Ekaterina M Kozinets

Marianna Fekete

Oleg A Filippov

Natalia V Belkova

Elena S Shubina

Rinaldo Poli

Simon B Duckett

Eric Manoury

Activation of a

Introduction

Square planar d 8 complexes of Rh I and Ir I are commonly employed as catalysts in a variety of hydrogenation processes. The most popular examples include the so-called "Wilkinson's catalyst", [RhCl(PPh3)3], 1 and the cation [Ir(COD)(L)2] + (L = PPh3, PMePh2, py), the latter of which displays high activities even with relatively hindered C-C double bonds. 2 Complexes of type [MCl(diene)]2 or [M(diene)2] + [M = Rh or Ir], and particularly those of iridium, have also proven suitable as precatalysts for the ionic hydrogenation of polar substrates such as ketones and imines in the presence of appropriate ligands, mostly diphosphines. [3][4][5][6][7][8][9][10] A notable example of this is provided by Ir I -catalyzed imine hydrogenation as used in the multi-ton scale industrial production of the herbicide metolachor. 11,12 In one of our laboratories, chiral ferrocene-based phosphine thioether ligands (P,SR in Scheme 1) 13 have been developed and shown to be particularly efficient in terms of both activity and enantioselectivity for the hydrogenation of aromatic ketones when combined with [IrCl(COD)]2. 14 The Ir I coordination chemistry of these ligands has also been described. 15,16 Since the initial studies that were aimed at generating and characterizing the catalytically active species met with difficulty, our attention has more recently turned to the analogous (P,SR)-based rhodium complexes, inspired by reports of the isolation and characterization of related diphosphine-based complexes at the pre-catalyst activation stage. [17][18][19][20] We have thus synthesized and characterized complexes of type [RhCl(P,SR)(diene)] and [Rh(P,SR)(diene)] + BF4 -(diene = cyclooctadiene, COD; norbornadiene, NBD), 21 as shown in Scheme 1. These complexes have been demonstrated to act as both structural and functional mimics of the analogous Ir systems, although they show lower catalytic activity and selectivity. 22 We considered it of interest to investigate the phenomenon of precatalyst activation by use of parahydrogen NMR. The parahydrogen method has been used extensively to probe reaction mechanisms by enabling the detection of low concentration species such as intermediates in catalysis. 23,24 It was first successfully employed by Weitekamp, 25 and later 55 Eisenberg and Bargon, 26 but is now being employed much more widely as a consequence of the potential that hyperpolarization methods offer to magnetic resonance imaging and hence health care. 27 When this approach is used in conjunction with DFT the synergy of the two methods becomes readily apparent as hitherto 60 unseen species are not only predicted but firmly characterized in solution. Such studies have already been used to rationalize a series of reactions involving ruthenium clusters [28][29][30] where they can play a key role by helping with chemical shift and coupling assignments. Furthermore they have established a definitive 65 opportunity to examine the role of electronic states in the oxidative addition of H2 to a series of 16 electron ruthenium complexes 31,32 and aided in the understanding of hydrogenation catalysis by a range of palladium complexes, 33,34 and even to detect unexpected CH bond activation products in conjunction 70 with the well-known complex W(N2)2(dppe)2. 35 In this contribution, we address the stoichiometric reactivity of two representative compounds containing the same (P,S t Bu) ligand, [RhCl(P,S t Bu)(COD)] (1) and [Rh(P,S t Bu)(COD)] + BF4 - (2), towards H2 through the eyes of parahydrogen NMR, 75 complemented by DFT calculations.

Experimental Section

General

All the reactions described here and the complex purifications were carried out under argon using high vacuum line or Schlenk line techniques. The Rh complexes [RhCl(P,S t Bu)(COD)] (1) and [Rh(P,S t Bu)(COD)]BF4 (2) were synthesized from 2diphenylphosphino-(t-butylthiomethyl)ferrocene (P,S t Bu) and [RhCl(COD)]2 or [Rh(COD)2]BF4 according to the published procedure. 21

NMR experiments

NMR measurements were made using NMR tubes that were fitted with J. Young's valves and solutions were degassed on a high vacuum line prior to H2 addition. The samples were prepared in a glovebox by addition of the specified deuterated 10 solvents to the solid complex in the NMR tube. For the p-H2 induced polarization (PHIP) experiments, hydrogen enriched in the para spin state was prepared by cooling H2 to 36 K over the paramagnetic catalyst Fe2O3 which was doped on silica. 23,24,36 All the resulting NMR studies were then carried out with sample concentrations of approximately 4.6 mM and all spectra were recorded on a Bruker Avance III 400 NMR spectrometer. 1 H NMR chemical shifts are reported in ppm relative to the residual 1 H signal of the solvent which for CHD2OD is  3.31 and for CDHCl2 is  5.37. Further details can be found in the 20 supplementary information. Scheme 2

