pre-trained LMs are also capable to write poctry, music, and code, while such tasks often require we human to spend a significant amount of time in learning the relevant knowledge to work well. In fact, these pre-trained LMs automatically acquire factual knowledge from large-scale corpora (e.g.. BookCorpus (Zhu et al. 2015). Common Crawl (Brown et al., 2020)) via pre-training. The learned knowledge in pre-trained LMs is the key the current success. We therefore consider the following question: instead of using the manually created knowledge, can we use the knowledge stored in pre-trained LMs construct KGs?
In this paper, we design an unsupervised approach called MAMA that successfully recovers the factual knowledge stored in LMs to build KGs from scratch. MAMA constructs a KG with a single forward pass of a pre-trained LM (without fine-tuning) over a textual corpus. As illustrated in Figure MAMA has two stages: Match and Map. Match stage generates a set of candidate facts by matching the facts in the textual corpus with the knowledge in the pre-trained L.M. General or world knowledge from large-scale corpora is embedded in the LM,, thus candidate facts in the target corpus are often covered by the knowledge in the LM. The candidate facts are matched through an efficient beam search in the attention weight matrices of the pre-trained LM without fine-tuning. Map stage produces open KG via mapping the matched candidate facts from Match stage to both fixed KG schema and open schema. If the schema of candidate facts exists in the KG schema, we map the candidate facts directly to the fixed KG schema. Otherwise, reserve the unmapped candidate