Computational details

Calculations were performed with the Gaussian09 package 37 25 using the B3LYP [START_REF] Becke | [END_REF]39 and M06 40 functionals under the DFT approach. All carbon and hydrogen atoms were described with the 6-31G(d,p) basis set, whereas the 6-31++G(d,p) basis set was applied to the atoms of ligands involved in the bonding with Rh (P, S atoms, hydride ligands, OH group of methanol and N atoms of pyridine and acetonitrile). Effective core potentials (ECP) and its associated SDD basis set [41][42][43][44] supplemented with f-polarization functions (SDD(f)) 45 were applied for the Rh and Fe atoms. Geometry optimizations were performed without any ligand simplification for the cationic [Rh(P,S t Bu)L2] + and [Rh(P,S t Bu)(H)2L2] + complexes (L = pyridine, CH3OH, CH3CN). Frequency calculations were performed for all optimized complexes in the gas phase and reported without use of scaling factors. The nature of all the stationary points on the potential energy surfaces was confirmed by a vibrational analysis. 46 40 Transition state (TS) structures showed only one negative eigenvalue in their diagonalized force constant matrices, and their associated eigenvectors were confirmed to correspond to the motion along the reaction coordinate under consideration using the Intrinsic Reaction Coordinate (IRC) method. 47

Results and Discussion

(a) NMR experiments

In CD3OD without additives

The addition of p-H2 to d4-methanol solutions of either [RhCl(P,S t Bu)(COD)] (1) or [Rh(P,S t Bu)(COD)]BF4 (2), over the 50 temperature range 233 -298 K, failed to result in the observation of any detectable hydride containing species. There was, however, evidence for a common slow reaction (ca. 18% conversion in 20 minutes at 243 K) which transformed the  2  2 -COD ligand into a κ 3 (σ:π) cyclooct-4-enyl ligand and generated 55 the new proton loss complex, 3, (Scheme 2) in very small amounts. The κ 3 -ligand in this complex is characterized by features at  4.62, 1.54 and 2.15, see Figures S1 andS2. This suggests that while slow H2 addition to the Rh centre occurs, rapid reaction transforms the resulting dihydride into 3. This

]BF4. In this case, these species could be isolated and fully characterized. 48 We note that 3 is related to this Ir system by the formal loss of a proton. Given that Rh III hydride complexes are 70 known to be less stable than their Ir III analogues this transformation is not unexpected.

At 253 K the generation of cyclooctene was revealed by its characteristic 1 H NMR resonance at δ 1.21, which rapidly increases in intensity over the time scale of 17 minutes (Figure 75 S2). This demonstrates that the initial H2 addition to 1 or 2 proceeds at 253 K. When the temperature was raised to 263 K the formation of cyclooctane was also observed through its characteristic singlet at δ 1.50. Hydride resonances were not observed at any time during this reaction. However, the residual 80 OH signal of methanol and the H2 signal show dramatic temperature dependence, coalescing at 263 K thereby suggesting the rapid interchange of these sites, presumably through the formation of transient, non observable hydride species.

At 273 K the deuteration of the H2, forming HD becomes 85 evident (Figure S3). In addition, the phenyl proton signals for the phosphine become more complicated in appearance at this point. This reaction monitoring demonstrated the instability of unreacted starting material could be seen even after 7 h. After 3 days the starting material was completely consumed and the new NMR spectrum shows very few small resonances, the major one being a doublet at δ 43.9 (JPRh = 134.1 Hz). These data suggest that 4 is not stable.

Our attempts to isolate the final product of this reaction in a crystalline form have so far not succeeded. Heller et al. have recently reported the successful generation and isolation of the complex [Rh(BINAP)(MeOH)2] + by hydrogenation of [Rh(BINAP)(COD)] + or [Rh(BINAP)(NBD)] + in methanol, although no reaction intermediates were observed in those cases. 18 When this reaction was carried out in CD2Cl2 using 2, reaction with p-H2 was evident at temperatures down to 233 K. Under these conditions weak signals for a new hydride containing species were detected at  -9.7 and -23.0. The high field signal yielded a 31 P coupling of 188 Hz which is indicative of a trans 31 P splitting. These two resonances were broad and neither the JHH nor the JRhH couplings could be quantified. In the corresponding 31 P decoupled spectra the  -23.0 signal yields a JHH splitting of -9 Hz and a JRhH splitting of 20 Hz. The signals for this species were too weak to enable its characterisation but the hydride trans to phosphine deduction is confirmed by the large coupling and a species such as A in Scheme 2 is possible. The addition of 1 l of MeOH to this solution suppressed the observation of these signals.

Two key observations outlined above (the coalescence of the MeOH and H2 signals, and H/D exchange between these two molecules) lead us to speculate on the mechanism associated with these phenomena, which are obviously related to the same process, occurring rapidly on the NMR timescale. Two possible pathways are indicated in Scheme 3. In pathway a, H2 oxidative addition yields a Rh III dihydride species 5 that would be sufficiently acidic to release a proton to the solvent and yield a Rh I monohydride intermediate 6. Reversal of all steps with implication of deuteron incorporation is a consequence of this exchange pathway. In the alternative pathway b, H2 replaces a methanol ligand to yield an H2 complex (non-classical dihydride) 7, which is then deprotonated by the solvent to yield the same monohydride complex 6 as detailed above. Both of these pathways can be imagined to occur via initial reaction which places the hydride trans to S (as shown in Scheme 3) or in the alternative position trans to P which is not illustrated. Incidentally, the lack of observation of a hydride resonance under these conditions does not exclude the fact that the most stable species (at least at low temperatures) is indeed a mono-or dihydride complex, since such a species would exist primarily in the form of a deuteride and would therefore be unobservable in the hydride region of the 1 H NMR spectrum.

In CD3OD in the presence of pyridine

When the analogous reactions of either 1 or 2 with H2 were carried out in the presence of pyridine at low temperature (233 -55 283 K), on the other hand, two common and dominant hydride containing products formed. For example, when a d4-methanol solution of 1 was prepared which contained a 75-fold excess of pyridine (75 equiv) and the resulting reaction with p-H2 monitored by 1 H NMR spectroscopy at 233 K, four PHIP 60 enhanced hydride signals were observed at  -13.1,  -14.3,  -17.2 and  -17.6. These hydride ligand signals all contained antiphase features due to their formation from p-H2 that are associated with their common JHH coupling in addition to inphase splittings due to further couplings to a single rhodium and 65 single phosphorus centre. A more complete NMR monitoring of this reaction was undertaken after warming the sample to 263 K, during which process the NMR spectral features did not change. The hydride region of the resulting 1 H NMR spectrum is shown in Figure 1a. The JPH coupling values are all consistent with cis 70 hydride phosphorus ligand arrangements. In addition, in the corresponding 1 H-31 P HMQC NMR spectrum (Figure 1b), the  -13.1 (JHH = -13 Hz, JRhH = 24 Hz) and  -17.6 (JHH = -13 Hz, JRhH = 19 Hz) signals proved to couple to a single 31 P centre that was located at  47.9 and appeared with a JRhP of 140.0 Hz. These 75 resonances therefore arise from groups within the same complex (8). In a similar manner the  -14.3 (JHH = -10 Hz, JRhH = 24 Hz) and  -17.2 (JHH = -10 Hz, JRhH = 20 Hz) signals arise from a second species (9) where now the connected 31 P resonance appears at  45.0 with a JRhP of 141.6 Hz. On the basis of the 80 relative hydride resonance peak areas it can be suggested that 8 and 9 are formed in a step which shows a kinetic selectivity of approximately 1:4 at 233 K, if it is assumed that these resonances This information therefore confirms that there are two pyridine ligands attached to the metal centre in 8 and 9, which are located 35 trans to one hydride and to the phosphine donor. Given the bidentate nature of the P,S ligand it can be further concluded that When an nOe experiment was recorded to probe the hydride site interchange process undergone by these complexes at 273 K with the 75 fold excess of pyridine, several exchange processes were observed (as revealed in Figure 2). These include a mutual hydride site interchange within 9 which proceeds with an exchange rate constant of 0.785(6) s -1 and the interconversion of 9 into 8 on a slower timescale that places the moving hydride into either site of 8 with rates of 0.167(2) s -1 where Hc becomes Ha and Hd becomes Hb and 0.160(3) s -1 where Hc becomes Hb and Hd becomes Ha. Hence there is a limited selectivity in this process.

Isomer 8 converts into 9 on a faster timescale where the observed rate constant is 1.10(1) s -1 for the Had and Hbc transformations and 1.19(1) s -1 for the Hac and Hbd transformations. The experimental rate constant for hydride site interchange in 8 is zero. The overall scheme of hydride site exchange rates is summarized in Scheme 5. Table S1 (Supp. Inf.) shows the list of constraints that were used for the calculation of the exchange rate constants. There was no evidence for hydride exchange into free H2 or MeOH from 8 or 9 in these experiments which are limited by the timescale of NMR relaxation and contrary to the experiment run in pure CD3OD there was no coalescence between the solvent and H2 resonances. We note, however, that the hydride sites of 8 and 9 are partially deuterated in these experiments. This readily shows up in a 31 P-decoupled HMQC measurement as isotopically perturbed signals at  -13.1, -14.3, -17.2 and -17.6 in the corresponding 1 H NMR spectrum for the Rh(H)(D) partners and at  -14.3 and  -17.2 for the corresponding 31 P signals. Hence, the electronic effect of pyridine coordination stabilizes the dihydride species against deprotonation (cf. Scheme 3). While the deuterium label incorporation into the hydride sites is relatively slow, it precludes the measurement of precise rate data as would be required for the assembly of an Eyring plot. Nonetheless, the interconversion between these species has been 55 defined as occurring without H2 loss. This is reflected in the fact that strong PHIP is only seen when 1 or 2 are being converted into 8 and 9. When we monitor these processes with differing amounts of pyridine, changing the Rh/pyridine ratio from 1:8, to 1:75, and 1:173 we see no change in the relative hydride signal 60 intensities of 8 and 9. There is, however, a significant effect on the hydride site interchange rate constants.