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Corpus
Dylan songwriter.
Open Knowledge Graph
Dylan was awarded Nobel Prize.
pylan signed Albert
Match
(Dylan, is, songwriter)
Candidate Facts
Map
Boo, Dylan
Ago
Alber, Groosman CL708584
(Dylan, owarded, Nobel Prize] (Dylan, signed, Albort Grossman
PRCR PIEK
Pre-trained language model
GPT-2
Nobol Pr8e.07191
Figure 1: Overview of the proposed approach MAMA. MAMA constructs an open knowledge graph (KG) with a single forward pass of the pre-trained language model (I.M) (without fine-tuning) over the corpus. Given the input: textual corpus containing passages and sentences, e.g.. English Wikipedia, and pre- are-trained LM, e.g., BERT, GPT-2/3, MAMA (1) generates a set of candidate facts via matching the knowledge in the pre- trained LM with facts
textual corpus, e.g., a candidate fact (Dylan, is, songwriter) from the sentence "Dylan is songwriter", and (2) produces an open KG by mapping the matched candidate facts to both an existing KG sehema, c.g., (Bob. (Bla_ Q392, occupation P106, Songwriter: Q753110) in Wikidata schema, and an open schema, c.g., (Bob. D39,, sign, Albert. Grossman. G0ossman.Q7085844)
facts in the open schema. This results in a new type of KG, open KG, with a mixture of mapped facts in fixed KG schema and unmapped facts in the open schema.
Our contributions are as follows:
1. We show how to construct KGs from pre-trained LMs. The KGs are constructed with single forward pass of the pre-trained LMs without fine-tuning over the textual corpora. This helps researchers explicitly understand what the language models learn, bridging the deep LM and KG communities through enhanced model transparency.
2. We propose an unsupervised two-stage approach, MAMA, to first match the candidate facts in the corpora with the knowledge stored in LMs, then map the matched candidate facts to both fixed and open schema to produce a KG.
3. We generate schema of existing KGs (Wikidata and TAC KBP) annotated by humans; and unmapped facts in the open schema that are in the reference KG schema. The reach of this result is broad and has downstream utility for knowledge graph construction, deep neural network interpretation, and information extraction.
type of KG, namely open KG, consists of mapped in the fixed KG
2 MAMA
We introduce an unsupervised end-to-end approach Match and Map (MAMA) as illustrated in Fig- ure to construct open knowledge graphs (KGs) from language models (LMs). MAMA constructs the KGs with a single forward pass of the pre-trained LMs (without fine-tuning) over the corpora. The two stages of MAMA are:
Match generates a set of candidate facts from textual corpus. LMs contain global or world knowl- edge learned from large-scale corpora, which often does not perfectly match the knowledge in the target corpus. The goal of this stage is to match the knowledge stored in pre-trained LMs with facts in the corpus. Each fact is represented as triplet (head. relation, tail) in short, (h, t), and passed to Map stage. Match procedure is detailed in Sec. 2.1
Map produces an open KG using the matched candidate facts from Match stage. The constructed open KG has two portions: (a) mapped candidate facts that are in fixed KG schema, e.g., (Dylan, is, songwriter) is mapped to (Bob_Dylan Q392, occupation. P106,
according to Wikidata schema; and (b) unmapped candidate facts that are in an open schema, e.g., a can- didate fact (Dylan, signed, Albert Grossman) is partially mapped to (Bob Dylan Q392, sign, Al- bert. Grossman. (708584) in the open schema. This stage is described in Sec. 2.2
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Step
Step 1
Step
Step 3
Dylan songwriter
Dylan
songwriter
Query: Dylan
Intermediate
Matching degrees
Step
Action
x
X
x
X
START
(Dylan,
0.3
X
X
(Dylan, is
0.3
0.1
0.2
X
(Dylan, songwriter
STOP
(Dylan, sangwriter)
0.7
songwriter
0.1
0.4
0.2
x
(a) Matching example.
(b) Attention matrix for matching
degree calculation.
Figure 2: Illustration of Match stage. The upper part of (a) represents the general matching steps of generating the best matched candidate fact (Dylan, is, songwriter) from the sentence "Dylan is songwriter." The lower portion shows the corresponding step-by-step process. Given a hcad-tail pair (Dylan, songwriter), at each step. search chooses one fact. The search starts by adding the head "Dylan" an initial candidate (step O). The matching degree of candidate is initialized as Next, new candidate is yielded
actions, i... START, YIELD, STOP to produce an intermediate candidate
candidate has not reached the "songwriter" (step and step 2), by appending the next largest attended token (with the largest score from the attention matrix (b) the sentence) to end of current candidate, and corresponding matching degrees inereased by the associated attention scores (0.3 and 0.4) to 0.3 (0+0.3) and 0.7 (0.3+0.4) respectively. Otherwise, the search stops, and the candidate fact with the best matching degree returned for the head-tail pair (step 3). The attention matrix (b) is from the forward pass of the LM without fine-tuning the sentence. "x" marks the tokens to prevent searching backward.
2.1 MATCH
We frame the matching procedure search problem. To obtain the best matched candidate facts of an imput sentence, the candidates with the top matching degrees are returned from a search process. The matching degree is derived from the search in the attention weight matrices of the pre-trained LM, since the attention weight matrices are one of the main containers of the knowledge in the pre-trained LM. The attention weight matrices are simply from the forward pass of the LM without fine-tuning over the sentence.
2.1.1 BEAM SEARCH
We design a simple-yet-effective beam search to find the best matched candidate facts. For every head-tail pair (h, t) in sentence, the search maintains the k-best matched candidate facts of the pair. Let's first consider the search from left to right with beam size equals to 1. An example search process is shown in Figure Given head-tail pair (Dylan, songwriter). at each step, the search performs one of the following actions:
START the search from the head. The head added initial candidate into the beam. For simplicity, we use START(h) to denote the action, which returns a candidate (h, In Fig- ure aa),, step the head "Dylan" is added as (Dylan, into the beam. The matching degree is initialized to 0.
YIELD new intermediate candidate in the beam if the current candidate has not reached the tail. The next largest attended token (with the largest score from the attention matrix) is ap- pended the end of the current candidate to yield the new candidate. The corresponding matching degrees are increased by the associated attention scores. At step (orange arrow in Figure aa)). "is" appended to the current candidate to yield (Dylan, is, since "is" has the largest attention score with "Dylan" in the attention matrix. The attention score is 0.3 as highlighted in orange in Figure The matching degree becomes 0.3 (i.c. 0+0.3). The multi-head attention reduced to single head so that every tokens of the sen- tence are associated with one attention weight. We experiment with different reduction
use the "Hal" denote tnllss "entities" "entiry mentinn" for simplicity
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Algorithm Beam search for matching candidate facts.
Input: Hcad-tail pair (h, sentence 8, attention matrix As, action manager O = START, YIELD, STOP),
beam size k
Output: Candidate facts (M..))
1: To.,0) <- (START(h))
Start by adding the head as a candidate in beam
2: while 3c ((,,e)((()) = YIELD] do
3: T(h.t) + 0
b Initialize a new beam
4: for each e T(h.o) do
5: if O(c) YIELD then
6:
T(,, t (m. & A.))
Yield new candidate if not reached the tail Stop then produce valid fact reached the tail
7:
else
TT,, f T(me u (sToP(c,t))
9: end if
10: end for
11: T(p..) (((,A)
Maintain k-best candidates in the beam
12:
end while
13:
return T(k
"x" marks the tokens (prior the current token) that are not consid- ered in the search to prevent searching backward. Step 2 similarly takes YIELD action to produce (Dylan,
setups in Sec. A.3
songwriter, The matching degree is now 0.7 (i.e. 0.3+0.4). We use
YIELD(c, s, A,) to denote the action, where is current candidate, represents the sen- tence, and A, is the attention matrix from the forward pass of the pre-trained LM over s, which yields new candidate.
STOP
the search step if the candidate has reached the tail, then add the candidate as a valid candidate fact into the beam. As beam size equals candidate fact for the given pair. The final matching degree of the candidate is 0.7. We denote this step using STOP (c, t). which returns valid fact.
(Dylan, is, songwriter) is the only returned
The details of the proposed beam search are Algorithm [T] The inputs of the search algorithm are head-tail pair (h, f). a sentence an attention matrix A, of s. Both h and are identified by the noun chunk in s. A, is the attention matrix associated with s from the forward pass of LM without fine-tuning. The search gets started by adding the head / the initial candidate in the beam (line 1). While there are still new candidates waiting to yielded (line 2), the search continues, and the top & candidates sorted by the matching degrees are maintained (line 3-11) 1) in the beam. In practice, we implement an action manager O to decide which action to take at each step. Given a candidate in the beam, (cc) START always happens the beginning of the search. If has not reached the tail / yet, O(c) = YIELD. Otherwise, ((c) = STOP. We convert the subwords to the corresponding full words. We also notice some facts in reverse order in the sentence, e.g., .... said Jason Forcier, a vice president at battery maker A123 Systems Inc." for facts of relation "org:top members employees", thus enable bidirectionality by rumning the algorithm in both direc- tions (left to right and right to left). The beam search is implemented by the treadth-firs search, which is efficient as the time complexity is O(k d), where d is the maximum depth of the search tree.
2.1.2 FILTER
Although the basic functionality provided by beam search is sufficient for finding useful candidate facts, we have found a few constraints useful. Given a candidate fact (h, r, t) from beam search result T(,.), remains
fact if satisfying all the following constraints.
Constraint #1 The matching degree of (h, f) is above a threshold. We compare the matching degrees corpus-wide to only reserve the facts that are matched better with the knowledge in LMs. For example, MAMA extracts fact (Rolling Stone, wrote, pop song) from "Rolling Stone wrote: "No other pop song has so thoroughly challenged artistic conventions"'. "!" which not an accurate fact based on the sentence. We observe the associated matching degree is below proper threshold, while the matching degrees of high-quality facts from the same documents, e.g., (Dylan, is, songwriter), confident facts from the other documents beyond the threshold.
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Constraint #2 The distinct frequency of r is above threshold. To avoid facts to be over-specified, e.g., (Dylan, signed to Sam Peckinpah's 's film, Pat Garrett and Billy the Kid), we require r should take many distinct head-tail pairs in the corpus.
Constraint #3 Relation r is a contiguous sequence in the sentence. We can avoid r that has no meaningful interpretation (Fader et al. 2011), e.g., (Rolling Stone, wrote challenged, conventions) from the above sentence.
2.2 MAP
The objective of Map stage is to generate an open KG. The open KG contains (a) mupped facts in a KG schema (Sec. [.2.1]. e.g., Wikiddta schema. if the schema of the candidate facts is within the existing KG schema; and (b) unmapped facts from (a) in open schema (Sec. 2.2.2)
2.2.1 MAPPED FACTS IN KG SCHEMA
The goal is to map candidate fact (h, r, t)) to fact (hR; Pk, t&) in the KG schema. The reason for mapping to an existing KG schema is make use of the high-quality schema designed by experts (to avoid duplicated efforts of building from scratch) and enable evaluating the candidate facts with oracle KG facts contributed by human volunteers. We first map both entities h, / to hi, lx, then map the relation to r7 in the reference KG schema. Additional details are presented in Sec. Sec. A.
Entity linking KG schema We adapt an unsupervised entity linker based on mention-to-entity dictionary (Spitkovsky & Chang, 2012) to link the entities for scalability consideration. Besides, contextual information is crucial link the entities correctly, we use the word embedding of the context to disambiguate the entities, which means we only link the entities with high contextual similarities based on the word embedding. We adopt the entity linker to map h, to hy; 4x-
Relation mapping with KG schema We largely follow the relation mapping method proposed by Angeli et al. (2055) construct an offline relation map between KG relation and relation phrases of the candidate facts. The basic idea is that the more often linked head-tail pairs (i.e., entities are with type information from the entity linking step) co-occur between the candidate facts and KG facts, the more likely the corresponding relations are mapped to each other. In addition, we normalize each relation phrase of the candidate facts by lemmatization, and removing inflection, auxiliary verbs, adjectives, adverbs. After the relation mapping is constructed, the top 15 relation phrases are true mappings for each KG relation. We only reserve the true ones in the final relation mapping. This process takes approximately one day. Later, is mapped to r&k with an efficient look-up operation in the relation mapping.
author manually checks whether
2.2.2 UNMAPPED FACTS IN OPEN SCHEMA
An unmapped candidate fact (h,, r, 1) means at least one of h, r. and is not mapped to the KG schema based on the method in Sec. 2.2. There are two types of unmapped candidate facts:
Partially unmapped facts represent least one of h, r, and are mapped the KG schema. It can be h or mapped to h or t based on the entity linker in Sec. Sec.2.2.1 It can also be that mapped to rkk using the relation mapping Sec. 2.2.1 This actually results in unmapped facts that are in a mixture of the KG schema and the open schema. As the overall schema of the unmapped facts is not in the KG schema, we use open schema to denote such unmapped facts in the rest of the paper for simplicity. An example is (Dylan, signed, Albert Grossman) in Figure [ where both head and tail are linked to Wikidata schema based on the entity linker in Sec. P.2.T but the relation cannot be mapped since there is no relation mapping from "signed" to KG relation in Wikidata schema.
Completely unmapped facts indicate all h, r, and t are not mapped to the KG schema. This means neither the entity linker nor the relation mapping is able to map h and / to h1, Tk, la respectively. The resulting unmapped candidate facts stay in the open schema, e.g., a candidate fact (Jacob, was, A Registered Mennonite) stays the same in the open schema from sentence "Jacob was registered Mennonite in Amsterdam.". ""
The resulting open KG is a new type of KG that mixes the fixed KG schema with the flexible open schema, suggesting new directions for the next generation of KGs. The open KG not only contains existing knowledge in the reference KGs, but also extends the fixed KG schema with an additional open schema to improve the coverage of the KGs, that benefits the downstream KG based applications, e.g., QA and commonsense reasoning (Wang al. 2019 Brown et al. 2020
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KG TAC KBP
# of oracle facts
# of documents
27.655
3,877,207
Wikidata
27.368,562
6,047.494
Table 1: Dataset statistics of two knowledge graphs: TAC KBP and Wikidata. TAC KBP refers TAC KBP Slot Filling 2013 challenge. of oracle facts for TAC KBP is the number of oracle facts in the 2013 task. of documents for TAC KBP is the number of the documents in the 2013 task. # of oracle facts for Wikidata is the total number of oracle facts in Wikidata. # documents for Wikidata is the size of English Wikipedia.
EXPERIMENTS
How well can language models generate knowledge graphs? We experimentally explore how well can MAMA answer the question in the section. To measure the ability of LMs in generating KGs, we directly measure the quality of resulting open KGs. The open KG contains two types of facts: mapped facts in the fixed KG schema; and unmapped facts in the open schema. We first quanti- tatively evaluate MAMA by comparing the mapped facts to oracle KGs annotated by humans in Sec. 3.1 then conduct an in-depth analysis of the unmapped facts in Sec. Sec.3.2
3.1 RESULTS ON MAPPED FACTS
We first study the quality of the mapped facts. As the candidate facts have been mapped to the schema of oracle KGs, we are able quantitively compare the candidate facts with the oracle facts in the reference KGs.
3.1.1 DATASETS
We compare the mapped facts from MAMA with the facts in two KGs:
TAC KBP TAC Knowledge Base Population (KBP) Slot Filling task search a document collection to fill in the tail/object entity for predefined relations (slots) for a given head/subject entity in a reference KG. We experiment with the reference KG in the 2013 challenge. We use the document collection and oracle facts of the 2013 task. The statistic of the dataset is shown in Table
Wikidata We use popular Wikidata as another KG. We use all the oracle facts in Wikidata. We use the English Wikipedia as the text corpus, since large amount of facts in Wikidata is from English Wikipedia. The statistic is in Table
To evaluate the mapped facts, we first use Match stage of MAMA to run over the corresponding documents generate the candidate facts. Then Map stage is leveraged to map the candidate facts to the schema of TAC KBP and Wikidata respectively. The parameter settings, such as beam size in Algorithm are shared across TAC KBP and Wikidata based on the parameter study in Sec. A.3
3.1.2 TAC KBP
To verify the ability to produce correct facts, we compare candidate facts from MAMA to the outputs of two open information systems, which also produce triplets in the form of (h, r, (). After collecting the triplets from the corresponding system, we use the same Map procedure with MAMA (Sec.) 2.2.1l
[Scc.2.2.1
to map the triplets to the corresponding KG schema.
Stanford OpenIE leverages POS tag and dependency parser, and generates self-contained clauses from long sentences to extract the triplets, which is the best open information extraction system (An- geli et al. 2015) on TAC KBP (Surdeanu) 2013).
OpenIE 5.1 is one of the state-of-the-art open information extraction systems, which is the suc- cessor to Ollie (Schmitz al., 20022). and it improves extractions from noun relations, numerical sentences, and conjunctive sentences depending on the linguistic patterns.
We use two families of pre-trained LMs with MAMA. We use BERT BASE and BERTLARGE from Devlin et al. (2018) with MAMA, namely MAMA-BERT BASE and MAMA-BERT LAGE- Be- sides, GPT-2s from Radford et al. (209)) are used, i.e., MAMA-GPT-2, -PMA-GPT-2 MAMA-GPT--2ARRCC and MAMA-GPT-22XL
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Method
AParams of LM
Precision %
Recall %
F1%
OpenlE
56,98
14.54
23.16
Stanford OpenIE: Angeli al 2015 2015
61.55
17.35
27.07
HAMM-BFRTBBAS. BASE (mi6s))
18.79
28.79
MAMA BERT (ours)
335M
61.69
18.99
29.05
MxMA-GPP-2 (ouns)
61.62
18.17
28.07
MAMA 2MRDMM (ours)
345M
62.10
18.65
28.69
CAAMA-CPP-22.nmmm (ours)
774M
62.38
29.12
MAM4-GPP-22xi (ours)
1558M
62.69
19:47
29.72 (+2.65)
Table 2: Compare the quality of mapped facts on TAC KBP, #Params LM refers to the number of parameters of the pre-trained L.M.
Table 2 shows the results TAC KBP. We use the official scorer of TAC KBP Slot Filling 2013 to evaluate precision, recall, and F1 on TAC K
MAMA constructs improved KGs compared to open IE. From the results, we find that all our methods achieve competitive precision, which greater than 60%, given the unsupervised nature of MAMA. All the proposed methods outperform the two open IE systems. This shows that MAMA able to produce high-quality knowledge directly from pre-trained LMs by a single forward pass without human supervision. The results show the effectivvness of MAMA in generating candidate facts from Match stage, and producing high-quality KGs through Map stage. We also find that MAMA-GPT-22L. performs the best. MAMA-GPT-2xx. outperforms the previous state-of-the-an Stanford OpenlE: by over 2.6% in F1. This shows the proposed end-to-end MAMA is able to recover the knowledge stored in pre-trained LMs without relying on any extra linguistic features, such as POS tag and dependency parser used in open IE systems. The main reason leading to the moderate results of OpenIE 5.1 is that the system generates objects of the triplets with extraneous words, which hurt the performance in slot filling tasks. Even though the proposed methods all outperform the two open IE systems in the recall, however improving recall is clearly the future direction to further improve the performance of MAMA. We find that the main cause of the moderate recall is the incorrect entities caused by spaCy noun chunk as summarized in Sec. A.2
Larger/deeper LMs produce KGs of higher quality. BERTLARGE outperforms BERT since the doubling parameter size. GPT-2s share similar trends, where we observe performance increases when the model size increases. This complies with our intuition on more knowledge is stored in deeper and larger models. Such increases in performance seem subtle on TAC KBP, we find this might due to the relatively small number of oracle facts by noticing more significant improvement on Wikidata in Sec. 3.1.3 We plan to further improve the results with larger pre-trained LMs, e.g.,
GPT-3 (Brown et al. (020), Megatron-LM (Shoeybi et al. 2019).
BERT LMs outperform GPTT2 LMs under similar model sizes. More specifically, BERT BASE performs better than MAMA-GPT-2 in F1, and MAMA-BERT, BERT outperforms MAMA-GPT: MEEDIUM in F1. BERTBASE and MAMA-GPT- are similar in size, while MAMA-BERTI, BERTLARGE and MAMA-GPT-22 AA-GPT-2MMEDUM are similar in model size as well. This is mainly because that the recall of BERT LMS is higher than that of corresponding GPT-2 LMs. The result indicates that the Cloze-style loss function (i.e., masked language model) of BERT is more effective and flexible in recovering more knowledge than the autoregressive LM objective. We also notice that the precision of GPT-2 LMs is higher than that of according BERT LMs. The reason is that the autoregressive LM objective captures more accurate knowledge than Cloze-style loss does by not introducing extra noise (e.g., masked tokens) during pre-training.
3.1.3 WIKIDATA
We select our best BERT based method NAMA-BERTTANEE LARGE, and GPT-2 based method MAMA- GPT-2x1. on TAC KBP to compare with Stanford OpenIE (the best open IE system TAC KBP) for scalability experiments on Wikidata. We follow the same definition as the slot filling task to calculate precision, recall, and Fl on Wikidata. Table summarizes the results.
MAMA is scalable to larger corpora. Similar to the trends on TAC KBP, MAMA-GPT-2XI per- forms the best in precision, recall, and F1. The results show the effectiveness of MAMA in gen- erating candidate facts and high-quality KGs. We also find that MAMA-GPT-2X1. outperforms MAMA-BERTTARGET by over 1% in F1. This shows that the larger model (GPTT-Xx1, has 5x more
There are 2.38% cornect ocacle Bets beaed the "nanesl runs" assesoment in TAC