The rate constant for the mutual hydride site exchange in 9 falls to 0.29 s -1 with an 8 fold excess of pyridine while it is 0.39 s - 1 with a 173 fold excess. The kinetic effect of pyridine is 65 therefore complex, first promoting the process and then inhibiting it. A similar trend is observed for the Hda (and Hcb) rate from 0.06 s -1 through 0.16 s -1 to 0.02 s -1 and for the Hca (and Hdb) rate from 0.14 s -1 through 0.17 s -1 to 0.12 s -1 , though the effect here is less dramatic. In contrast, the Hac (Hbd) process shows a 70 rate increasing with [py] from 0 s -1 through 1. while the rate of the Had (Hbd) process shows the opposite trend from 1.47 through 1.10 to 0.02 s -1 . We can conclude therefore that pyridine plays a role in these processes. All observed hydride site exchange rate constants are collected in Table S3.

The hydride signal at  -17.2 shows nOe connections to peaks at  8.71, 8.23, 4.67, 3.84 and 1.21, which are due to the ortho proton of a pyridine ligand, the ortho proton of the phosphine, the ferrocenyl group and the t Bu group in 9. The second hydride signal in 9 which resonates at  -14. The hydrogenation of the cyclooctadiene ligand is readily evident in these p-H2 enhanced 1 H NMR spectra from 253 K. Two sets of polarised signals appear at  1.53 and 1.50 due to the CH2 protons of the hydrogenated components of COE. A further resonance is evidenced at  1.41 for the backbone signals as described above. In these 1 H NMR spectra the corresponding cyclooctane signal appears at  1.2 and forms very slowly at 273 K.

When the reaction of the chloride derived precursor 1 with normal dihydrogen is monitored in an analogous experiment with only a 15 fold excess of pyridine (instead of 75 in the experiment described above) 5 appears as the first formed hydride containing product at 273 K. The problem with this measurement is, however, that it relies on detecting a weak signal from magnetisation that is at thermal equilibrium. When this reaction is repeated with p-H2 weak PHIP-enhanced hydride signals are now seen for a further two species, 10 and 11, besides those of 8 and 9 in the resulting NMR spectra at 273 K (Figure 3). The hydride signals of 10 appear at  -12. When a series of EXSY measurements were undertaken to examine the dynamic behaviour of this complex reaction system, interconversion between 10, 8 and 9 was evident with the signals 65 for 11 proving to be too weak to monitor. When the pyridine excess was 75 fold, the experimental rate constant for the Hec and Hed processes (109) were indistinguishable at 31 s -1 . In contrast the corresponding rate 75 constant for Hea and Heb (108) was zero, as was mutual He-Hf interchange (10). These values reduce from 31 s -1 to 20 s -1 to 3 s -1 as the pyridine excess falls from 75 fold through 42 fold to 15 fold. Hence this process is [py] dependent. Concerning the 910 process, the Hce rate constant is 0.41 s -1 , and the Hcf rate has a 80 similar value. The corresponding rates for the 98 process are slightly smaller than those found when 2 is employed (Hca and Hcb are again similar at 0.1 s -1 vs. 0.16 s -1 for 2). The new rate constants for 89 with hydride position retention are zero when 1 is the precursor and 1.1 s -1 when 2 is employed. 8 does, 85 however, form 10 with rate constants 1.3 s -1 for Haf and zero for Hae. The observed hydride site exchange rate constants at 273 K measured on sample 1 for different pyridine concentrations are reported in Table S2.

90

In CD3OD in the presence of acetonitrile

In a further study, a d4-methanol sample of 1 containing 20 L of acetonitrile was prepared. No reaction of 1 with H2 was evident until 273 K. At this point, the corresponding 1 H NMR spectrum contained two PHIP polarized hydride peaks at δ -14.5 95 and -17.6. These hydride signals again appear as simple anti-phase doublets with additional phosphorus and rhodium couplings (Figure 4). The signal at δ -14.5 exhibits a JHH coupling of 11 Hz, a JHP coupling of 20 Hz and a JRhH coupling of 21 Hz while the corresponding splittings of the δ -17.6 signal are 12, 17 Hz and 20 Hz respectively. In the corresponding 1 H-31 P HMQC NMR spectrum, the  -14.5 signal and the  -17.2 signal proved to couple to a single 31 P centre located at  47.6 which exhibited a JRhP splitting of 148 Hz. These results suggest that hydrogenation of 1 in the presence of acetonitrile selectively forms the MeCN analogue (12) of 8 (Scheme 7). There are a number of similarities 10 between these data and those of 8. For example, all the couplings suggest a cis PH ligand arrangement and the chemical shifts are reflective of hydride ligands trans to sulfur and nitrogen. The coordination of MeCN has therefore the same effect as that of pyridine in reducing the hydride acidity and reducing the speed of the H/D exchange between H2 and the solvent.

Hydrogenation Studies

We have also explored the activity of these systems as 25 hydrogenation catalysts. When a d4-methanol sample of 1 containing 20 l (75 equiv) of pyridine and diphenylacetylene was examined at 258 K, limited hydrogenation was evident and strongly enhanced signals for 8, 9, 10 and 11 were visible which exceed those in the analogous experiment without the alkyne.