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Method Stanford Openle Angeli at. 2015]
#Params of L.M
Precision %
Recall%
F1%
23.32
13.09
16.77
M..MA-BRRTAAnD (ours)
333M 1558M
29.32
16.36
21.22
MAMA-GPT-2xL (suns)
31.32
17.42
22.39 (+5.62)
Table 3: Compare the quality of mapped facts on Wikidata, #Params LM refers to the number of parameters of the pre-trained LMM
parameters compared to LERTLARGE) contains more knowledge, and MAMA is able to restore the knowledge. When larger or deeper models (e.g., GPT-3) are used with MAMA, we can expect more gains of the KG quality. Thanks to the efficient nature of MAMA, which relies only on the forward pass of the LMs without fine-tuning, the results suggest that MAMA is scalable large KGs.
Larger corpora embed more complete KGs. In particular, MAMA-GPT-2XL outperforms Stan- ford OpenlE by 5.6% in F1. MAMA-BERT LARGE outperforms Stanford OpenlE by approximately 4.4% in F1. Both F1 gains are larger compared to that on TAC KBP. This is because that the LMs contain world knowledge from pre-training corpora, e.g. Wikipedia and Common Crawl. The larger the textual corpora are, the more knowledge our method is able to recover and match to the knowl- edge stored in LMs. The finding is particularly important, since we are now able to construct larger KGs of high quality from scratch when larger datasets are used, such as WebText2 and Common Crawl (Raffel et al., 2019; Brown et al. 2020). Similar to the observations on TAC KBP, the preci- sion is higher compared to recall. Wikidata is not fully built from Wikipedia, MAMA could improve the recall by running on those larger corpora to collect more facts.
3.2 ANALYSIS OF UNMAPPED FACTS
The open KG constructed by MAMA is new type of KG combining the fixed KG schema with the flexible open schema. We turn to study the quality of the candidate facts that are not mapped to the above reference KG schema, but in the open schema generated by MAMA. We manually judge such unmapped facts generated by our best method MAMA-GPT-2xL from 100 sampled documents in Wikidata and TAC KBP respectively.
The quality of unmapped facts is verified by human annotators. We find 35.3% of the unmapped facts are true Wikidata. We find 83.2% of those true facts partially unmapped facts as defined in Sec. 2.2.2 e.g., (Bob Dylan. Q392, tour with, the Grateful Dead. Q212533), whose relation is not within the schema of Wikidata, while both head and tail are in the schema. The remaining true facts are completely unmapped facts (Sec. 2.2.2). c.g., candidate fact (Jacob, was, A Registered Mennonite) stays the same in the open schema.
Accurate entity detection is desired. We also notice 45.5% of the untrue unmapped facts on Wikidata are due the incorrect entities detected by the spaCy. Incorrect missing entity linking (to either head or tail) in Sec. [2.2.]] causes additional 9.1 % errors in the unmapped facts. 4.5% of the untrue unmapped facts are caused by the missing relation mapping in Sec. 2.2.1 The rest erross made by MAMA-GPT-2XI, are incorrect relation phrases, such as uninformative relation phrases, e.g., (Dylan, made, his breakthrough) which similar to the errors made by open IE systems (Fader et al. 20111. Both entity linking and relation mapping of Map stage rely heavily on the accuracy of entity detection from the spaCy noun chunk. We conclude that the main root cause of the untrue unmapped facts is due to the errors made by the spaCy noun chunk.
We observe similar trends on TAC KBP. We plan to leverage crowdsourcing platforms, e.g., Me- chanical Turk, to conduct quantitative evaluations over the unmapped facts to better understand the strengths and shortage of MAMA. We plan to identify more accurate entities by relying on attention weights in LMs (Clark et al., 2019 Hewitt & Manning 2019) instead of using extra resources. We will also investigate stronger entity linkers (Kolitsas et al. 2018) and learn a more robust relation mapping through weak or distant supervision (Mintz et al. 2009 Ratner et al. 2017). We will in- vestigate more sophisticated approaches, such as graph neural networks (Kipf & Welling (016)), to generate more accurate relation phrases from the attention weight matrices by considering structural information.
4 RELATED WORK
Knowledge graph construction can be generally categorized into two groups. 1) supervised ap- proaches. Wikidata, Freebase (Bollacker et al. 2008), YAGO (Suchanek et al. 2007). YAGO2 (Hof-
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fart et al. 2013), DBpedia (Auer al.] 2007) are built based human supervision from Wikipedia infoboxes and other structured data sources; 2) semi-supervised approaches. Open information ex- traction systems, e.g.. OLLIE (Schmitz et al. 2012), Reverb (Fader et al., 2011). Stanford Ope- nIE (Angeli et al.] 2015). and OpenlE: 5.1 aim to leverage carefully-dsiineed patterns based on linguistie features (e.g.. dependencies and POS tags), extract triplets from weh corpora for open schema KG. Besides, NELL (Carlson et al. 2010). DeepDive (Niu et al. 2012), Knowledge Vault (Dong al. 2 extract information based on a fixed schema or ontology, where humans help improve the accuracy of the extractions. Probase (Wu et al. 2012) produces taxonomies instead of rich typed relations in general KGs. MAMA instead uses learned knowledge stored in pre-traines
LMs without human supervision to construct an open KG, which is a mixture of fixed schema and open schema. Different from commonsense knowledge construction using Transformers (Davison er al. 2019 Bosselut et al. 2019), the proposed method is unsupervised and end-to-end, and con- structs general-purpose KGs instead of commonsense knowledge.
Language models, c.g., BERT (Devlin et al. 2018), GPT Radffrd et al.
2018 GPT-2/3 (Radford
et al. 2019: Brown et al.
2020), ELMo (Peters et al. 2018), Transtormer-X XL DDi et al. 2019).
ALBERT (Lan et al. 2019). RoBERTa (Liu et al. 2019), XLNet (Yang al. 2019) and Megatron-
LM (Shoeybi et al. 2019) contain factual knowledge obtained via via peeppretrame pre-trainig on large-scale corpora such Wikipedia and BookCorpus (Zhu al. 2015). Studies have leveraged the pre-trained LMs as virtual KGs, and show reasonable performance in QA tasks (Dhingra et al.) 20200 Guu et al.] 2020)) and language modeling (Khandelwal et al. 2019). LMs are further enhanced by KGs (Peters et al., 2019) to improve knowledge-driven tasks. While the existing work utilizes knowledge in an implicit way, the main difference is that our approach explicitly extracts knowledgeable facts from the LMs.
Compare to the joint training with knowledge base to improve shallow word embedding (Wang al. 2014), we show that the knowledge is already stored in the deep LMs. We plan incorporate domain knowledge into language models to construct domain-speciee
KGs. The main difference
between LAMA (Petroni et al. 2019; 2020) and MAMA is mainly two-fold: (1) LAMA aims to complete Cloze-style statement, e.g., given "Dylan is -" LAMA predicts which words/phrases should fill the blank " which has no direct connection to KGs. There are several fundamental limitations when adapting LAMA construct KGs, e.g., additional queries must be constructed first, and the answers for the queries must be linked to KGs. MAMA aims to solve a reasoning problem, e.g., given a passage, MAMA directly matches the fact in the form of triplet (Dylan, is, songwriter) at the first step, then maps the fact to produce KG. (2) The benchmark datasets used with MAMA are larger compared the LAMA benchmark, e.g., Wikidata is 3 orders of magnitude larger compared to the largest dataset in the LAMA benchmark.
Neural network interpretation here specifically refers to pre-trained deep language model analysis. There has been lot of work to understand what the neural networks Iearn (Linzen et al. [0016] Adi et al. 2016 Tenney et al. 2019). With regards to analyzing Transformer (Vaswani al. (017)) based language models (e.g.. BERT and GPT-3), substantial recent work focuses on both visualizing and analyzing the attention (Vig 2019 JJain & Wallace, Clak
Clark et al., 2019 Michel et al. 2019 Vig et al. 2020 Ramsauer et al. 22200] Hendrycks et al. 2020). Instead of analyzing or visualizing, we
use LMs to generate structured KGs to directly recover what LMs learn from the corpora.
CONCLUSION
We show that the knowledge graphs can be constructed by single forward pass of the language models over textual corpora. We propose a two-stage unsupervised approach MAMA to first match the facts in the corpus with the internal knowledge of the language model, and then map the matched facts to produce a knowledge graph. We demonstrate the quality of the resultant open knowledge graphs by comparing to two knowledge graphs (Wikidata and TAC KBP). The open knowledge graph also features new facts in the open schema, which could have broad implications for knowl- edge graphs and their downstream applications. The results also suggest that larger language models store richer knowledge than existing knowledge graphs, and generating on even larger high-quality text corpora could continue improving knowledge graphs. Additionally, the knowledge graphs gen- erated by our approach can help researchers to look into what the language models learn, interpretable knowledge graphs establish a bridge between the deep learning and knowledge graph communities.

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A ADDITIONAL DETAILS AND ANALYSIS OF MAMA
A.1 METHOD DETAILS
Map stage details To evaluate the mapped facts, we first use Match stage of MAMA to run over the corresponding documents to generate the candidate facts. For Map stage on TAC KBP, we link to the oracle annotation of the entities or spans in the TAC KBP corpus. On Wikidata, the entity linking method described in Sec. Sec. 2.2.T first leveraged to link entities in the candidate facts to Wikipedia anchors. We build an enhanced vention-to-entti dictionary based on Spitkovsky & Chang (2012). In particular, we add new Wikipedia anchors to the dictionary which results in 26 million entries comparing to 21 million entries in (Spitkovsky & Changl (2012). Then a Wikipedia anchor to the Wikidata item dictionary constructed and used further link the entities to Wikidata. If the head or tail is a pronoun, we further use neuralcoref[] for coreference resolution. We use GloVe (Pen- nington et al. 2014) embedding for disambiguation. The relation mapping is constructed offline for TAC KBP and Wikidata respectively using the method in Sec. 1.2.1 Besides the automatic relation mapping method proposed in Angeli et al. (2015), we manually check whether the top relation phrases are true as described in Sec. 2.2.1 For relation mapping, we randomly sampled a hold-out dataset including 2,000 documents from the TAC KBP corpus and English Wikipedia for the rela- tion mapping construction TAC KBP and Wikidata respectively. For oracle facts in Wikidata, we only preserve those facts describing relations between entities that could be linked to corresponding Wikipedia anchors. We rule out facts of attributes about entities and facts of auxiliary relations (such as topic's 's main P901) and finally results in 27,368,561 6,2 oracle facts.
Implementation details For Wikidata, at Match stage, we randomly split the English Wikipedia data into 20 partitions, and map the data partitions to 20 distributed servers to run. Each server is configured with four Tesla K80 12Gs. We set the max sequence length to 256, and batch size as 32 for MAMA-BERT LARGE and 4 for MAMA-GPT--2XL. We use implementations of pre-trained LMs in Transformers package We use spaCy sentencizer 6 to segment the documents into sentences. MAMA-BERT LARGE takes approximately 48 hours, and MAMA-GPT-2xI. costs around 96 hours. The resulting candidate facts of Match stage from the 20 servers are then reduced a data server, where a MongoDB database is maintained store the oracle Wikidata and entity linking results to enable the efficient Map stage. To produce the open KGs, Map stage takes around 18 hours. The setup is similar TAC KBP. Match stage is done within 48 hours for all the settings. The batch sizes of MAMA-BERTBAS.. MAMA-GPT-2, MAMA-GPT-2WEDDIUSS MAMA-GPT-21ARGH are 64, 32, 16, 8 respectively.
Parameter settings The parameter settings are shared across TAC KBP and Wikidata. All the choices are based on the parameter study in Sec. A.3 The beam size of Algorithm [ is set to 6. The matching degree threshold of Constraint #1 (Sec. 2.1.2) is set to 0.005, and the number of distinct head-tail pairs of Constraint #2 (Sec. 2.1 is set to 10. To generate the attention weight matrix A, of a sentence, we reduce the weights of every attention head in the last layer of pre-trained LMs using the mean operator.
A.2 ERROR ANALYSIS
There is still significant room to improve MAMA. To further understand the shortage of MAMA, conduct error analysis of the errors in precision (i.e., incorrect facts returned by MAMA) of Table and Table 3 We choose our best method MAMA-GPT-2xL. for the study. We sample 100 documents from the Wikidata dataset, and manually check the reasons for the errors. We find 33.1% of the errors are caused by incorrect entities, while the relation phrases are correct. The errors are due to the incorrect noun chunk detected by the spaCy [ 18.3% of the errors are due to the missing relation mapping created in Sec. 2.2.1 Note that we find approximately 23.8% of the errors are actually correct facts that are new in the reference KGs. e.g., (Bob Dylan. Q392, residence. P551, Nashville, Q23197) (in Figure 16) is not an existing fact in Wikidata, but it is correct mapped fact based on our annotation. The rest errors made by MAMA-GPT-2XL are incorrect relation phrases,
https:/ http:://giihhoo..m////nnnnnnneee
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Festuree/4 hawpe//noun--huee
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(d) Attention weights layers.
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Figure 3: Parameter study with MAMA-BERT TAC KBP hold-out subset.
such as uninformative relation phrases. We find similar errors are made by MAMA- MAMA-GPT-2xL. on TAC KBP. Similar to Sec. 3.2 enhancing the entity detection, entity linker, relation mapping, and relation generation are helpful. We also plan to leverage lifelong learning (Carlson et al., 2010) to add true facts to the reference KGs to improve the evaluation.
A.3 PARAMETER STUDY
We study the effects of the parameters using MAMA-BERT BASE on TAC KBP. We randomly sample 20% of the oracle query entities as a hold-out dataset tune the parameters, and use the best parameter setting achieved for both TAC KBP and Wikidata experiments. When studying the effect of certain parameter, we keep the remaining parameters as default described in Sec. A.I We use
Fl to measure the effects.
Effects of beam size Figure illustrates the effects of various beam sizes in Algorithm 1 We find that in general, the larger the beam size is, the better Fl the setting achieves. This is because that MAMA able to reserve more potentially correct facts when more candidates are allowed in the Match stage. However, Fl improvement gradually becomes subtle, while the computation costs increase more significantly. For sake of the efficiency, we do not explore larger beam sizes. We set the beam size as 6.
Effects of search constraints Figure 3 3(b) compares the effect different thresholds of the match- ing degree of Constraint #1 in Sec. 2.2 We set the threshold 0.005 since achieves the best result. Note that the summed attention score normalized by the length the fact to penalize the cumbersome facts. The matching degree threshold is effective, which is mainly because of the knowledge contained in the self-attention matrix. The score in the attention matrix is representing the chance of the facts be the true facts based the stored knowledge. Figure (3c) shows the impact of the number of distinct head-tail pairs in identifying common relations of Constraint #2 Sec. 2.1.2 The best result is achieved when it equals 10. This shows that while MAMA mostly identifies frequent relations, it is also able to capture some rare relations for the open schema.
Effects of attention weights Figure 3(d) shows the comparison between attention weights of the last layer and the mean of all layers. The attention weights of the last layer perform better. This is due to the attention weights lower layers are low-level linguistic knowledge according to (Clark) al. 2019: Ramsauer et al. 2020), which are less relevant to the factual knowledge for the KG construction. Figure ((e) compares the impact of different attention reduction, i.e., mean, max, over the attention heads of the last layer. We find the "mean" perform better. The reason is that the token often intensively attends to several specific tokens in the sequence (Michel et al. 2019), and the "mean" operator is more sensitive such information.
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B SAMPLES FROM MAMA ON TAC KBP
B. MAPPED FACTS
We randomly sample 100 documents from TAC KBP corpus, then randomly sample sentences from those documents. The uncurated candidate facts and the corresponding mapped facts of the sam- pled sentences based on our best methods We also randomly sample several sentences in which MAMA-
and MAMA-GPT-2XL are shown
in Figure and Figure]Jrespectivel BERTLARGE differs from MAMA-GPT-2xI. in the resulting facts for comparison, which illus- trated in Figure In each table, "ID" represents the document IID of sampled sentence in TAC KBP corpus. "Sentence" indicates the sampled sentence. "Candidate facts to mapped facts" col- umn contains the candidate facts (on the left side of "-") and their corresponding mapped facts (on the right side of "->)..
B.2 UNMAPPED FACTS
We randomly sample 100 documents from TAC KBP corpus. From those documents, we show unmapped facts from the sampled sentences from those documents. We manually check the correct- ness of the unmapped facts according to Sec. 3.2 and show the correct ones. The original candi- date facts with the corresponding unmapped facts the sampled sentences generated by MAMA- BERT and MAMA-GPT-2xI. are shown in Figure 7 and Figure g A further comparison of the unmapped candidate facts is illustrated in Figure 9 In each table, "ID" represents the document ID of a sampled sentence in TAC KBP corpus. "Sentence" indicates the sampled sentence. "Can- didate facts to unmapped facts" column contains the candidate facts (on the left side of "--") and their corresponding unmapped facts (on the right side of "-"").
C SAMPLES FROM MAMA ON WIKIDATA
C.I MAPPED FACTS
Similar to TAC KBP, we randomly sample 100 documents from the Wikidata corpus (i.e., English Wikipedia), then randomly sample sentences from those documents. The uncurated candidate facts and the corresponding mapped facts of the sampled sentences based our best methods MAMA- BERTLARGE and MAMA-GPT-2XL are shown in Figure I0 and Figure I1 respectivvey. We also randomly sample several sentences which MAMA-BERS BERTLARGE differs from MAMA-GPT-2xLL in the resulting facts for comparison, which is illustrated in Figure 12 In each table, "ID" repre- sents the Wikipedia page's title of a sampled sentence. "Sentence" indicates the sampled sentence. "Candidate facts to mapped facts" column contains the candidate facts (on the left side of "->") and their corresponding mapped facts (on the right side of "-").
C.2 UNMAPPED FACTS
Similar to TAC KBP, we randomly sample 100 documents from the Wikidata corpus. From those documents, we show unmapped facts from several sampled sentences from those documents. We manually check the correctness the unmapped facts according to Sec. 3.2 and show the correct ones. The original candidate facts with the corresponding unmapped facts of the sampled sentences generated by MAMA-BERTTLARGE and MAMA-GPT-2XX. are shown in Figure 13 and Figure 14 A further comparison of the unmapped candidate facts is illustrated in Figure 15, In each table, "ID" represents the Wikipedia page's 's title of a sampled sentence. "Sentence" indicates the sampled sentence, "Candidate facts unmapped f'acts" column contains the candidate facts (on the left side of -") and their corresponding unmapped facts (on the right side of "-").
D ADDITIONAL OPEN KG SUBGRAPHS FROM MAMA ON WIKIDATA
We sample several documents from the Wikidata corpus. We visualize the mapped facts and un- mapped facts from those documents as examples of subgraphs in the resulting open KGs. We show the snapshots of the subgraphs generated by MAMA-BERT LARGE from Figure Figure 24 We
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siglt (i))
Figure 4: Mapped facts: MAMA-BERTLARCE LARGE on TAC KBP.
similarly illustrate the snapshots of the subgraphs constructed by MAMA-GPT-2xL. from Figure 25 to Figure 32 In each figure, the blue node and arrow represent the mapped facts in the Wikidata schema, while the yellow node and arrow denote the unmapped facts in the open schema. We addi- tionally visualize the correct facts that are new in Wikidata according to Sec. A.2 in yellow.
16