Under these conditions at 258 K the ratio of the p-H2 enhanced hydride signals of 8 and 9 was now 1:3.8. In contrast, when a similar sample was examined without pyridine, more rapid, but still slow, hydrogenation of diphenylacetylene occurs; signals for both cis and trans stilbene are evident. These signals appear in an 35 OPSY measurement which was used to detect only protons that were previously located in a single molecule of p-H2. No hydride signals were observed during these measurements.

When hydrogenation of the more reactive substrate phenylacetylene by 1 was examined in d4-methanol using p-H2, 40 PHIP polarized signals could be readily seen for the styrene product at 263 K when no pyridine was present. No hydride signals still visible however in this experiment. In contrast, when a 10-fold excess of phenylacetylene and acetonitrile was added there was evidence for both the formation of 12 and the 45 hydrogenation of phenylacetylene.

(b) DFT calculations

On the basis of the presented experimental evidence, H2 is able to hydrogenate the COD ligand in compounds 1 and 2 in MeOH, presumably yielding a [Rh(P,S t Bu)(MeOH)2] + product (4). 50 However, any further oxidative addition of H2 to yield a putative dihydride product [Rh(H)2(P,S t Bu)(MeOH)2] + (5) would not be evidenced by 1 H NMR if there was rapid H/D exchange as suggested. On the other hand, further H2 oxidative addition takes place in the presence of py or MeCN to produce observable 55 dihydridorhodium(III) species. A first question addressed by the computation tool was to explore the relative stability of the [Rh(P,S t Bu)L2] + (L = MeOH, py, MeCN) complexes and their ability to oxidatively add H2, as well as defining the relative stability of all possible product isomers, see Scheme 8. The 60 associated calculations were carried out using either the B3LYP functional or the M06 functional.

The hypothetical replacement of the COD ligand in [Rh(P,S t Bu)(COD)] + (2) with two L donors to yield I L (without COD hydrogenation) was found at the B3LYP level as endoergic The oxidative addition of H2 was also found to be energetically favourable for all three ligand systems, as shown by the data 75 collected in Table 2. It should be noted that while the gas phase free energies are positive, the computed values do not take into account stabilisation from solvation and the partial quenching of translational and rotational modes in the condensed phase. Furthermore, the hydride complexes were observed at low 80 temperatures, where the detrimental effect of the positive TΔS contribution is smaller (for instance, the calculated ΔG for the oxidative addition to I py to yield V py at the M06 level decreased from +6.0 to +4.9 kcal/mol when applying the thermochemical corrections at 253 K instead of 298 K) and indeed the hydride 85 resonances were lost upon warming the NMR tubes to higher temperatures (vide supra). For the pyridine system, the calculations have also been carried out at the M06 level, showing a slightly less favourable process. We note that the oxidative addition process for L = MeOH is predicted by these calculations 90 to be more favourable than for the other two ligands. Therefore, failure to observe the corresponding hydride resonances by 1 H NMR during the experiments appears indeed attributable to the accumulation of deuteride species by rapid H/D exchange. The dihydride complex is, though, at least sufficiently accessible to 95 allow the H/D process to take place as suggested in Scheme 3. The computational results also suggest that the two isomers resulting from the H-H addition across the S-Rh-L axis are energetically preferred independent on L, in agreement with the assignment of the NMR spectra. The stereochemistry of structure IV corresponds to that of 8 and 12 and V corresponds to 9.

Structure V py is slightly less stable than IV py at the B3LYP level, but more stable at the M06 level in agreement with the NMR evidence. For L = MeCN and MeOH, the calculations also indicate greater stability for isomer V L , even at the B3LYP level, whereas the NMR assignment indicates that the observed compound 12 has the same configuration as IV.

A second question addressed by the DFT study is the mechanism for the H-site exchange in the two observed isomers for the pyridine systems (8 and 9, or IV py and V py ). The simplest way to imagine the isomerisation process, given the known strong trans labilizing effect of the hydride ligands, involves dissociation of a neutral donor placed trans to a hydride ligand (either the sulphur donor of the bidentate P,S ligand or a pyridine molecule) and rearrangement of the coordination sphere, as shown in Scheme 9 for the specific case of thioether dissociation.

The putative 5-coordinate intermediate A would then rearrange to its diastereoisomer B (same chirality at ferrocene and inverted chirality at rhodium) by either concerted hydride migration and rotation of the P,S ligand or via tautomerization to the dihydrogen complex C followed by P,S rotation to yield the rotamer D.

Hydride site exchange can be envisaged by rotation of the dihydrogen ligand in the intermediates C and D. The observation of such a ligand exchange process by NMR is not inconsistent provided the lifetime of the rapidly relaxing dihydrogen form is short. 23, 24 35 Scheme 9

All calculations of this pathway were carried out with the M06 functional, since this is expected to better handle the long range dispersion interactions involved in the ligand dissociation 40 processes. They show that pyridine dissociation is less favourable than the P,S ligand thioether arm dissociation. The most favourable dihydride complex resulting from py dissociation, [(P,S t Bu)Rh(H)2(py)] + , is located at 29.7 kcal/mol from V py , while the isomeric non-classical complex [(P,S t Bu)Rh(H2)(py)] + 45 is more stable at only 18.3 kcal/mol from V py . However, the most stable dihydride complex resulting from dissociation of the thioether arm (B in Scheme 9) is only 12.4 kcal/mol from V py , with A being only slightly higher at +14.7 kcal/mol and the nonclassical isomers C and D are even more stabilized at +11.8 50 and +8.3 kcal/mol, respectively. Therefore, the isomerization pathway shown in Scheme 9 was fully explored. The results are summarized in Figure 5. Rearrangement of the classical to the nonclassical dihydrides occurs via localized transition states TSAC and TSBD at +18.4 and +16.2 kcal/mol. A more direct 55 pathway from IV py to C and from V py to D could not be found. The P,S ligand rotation pathway involved in the interconversion of C and D was not straightforward to investigate for the location of a stationary point, but a manual scan of the dihedral N-Rh-P-C angle (see Figure S4) led to the identification a new local minimum corresponding to a new rotational intermediate E and two maxima, the highest point being 4.3 kcal/mol higher than D (+12.6 kcal/mol from V py ). The search for a more direct pathway from A to B was unsuccessful. The barrier for site exchange was calculated for D and turned out rather low (2.5 kcal/mol, or + 10.8 kcal/mol from V py ), lower than the barriers required to go back to the stable classical dihydride isomers. The results in Figure 5 are in agreement with certain experimental observations. Isomerization from V py (9) to IV py (8) requires transiting over the TSAC barrier, whereas the site exchange in 9 may occur by simple access of the nonclassical intermediate and facile H2 rotation, followed by the reverse pathway to V py through the lower TSBD barrier. This agrees with the faster site exchange relative to the 98 process. During the reverse isomerization of IV py (8) to V py (9), on the other hand, once the highest TSAC barrier is passed and the intermediate C is 20 generated, the isomerization process through P,S ligand rotation and the lower energy TSBD occurs faster than the reverse generation of IV py , consistent with the fact that site exchange for 8 is not observed.

A most interesting experimental result is that the

Conclusions

The present contribution has explored the precatalyst activation 65 phase for hydrogenation processes carried out in alcohol solvents with [Rh(diene)(LL')] + systems, using [Rh(COD)(P,S t Bu)] + as a model compound. The COD ligand is removed by hydrogenation to yield a putative [Rh(P,S t Bu)(CH3OH)2] + complex which promotes a very rapid H/D exchange between H2 and the solvent, 70 possibly via a dihydride species [Rh(P,S t Bu)(H)2(CH3OH)2] + that is accessible according to the DFT calculations. Addition of L (pyridine or MeCN) slows down this exchange, allowing the observation of diasteromeric dihydride species at low temperature. Evidence has also been obtained for equilibrium 75 deprotonation of these cationic dihydride complexes in the presence of strong bases (e.g. excess pyridine). This phenomenon is presumably linked to the need of a strong base promoter for the catalytic action of these compounds and of the iridium analogues in the ionic hydrogenation of polar unsaturated substrates.

  Scheme 1

  60 process is followed by reprotonation to form the corresponding alkene after hydride migration as shown in Scheme 2. The liberation of either HCl or HBF4 is proposed to give 3. Sola et al. have reported a related H2 addition to the complex [Ir(COD)-(NCCH3)(PMe3)]BF4, where the initial dihydride product [Ir(H)2-65 (COD)(MeCN)(PMe3)]BF4 (in CH2Cl2) reacts with MeCN to form the monohydride product [IrH(1-κ-4,5--C8H13)(NCCH3)2-(

  Scheme 3

Table 1 .

 1 Figure 1. NMR spectra showing characteristic resonances of 8 and 9 (as indicated) observed during reaction of 2 in d4-methanol with p-H2 and 75 equiv of pyridine at 263 K: (a) p-H2 enhanced 1 H NMR spectrum showing the hydride region; (b) 1 H-31 P HMQC NMR spectrum collected using 15 N labeled

Figure 2 .

 2 Figure 2. Hydride ligand exchange data for the interconversion of 8 and 9 in the presence of 75-fold excess of py, as probed through the selective excitation of (a) Ha of 8 and (b) Hc of 9, over the defined observation 45

  3 shows through space interactions with protons that give rise to resonances at  8.71, 8.31, 8.23 and 6.68. The extra signal at  8.31 is therefore the ortho proton of a second pyridine ligand. In the corresponding 1 H-31 P HMQC, the 31 P centre which resonates at  45.0 connects to two aromatic signals at  8.23 and 6.78. In contrast, the 31 P signal of the minor isomer 8 proved to connect with 1 H signals at 8.03 and 7.5 in an HMQC measurement. Furthermore, nOe data confirmed that the signal at  -13.1 in 8 connects with 1 H signals at  8.42 and 8.03. The  8.42 resonance therefore corresponds to an ortho-pyridine signal. This information has therefore confirmed the assignment shown in Scheme 4. The structure corresponding to 9 has therefore the hydride ligand trans to pyridine located on the same side as the ferrocenyl group. When a long range 1 H-31 P experiment was recorded, further proton signals were located at  7.6, 4.8 and 4.2 in 9.