---

Under review as a conference paper at ICLR 2021
W11300
W11360
Thi
$1000100
Figure 5: Mapped facts: MAMA-GPT-2xX, on TAC KBP.
17

---

Under review as a conference paper at ICLR 2021
WIUNG
mo Jacien
the Dugdrss Store perk Silay
jet ancoplemnauae Lotile Poslo
Oer on Survess habos lick Contiglery (eew Daniilors partar
En (hetroadd
Hoe Mo Bebiil oe Dostbats parc outt Now holedir
The Grglle
Devonio Hende
prodlcc Vodte du eneay)
pe pecigemmzaer
fed Mult
if dnd aejanet tice
duuo ados Al2l lpaker
porfic food wat lndrrr mit indo sleaty
Tradkc to Woll by PCC Taal aigaa shal
Tralis Dalld 10 Be 2C 1ola Tink'ss
Figure 6: Mapped facts: MAMA-BERTIARGE vs. MAMA-GPT-2XL, on TAC KBP.
18

---

Under review as a conference paper at ICLR 2021
SIG ANG
Aagaal for 100 183. Capa
SID AND
CLASS GiAsods Aotiia FRiSts FaaREAN 1et of
1l Calok Uia Dated Foad
cootie drow Sodii's leaes s161 pe www.ankeaaaaaoooooo feads
Figure 7: Unmapped facts: MAMA BERTLARGE on TAC KBP.
3707
An them
Haro Fou In yar poa
vololrane
Werre Tead ceke
(eve ai Bech Wea
per
dt;
Figure 8: Unmapped facts: MAMA-GPT-2XL on TAC KBP.
19

---

Under review as a conference paper at ICLR 2021
you boon
Dole Doldo
coal tasle ane of
to Bark Ban be 1991 wlso sig Juita
th. The bask Sear Moker Colk pwoc procanalankeaa Dh
The Yot Bui Tea (Milanedd Oulis
the Moait Chogle duitiat fitees
Feom Vis
de sted
He Sacr. Bi prodling Daar
wel whow Col. oolatoc fir Sybedd droucdd
Home Prix, eran tleee Putx porlfe
the Cle Asars Boome Diocii,, de liggge
Fils she Noww Car Yoan Soary Uompln Blog
SIAAINGA CLAIVEC Chalbi HOMEER RERAIE 1CS At layt
(Cank Outek, Roast froes llnden - Kitual Oltel pettil
wilh The The
Vanit
PRaic Flans Tarryc Klaao Spat
Now
Kades bosel
Kass parka
Simlty Prodent irae
Figure 9: Unmapped facts: MAMA-BERT, LERTLARGE vs. MAMA-GPT-2xL, on TAC KBP.
20

---

Under review as a conference paper at ICLR 2021
pungi pii
Thlin raoggpoo get yuur sagoes gamor
Tor To
b.
and Hodes doet
Thl
Figure 10: Mapped facts: MAMA-BERT on Wikidata.
21

---

Under review as a conference paper at ICLR 2021
but bradted
Larn and or dh pian lewp Mee gugile yolto.
Tilm
They wa & Chacke Intiubed Heley
We Curl Donion wottin ches
Tol
Figure 11: Mapped facts: MAMA-GPT-2XL on Wikidata.
22

---

Under review as a conference paper at ICLR 2021
gerallty
Thee
Tagiay gantt
and aenaalll comnty (on))
Time that
Figure 12: Mapped facts: MERT-BERT vs. MAMA-GPT-2XL, on Wikidata.
23

---

Under review as a conference paper at ICLR 2021
Callay (NI
dle Vede Oreel be of Rodd Calta
De wit aomrcl
To oo Aaym
So Too' tor leck Vigin (ilg) p
Kralosate quila
Ie tew 12 cadia 1ik 20 BAIINN alen lak wapmer
Body pncild
Figure 13: Unmapped facts: MAMA-BERTLARGE on Wikidata.
Blat sugund bos
2010 exbret uncee Lebeetcitonn
bade Se Di aucicioo
Ho
Obeillund lud bey daliccal
of
10040))
bode L8 aala de (0
Talle Doyas
Buter faler woted
dert
TA DO
Sol
antulle ue foell Aerl Il but Better
Otbebi the doaadd 018
Lie
He Cir
Figure 14: Unmapped facts: MAMA-GPT-2x., on Wikidata.
24

---

Under review as a conference paper at ICLR 2021
Crrda Alac
Conoieaatooi bey ber mocgh aBoood
Dopan''s dne lowne and Tackes aut
del
lor lo loom
The Weriiim Til Carc www.oaalaaiiiii..... SAS topponlicc
of
Figure 15: Unmapped facts: MAMA BERTLLANE MAMA-GPT-2xX on Wikidata.
Th: Jone D000
gripel
Rock, 2ara
ColunEaL Paacin 0183337
Popac Peodeck CPP20112
ciego Gegg-s6
Acteta Unieet Jgganes Oo
Thonas Veepi Awort 0000690
epond mytine 2106
Tor_Phaty the, www.tatttteeeeoooooooo
JOHLL COCOOO zwws_oowiinitee
Sae. Su00 C062
Hameo Viau 001553
Grsves Lifdre Atheowara Award
HI of St
Dulnh D485704
Ta. Pa_Sooee Danet G212333
Versepxts
(Nor, MMNRC Pres SPP4511
Chyoec
Figure 16: A snapshot subgraph of the open KG generated by MAMA using BERT LARGE from Wikipedia pages neighboring "Bob Dylan". The blue node and arrow represent the mapped facts in the Wikidata schema, while the yellow node and arrow denote the unmapped facts in the open schema. We also visualize the correct facts that new Wikidata in yellow.
25

---

Under review as a conference paper at ICLR 2021
Harequa www.eaaaac.....ooo 0.911-99355
Group Capaan Group_Capttn
renderfce
Royil AIL Force 0166802
Figure 17: A snapshot subgraph of the open KG generated by MAMA-BERTLARGE from the Wikipedia page "Douglas Bader"".
cxuntry otpenampp.P7
Helen Storew GEPN02Z24
Q6136765
Figure 18: A snapshot subgraph of the open KG generated by DAMMA-BETTARREE from the Wikipedia page "Helen Storrow".
Netherlsndes
pla deatty
Haariem OPNZU
Jacob, Van, Paliccasl Q213612
gerri.P130
Landsoape, Painding G191168
Amstercum 0727
Figure 19: snapshot subgraph of the open KG generated by MAMA-BERT, ARGE from the Wikipedia page "Jacob van "uisdael".
26

---

Under review as a conference paper at ICLR 2021
Vyeoamd miiile GTETEE
Pirt AB Kopnes G87/2899
Centarige 122
anotensua 20
Unftr Keg.hrrrra
Dkommbuury
Lieol P-0000000
2300
Figure 20: A snapshot subgraph of the open KG generated by MAMA BERTLARGE from the Wikipedia page "John Maynard Meynerd.Keenes....
Eotaar Collage, OW11S
Brish AA101200M
PrekatanCC45
edacita Pab
Jadin Mugian Long Gezcont nember of polsci
Liscust Aiinn G225213
plos
oocupetton 212
Morse C-Caudd G25P1405
Dt O735575
Figure 21: A snapshot subgraph of the open KG generated by MAMA BERTLARGE from the Wikipedia page "Liaquat Khan".
Mele Gelcel
Palk Par A.C.C Mry
Garger
Www.DrouP..oo F0.088/54
Uniel
Figure 22: A snapshot subgraph of the open KG generated by LARGE from the Wikipedia page "Neville Southal..
27

---

Under review as a conference paper at ICLR 2021
Carho www.k...1.1.....
Englih Of 850
Nagdalen, Collepe, Onlers-ee1
edk MI
laragga Joches pelinn siggmal F1412
Padlin dayraa 12101951
formad the arbiuen beceming conputton PID6
Slacke Of Ac003300230
Figure 23: A snapshot subgraph of the open KG generated by MAMA-BERTI from the Wikipedia page "Paulinc_Baynese
University Oelo. Q486156
Atheist
WAS
educatas .t.
Heyerdall Q183622
countr, of pttrenehip.po..m P17
place Keath F20
Puria
Norway C20
Colla, Michert.Q044777
Figure 24: A snapshot subgraph of the open KG generated by MAMA-BERTLANGE from the Wikipedia page "Thor_ Heyerrahyy.
Flying Ark G222812
S weweeeeeeOOrreeeeeeeeeeeee Q816838
Fores
Dougies akcilet 2,999
rember sports Iaam Edoard's School,
Hareegtr F.C. 01146630
Figure 25: snapshot subgraph of the open KG generated by MAMA-GPT-2xL. from the Wikipedia page "Douglas Bader".
28

---

Under review as a conference paper at ICLR 2021
2ttrnshipp.22
United States G30 sithh.h.19999
Helen
_elen_Storrow Storrow Q5703224
apoyse
Storrow.0613877
Figure 26: A snapshot subgraph of the open KG generated MAMA-GPT-2xL. from the Wikipedia page "toroo.
ploce a 2a Pas
Landecape, Painting ora1ge
0213312
middenc mideencap Agpatirdam D727
country of c.2.
beack, Ven Rasaaiied O8077456
Natherlenda Cab
Figure 27: A snapshot subgraph of the open KG generated by MAMA-GPT-2XL from the Wikipedia page "Jacob van Ruisdael".
Mes burutry denal Caraion
Aeoeen Si Fanii Sclee 07530656
Julo Maralt bayaic
Livill Marly Moll 0402641
Plane Aull Kour CONTrIIA
Vycaanial Intictio D12
Figure 28: A snapshot subgraph of the open KG generated by MAMA-GPT-2XL. from the Wikipedia page "John,Maynnn Maaahmaaaaarr_KKeeneess
29