  3 and -18.7 with Rh-H couplings of 25.1 Hz and 26.8 Hz, respectively, and a common JHH coupling of -5.9 Hz. The hydride ligand signals from species 11 are, however, much weaker than those of 10 and appear at  -15.0 (JRhH = 20, JHH = -9 Hz) and  -17.8 (JRhH = 15, JHH = -9 Hz). The relative ratio of the hydride signals for 10, 8, 9, and 11 proved to be 0.9:7.0:39.6:1 at 273 K. In a series of 2D measurements the  -12.3 hydride signal proved to connect with a  34.3 31 P resonance where JRhP = 140 Hz, while the  -15.0 hydride signal connects with a 31 P resonance at  51.4 that exhibits a JRhH coupling of 148 Hz. The relative intensity of the signals for 10 and 11 proved to fall as the excess of pyridine is increased. When labelled pyridine is employed both of the sets of hydride signals show limited broadening but no trans-15 N coupling is evident. We therefore assign 10 and 11 to chloride containing [Rh(H)2(P,SR)-(pyridine)Cl] as shown in Scheme 6. This deduction is further supported by the fact that while the relative intensities of the 60 signals for 8 and 9 seem to remain constant regardless of the [py] those for 10 and 11 drop as the [py] increases.

Figure 3 .

 3 Figure 3. NMR spectra showing characteristic hydride resonances of 8, 9, 10 and 11 (as indicated) observed during reaction of 1 in d4-methanol with p-H2 and 45-fold excess of pyridine at 273 K.70

Figure 4 .

 4 Figure 4. p-H2 enhanced 1 H NMR spectrum showing the hydride region of 12 observed during reaction of 1 in d4-methanol with p-H2 and acetonitrile at 273 K.20

  65 when L = MeOH (ΔE = 15.4 kcal/mol, ΔG = 24.7 kcal/mol) and more favourable for the other two ligands (ΔE/ΔG are -6.4/+4.4 kcal/mol for L = py and -1.5/+6.0 for L = MeCN). However, in consideration of the energy gain of the COD hydrogenation process (ΔE = -34.4 or ΔG = -18.5 kcal/mol for the 70 hydrogenation to cyclooctene; ΔE = -64.3 or ΔG = -33.1 kcal/mol for the hydrogenation to cyclooctane), all the underlying reactions become favourable.

Scheme 8 Table 2 .

 82 Scheme 8Table2. Relative gas phase energies E (free energies G in parentheses) in kcal/mol for the products of H2 oxidative addition to I L .L Functional II L III L IV L V L MeOH B3LYP-8.5(+2.6) -10.5(+1.3) -14.1(-2.5) -17.6(-6.5) py B3LYP -4.5(+7.4) -4.9(+7.9) -10.1(+1.5) -10.0(+2.1) py M06 +4.5(+15.2) +2.2(+14.7) -2.5(+7.8) -3.5(+6.0) MeCN B3LYP -4.2(+5.6) -4.9(+6.0) -8.5(+1.7) -10.9(-0.1)

Figure 5 .

 5 Figure 5. Energy profile (gas phase energies in kcal/mol) of the

  second hydride ligand in both 8 and 9 is trans to sulfur. All the NMR data associated with 8 and 9 resulting from this study are summarized in Table1. The similarity of these NMR data suggests that these two products are simply diastereoisomers of one another, differentiated by the ferrocene ligand orientation as shown in Scheme 4. This product geometry indicates that the initial H2 oxidative addition takes place over the S-Rh-C axis.

	(a)	9			9
	8					8
				Scheme 4
	-13	-14	-15	-16	-17	-18	ppm

the

  without moving the second H ligand in the equatorial plane. One attractive possibility for this transformation is a selective deprotonation of the axial site in A by excess pyridine, which is 45 the strongest base present in solution, to yield a square planar monohydride intermediate [RhH(κ 1 -P,S)(py)2], followed by reprotonation at the opposite face of the square plane to afford B. This pathway is closely related to one of the proposed pathways for the H/D exchange between H2 and solvent in pure CD3OH 50 (through intermediates 5 and 6 of Scheme 3). No calculations were carried out, however, to confirm the feasibility of this pathway. The fact that this strong effect is not observed during the transformation of the minor isomer into the major one (89) is consistent with the need to overcome the smaller TSBD barrier 55 in the first step to achieve the rapid site scrambling. Hence, if the transition state of the putative site-conserving concerted pathway is lower than TSAC but higher or comparable to TSBD, then the site exchange would remain operative even at low pyridine concentration. For the same reason, a very large concentration of 60 pyridine does not afford a selective site inversion because collapse to the nonclassical intermediate promotes non selective deprotonation.