---

Under review as a conference paper at ICLR 2021
Figure 29: A snapshot subgraph of the open KG generated by MAMA-GPT-2XL from the Wikipedia page "Liaquat Khan""
Figure 30: snapshot subgraph open KG generated by MAMA-GPT-2XL from the Wikipedia page "Nevill_SSutthall
Figure 31: A snapshot subgraph of the open KG generated by MAMA-GPT-2xL. from the Wikipedia page "Pauline Baynes".
Figure 32: A snapshot subgraph of the open KG generated by MAMA-GPT-2XL. from the Wikipedia page "Thor_Heyee "Tyhrrhheyrd
30

---

Language Models are Open Knowledge Graphs
Supplementary Materials

---

Problem
Language models store knowledge
Knowledge graph construction requires human supervision
WIKIPEDIA Free Encrclpele
Common Crawl
Smashwords your your vay.
WIKIDATA
Google BERT
OpenAl GPT-3
DBpedia
yagO
- Freebase
Cortect
Qlanel sourdd
Content
Mar 1epa
WIKIPEDIA The Sacuslope
setici
Everd Wiboo
mococded hedlory has Iten
from doogh of four and water
Bbread stiple food prepared
Bread
Adte
Wilzodds
0003, (dsantipuutoo)
Read
Wiee
Wew Nettoy
Search Wilpeto Q

---

Problem
How to use language models to construct knowledge graphs?
Google BERT
OpenAl GPT-3
YAGO
ConceptNet
OpeniE
Delt
DBpedia
Metaweb
Wikidata
KNOWLEDGE GRAPH
Freebase
Cyc
PROSPERA
NELL
GaoNaaass

---

Language model
Challenges
knowledge
General
Domain specific
corpus
Target
knowledge
Knowledge graph
representation
Structured

---

Pre-trained language model
Dylan was awarded Nobel Prize. Dylan signed Albert Grossman.
Dylan is songwriter.
Corpus
Proposed Approach
BERT
MAMA constructs an open knowledge graph with a single forward pass of the language model (without fine-tuning) over the corpus
graph Map: produces an open knowledge graph from the matched candidates
Match: generates a set of candidate facts from a textual corpus
GPT-2
Match
(Dylan, is, songwriter) (Dylan, awarded, Nobel Prize) (Dylan, signed, Albert Grossman)
Candidate Facts
Map
Songwriter
Open Knowledge Graph
occupation.PP P106
Bob Dylan, Q392
award received P166
Nobel Prize Q7191
sign
Albert Grossman. Q.005...
5

---

Results on TAC KBP
F1 (%)
MAMA-GPTZ
OpeniE 5.1
23.16
27.07
MAMA GPT2mmdmmmmmmmm
Stanford OpeniE
28.79
29.05
MAMA GPT2laree
MAMA MAMA-BERTHSE BERTbase
28:07
28.69
29.12
MAMA-GPTZ
MAMA-BER BER BEE--BRRTaaaa Tlarge
29.72
previous best system by 2.6%
MAMA outperforms the

---

Results on TAC KBP
F1 (%)
MAMA-GPTZ
OpeniE 5.1
23.16
27.07
MAMA MAMA-GPT200 GPT2medim
Stanford OpeniE
28.79
29.05
MAMA HAMA-GPT2large
MAMA HAMA-BERTbaa BERTbase
28:07
28.69
29.12
MAMA-GPT24
MAMA BERTlarge
29.72
MAMA outperforms the previous best system by 2.6%
Larger /eeperreerrrrr LMS produce KGs of higher quality

---

BERT LMs outperform GPT-2 LMS under similar model sizes
Results on TAC KBP
F1 (%)
MAMA-GPTZ
OpeniE 5.1
23.16
27.07
MAMA GPT2mmdmmm
Stanford OpeniE
28.79
29.05
MAMA HAMA-GPT2large
MAMA WAMA-BERTbaC BERTbase
28:07
28.69
29.12
MAMA-GPT22
MAMA MAMA-BERT BERTlarge
29.72
MAMA outperforms the previous best system by 2.6%
Larger /eeeerreerrrrr LMS produce KGs of higher quality

---

Results on Wikidata
F1 (%)
Stanford OpenIE
16.77
MAMA- HAMMA-ERTTaage Tlargge
21.22
MAMA-GPTZW
22.39
Larger corpora embed more complete KGs
MAMA is scalable to larger corpora
MAMA outperforms the previous best system by over 5.6%

---

Tom_ Petty and the Heartbreakers, Q21102111222 7272
Artists United Against Apartheid. Q126877
Harmonica Q5 1290
Rock music Q1,3999
An Open KG Example
Duluth. Q485708 708
the Grateful Dead Q212533
genre:
Polar Music Prize Q754841
the Band. Q600344
member 0fmber-of..P43 P4.3
Grammy Award for Best Solo Rock Vocal Performance Q1542199
residence P551 tour with
20ur with
Columbia Records Q183387
Instrument.Pro0s
genre P135 P136
Urban contemporary gospel. Q5967604
award rece ved. >166
tour with
record lapel P264 P264
award received. P166
Bob Dyaan Q392
genr
occupation P106
Sara Lownds Q457433
spouse P26
songwriter. songwrier.Q753m1 Q753110
work, Jocation. P937
award received. P166
residence PS51
genre.P136
residence, P551
award_ 2156.
Kennedy Center Honors
Nashville Q23197
country of cilttizer ship P27
New York City:QQ0
Folk wws.cmuic.oommmm 0235888
Grammy Lifetime Achievement Award Q835643
occupation P106
unmarrzed partrer
Minneapolis Q36091
United States of America Q30
Contains knowledge in existing KGs (in blue)
award reselved P166
country of www.ennniipppp..
singer-songwiing Q488205
Features new knowledge not in existing KGs (in yellow)
Joan Baez, Q1 Q131725
occupation.P10 record Jabel.P264
Proper Records. Q7250169
genre P136
award reselved
American Folk Music. Q Q1541229
Thomas Morton Award. Q337620
10

---

An Open Question
Assumption: Language models and knowledge graphs just encode the same world knowledge in two different formats, thus the two formats should be equal. But,
Language Models - Knowledge Graphs
Is the assumption TRUE?

---

Contributions
Result: Open knowledge graphs not only cover the knowledge already in existing knowledge graphs (e.g,, Wikidata), but also features open factual knowledge that is new
An unsupervised ttage approach that constructs knowledge graphs with a single forward pass of the pre-trai language models without fine-tuning over the textual corpora
Approach:
language models.
Problem:
foutperforming compared methods by over 5.6% in F1 on Wikidata).
How to construct knowledge graphs from pre-trained
12


Reviews:
[{"review_id": "R1cI1D8r2Iv", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 4, "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper targets an ambitious question -- constructing a knowledge base from scratch using pre-trained language models. The proposed approach deals two problems: (a) constructing facts from raw corpora and (b) disambiguating the entities in the fact by linking them to WikiData entities. \n\nFirst, the insight that knowledge base is embedded in the world knowledge, which is captured by the pretrained embeddings, is not new. [1] studies the joint embedding of knowledge base and text and thus performs knowledge base construction in a semi-supervised fashion, which seems to be more promising than reconstructing the knowledge base from scratch in an unsupervised fashion. I'm curious to see the performance of the power of existing knowledge bases, the signals there can be used to initiate the training.\n\nSecond, baseline comparisons seem to be missing in the experiment section. Even though the task sounds difficult, the numerical performance on recall and F1 are not satisfactory. It's hard to understand the current performance without a reasonable baseline. Apart from that, it would be nice to see the error analysis -- what's the common pattern where MaMa fails? \n\nIn addition to the above major comments, I have the following questions:\n(a) In the map phase where MaMa converts a sentence into facts, do we have an underlying assumption that the input sentence is a fact passage. What will happen if not? \n(b) What if the input passage contains more than one fact? E.g., \"Bob Dylan (born Robert Allen Zimmerman; May 24, 1941) is an American singer-songwriter, author, and visual artist.\" (the first sentence from Wikipedia) contains facts: (bob dylan, is, song writer), (bob dylan, is author), (bob dylan, is visual artist), (bob dylan, also named, Robert Allen Zimmerman), (bob dylan, date of birth, May 24, 1941).\n\n[1] Wang, Zhen, et al. \"Knowledge graph and text jointly embedding.\" Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014.", "extra_scores": {}, "extra_text": {}}, {"review_id": "Kmx2AiDbp0I", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 4, "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Update:  Thanks for the detailed response.  I appreciate the additional figures and other results that you have provided.  However, it seems like there's still some open questions about the types of improvements being made and what this implies about the LM's attention mechanism.  It seems like more quantitative analysis would be needed to determine how much the LM's attention is correlating empirically to factual knowledge or if there are other factors that are affecting the downstream improvements.  Additionally, there's some limitations to the way the language model is being leveraged and the types of knowledge it can extract.   \n\nI also wanted to mention that I appreciate the addition of the suggested related work, but I would still suggest that the authors consider looking into more detailed means of comparison in the future (especially to the Petroni work), since this seemed to be a concern in multiple reviews.\n\n----------------------------------------------\nOriginal:\nThis paper is aimed at using pre-trained language models to create open-ended knowledge graphs.  They introduce an unsupervised approach (MAMA) to construct a knowledge graph in two phases in which they take a target corpus and output a knowledge graph.  In the first phase (match), they extract knowledge tuples from each sentence in a corpus using a beam search over the self-attention within a pre-trained language model. In the second phase (match), they ground facts to a knowledge graph schema by using combinations of entity linking and relation matching techniques from previous work. \n\nWhile this is a promising direction for future research, I tend to lean towards rejection for a few reasons.  The main limitations seem to be: (1) the proposed method is a bit limited in that it can only be used with a corpus in which the target head, relation, and tail spans need to be directly mentioned in a single sentence (2) it’s not clear whether the quantitative improvements are due to factual knowledge in the pretrained model or the syntactic/semantic relationships encoded in the self-attention.  There's also some missing related work in extracting knowledge from pretrained models that should probably be discussed.\n\nContributions:\n- A new algorithm for unsupervised knowledge graph creation from a target corpus\n- Demonstrating the utility of large pre-trained language models towards knowledge graph creation (though, there are other works in this area that should probably be discussed more.  I will list a few in the limitations section.)\n- Improvement over previous state-of-the-art models.\n\nStrengths:\n- The algorithms presented here are relatively straightforward but surprisingly effective.  They improve computationally over previous work.\n- Most of the paper is clearly written.  The experiments seem easily reproduceable.\n- The algorithm presented here is able to be used in an unsupervised way and can work with both open-ended and more structured knowledge graph schema.  \n\nLimitations:\n- There are some limitations to the type of knowledge that can be extracted with the proposed approach: the only tuples that can be selected must be described within the target corpus in which the head and tail must appear within the same sentence.\n- There is some missing prior work in creating knowledge graphs from pre-trained language models. To name a few:\n  -“Language Models as Knowledge Bases?” EMNLP 2019\n  -“Commonsense Knowledge Mining from Pretrained Models.” EMNLP 2019\n  -“Comet: Commonsense Transformers for Automatic Knowledge Graph Construction.” ACL 2019\n- Although the proposed model achieves quantitative improvements over Angeli et al. (2015), it seems unclear whether these improvements are due to the factual knowledge encoded in the pre-trained LM, as claimed.  It seems possible that these improvements could instead be due to the high quality semantic/syntactic relations encoded in the attention mechanism.\n- The title of the paper is a bit strongly worded and may be over-claiming what is shown quantitatively in this paper.\n\nQuestions for the authors:\n- How the parameter study was conducted? Was this using the development set, and if so for which dataset?\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "5jRB1jNUxv", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 4, "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper presents an unsupervised approach for extracting OpenIE style triples from a corpus. The approach leverages the internal attention maps of pretrained transformers to identify paths which correspond to relations between a head entity and a tail entity. The extracted open triples are then mapped, wherever possible, to an existing KG to create what is referred to as an Open KG.\n\nStrengths:\n- The unsupervised matching approach for extracting triples is quite novel and, to my knowledge, the first evidence that the attention maps of pretrained transformers contain paths which capture relational knowledge.\n\nWeaknesses:\n- The main evaluation in the paper is done on the grounded facts after the mapping stage. While the details are hazy, based on Section 2.2.1, it seems the mapping stage relies on a pre-existing KB aligned with a corpus to do the entity linking and relation linking steps. In this case another strong baseline would be to learn distantly supervised entity and relation extraction methods. Of course, this approach would not be able to produce the ungrounded facts, but when evaluating only the grounded facts it would be nice to see how the proposed approach compares to this more traditional setting.\n\n- Related to the above, important details are missing about the interaction between the KG used for the entity and relation linkers and the KG used for evaluation. Specifically, what is the size of the KG available for entity / relation linkers, and were there any overlapping facts between this and the facts used for evaluation?\n\n- Several recent papers have looked at probing LMs for knowledge facts (e.g. \"Language Models as Knowledge Bases\" Petroni et al, EMNLP 2019, and follow up papers). These are very relevant, as another approach for constructing KGs from pretrained LMs is to use natural language templates. But there is no discussion of these works in the paper.\n\n- Selecting the threshold for the matching degree seems to be an important step influencing the quality of the final KG, but it seems to have been done in an ad-hoc manner here.\n\nOther comments:\n- Section 2.1 states that \"self-attention weight matrix ... is the main container of the knowledge information in pre-trained LMs\". This is clearly not true as a large amount of knowledge is also stored in the word embedding table.\n\n- Section 3.2 has some confusing terminology where the facts supported by the KG schema are also referred to as \"ungrounded\" which contradicts the definitions in section 2.2.2.", "extra_scores": {}, "extra_text": {}}, {"review_id": "4s8A2Gfswf2", "reviewer": "AnonReviewer2", "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Paper summary: The paper introduces an unsupervised method that utilizes an off-the-shelf BERT (without any fine-tuning) to create an information extraction system without any training data. It first creates ungrounded triples (a.k.a. OpenIE) from raw text by looking into the attention weights between words and finding a sequence of words through a beam search that has high attention weights between every consecutive words. When such sequence is created, the first and the last word become the head and the tail entities and the words between them becomes the relation. The next step is grounding each triplet to a known Knowledge Graph by utilizing off-the-shelf entity and relation grounding mechanisms. The proposed method shows 1-2% F1 score advantage over Stanford OpenIE on TAC KBP and Wikidata.\n\nStrengths:\n- The proposed method is unsupervised (doesn't require any training process or data).\n- The proposed method only requires a single application of BERT per paragraph (instead of, for instance, applying BET for every possible triplet), which is relatively efficient.\n- The idea of looking into the pooled attention weights for linking entities and relations is novel.\n\n\nWeaknesses:\n- The head and the tail entities are always single words, whereas many entities such as names have two or more words.\n- The comparison is made only against Stanford OpenIE, which was proposed 5 years ago.\n- The paper highly depends on the grounding techniques from many years go, namely Spitkovsky & Chang (2012) and Stanford OpenIE (2015). Given that the proposed model and Stanford OpenIE have similar performance, I think it is necessary to perform ablation study and show how much is the dependency. In the worst case, I am worried that most of the work is being done in this grounding stage (i.e. ungrounded triplets are not so good)\n- If all possible head-tail pairs are enumerated in a sentence, my expectation is that there will be a lot of garbage triplets. It is amazing yet hard to believe that such simple thresholding techniques make it work and get rid of all of the bad triplets. More analysis will be helpful.\n\nOverall: The approach is interesting in that it is completely unsupervised, and is able to achieve a meaningful performance (compared to Stanford OpenIE). However, I am not 100% convinced how this could work so more analysis would be helpful, and given that Stanford OpenIE is pre-BERT from 5 years ago, the gain seems to be not so exciting.", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: We want to acknowledge that there has been a tremendous amount of work done during the discussion period on this paper for clarifying multiple points, adding multiple new comparison methods and new analysis. This is a very different draft than what was submitted and the reviewers acknowledged that. The draft is much closer from acceptance at ICLR than it was at submission time. However, despite all those additions, we do not support a publication at ICLR. 

The main issues with the current draft are its positioning and motivations.  Right now the draft is in-between a paper about Knowledge Base Construction (KBC) and a paper analyzing the knowledge contained within a large language model. This in-between came up in multiple places during the discussion and is what causes the biggest confusion around this work. And, since there is no clear choice, the draft has limitation on either side. 
* If the main point is around KBC to build general-purpose KBs, then one would expect experiments on downstream tasks powered by a KB, language understanding tasks for instance. Indeed, KBs are just a means to an end and the latest advances in very large language models have shown that KBs were not essential to be state of the art in language understanding tasks (GLUE, QA datasets, etc). So we would like to see whether these enhanced KBs could be beneficial. Or the KBs are studied as a way to encode commonsense like in (Bosselut et al., 2019) or (Davison et al., 2019), but this is not the point of the current draft.
* If the main impact of the draft is around what the language models learn, bridging the deep language model and knowledge graph communities through enhanced model transparency, as it has been said in the discussion, then the discussion with (Petroni et al. 19) should be more prominent and the introduction, motivation and experiments of the draft should reflect that.

That's why, even if this work is of solid quality, the current draft can not be accepted.
Decision: Reject