	25
	transformation of the minor isomer (8) into the major one (9)
	occurs preferentially as Hac and Hbd at low [py] but
	preferentially as Had and Hbc at high [py] (see Scheme 5). The
	former situation, according to Scheme 9, would be compatible
	with a concerted pathway directly converting A to B without
	transiting through the nonclassical intermediate (arrows with
	question mark in Scheme 9) through a transition state at lower
	energy than TSAC and TSBD. As stated above, such a pathway
	could not be located but we cannot exclude that it exists. At
	greater pyridine concentrations, this pathway may be blocked by
	reversible coordination of pyridine to A and B while perhaps the
	excess pyridine could also assist a faster collapse of the two
	hydrides into the H2 ligand (for instance through the effect of H-
	bonding).
	Reversal of the relative rates of site exchange requires a new

40

isomerisation pathway, resulting in selective migration of the axial H ligand in the square pyramidal geometry of A and B

  14,22 CNRS, LCC (Laboratoire de Chimie de Coordination), 205 route de Narbonne, BP 44099, F-31077 Toulouse Cedex 4, France CNRS. Fax: +33-561553003; Tel: +33-561333173; E-mail: rinaldo.poli@lcctoulouse.fr, eric.manoury@lcc-toulouse.fr. b Université de Toulouse, UPS, INPT, F-31077 Toulouse Cedex 4, France c A. N. Nesmeyanov Institute of Organoelement Compounds, Russian Academy of Sciences, Vavilov Street 28, 119991 Moscow, Russia; Fax: +7-499-1355085; Tel:+7-499-1356448; E-mail: nataliabelk@ineos.ac.ru d Centre for Hyperpolarisation in Magnetic Resonance, Department of Chemistry, University of York, Heslington, York, YO10 5NY, UK. Fax :

	+44-1904-322516 ; Tel : +44-1904322564 ;E-mail:
	simon.duckett@york.ac.uk
	e Institut Universitaire de France, 103, bd Saint-Michel, 75005 Paris,
	France.
	† Electronic Supplementary Information (ESI) available: sample
	preparation details for the parahydrogen NMR experiments, selected
	NMR spectra, description of the EXSY experiments of hydride exchange,
	figures of DFT optimized geometries and full table of cartesian
	coordinates	for	the	DFT	optimized	structures.	See
	DOI: 10.1039/b000000x/

80 10 a

pyridine; (c)15 N labeled, 1 H-15 N HMQC NMR spectrum; (d)1 H-103 Rh HMQC NMR spectrum (in (b) the inset boxes reflect vertical expansions of x 2 and x 8 relative to the baseline).result from PHIP derived magnetic states that are created with identical efficiency. When the temperature is raised to 273 K the ratio increases to 1:13.Upon increasing the solution temperature to 283 K, no change in the relative size of the PHIP-enhanced signals was observed. However, upon warming to 298 K and beyond, the hydride signals for 8 and 9 could no longer be observed. When this experiment was repeated at 263 K with normal dihydrogen, the ratio of the hydride signals of 8 and 9 was 1:7. When, on the other hand, the reaction was conducted at room temperature (ca. 15 min between bubbling and recording the NMR spectrum), only the presence of compound 9 could be detected, reflecting the greater thermodynamic preference for this isomer.

(Centre Informatique National de l'Enseignement Supérieur) and the CICT (Centre Interuniversitaire de Calcul de Toulouse, project CALMIP) for granting free computational time. EMK thanks the Embassy of France in Moscow for a Ph.D. grant.

Acknowledgements

The York authors would like to thank the EPSRC (grant no. EP/G009546/1) for funding. The Toulouse and Moscow authors are grateful to the CNRS (Centre National de la Recherche Scientifique) and the RFBR (Russian Foundation for Basic 85 Research) for support through the GDRI (Groupe de Recherche Internationale) "Homogeneous catalysis for Sustainable Development" and the RFBR grants no. 12-03-93112, 12-03-31326, and 12-03-33018. We are also grateful to the IUF (Institut Universitaire de France) for additional funding and to the CINES


Gerhard Weikum
email: weikum@mpi-inf.mpg.de

Johannes Hoffart
email: johannes@ambiverse.com

Fabian Suchanek
email: fabian@suchanek.name

Knowledge Harvesting: Achievements and Challenges

whether they are published or not. The documents may come

Introduction

Enhancing computers with "machine knowledge" that can power intelligent applications is a long-standing goal of computer science [START_REF] Lenat | On the Thresholds of Knowledge[END_REF]. Major advances on knowledge harvesting -methods for turning noisy Internet content into crisp knowledge structures on entities and relations -have made this formerly elusive vision practically viable today.

A prominent use case where knowledge bases (KB's) have become a key asset is search engines. When we send a query like "jobs biography" to Bing or Google, we obtain information on the life of Steve Jobs. So the search engine automatically detects that we are interested in facts about an individual entity. On the other hand, for a query like "jobs in bay area", the search engine locates the spatial entity Bay Area and properly interprets the query as a request for local job ads. Finally, for the query "jobs at apple", the system returns a mix of two different interpretations. All this is feasible because the search engine has a huge knowledge base on its back-end servers, aiding in the discovery of entities in user requests (and their contexts) and in finding concise answers.

The KB's in this setting are centered on individual entities, containing: • entities like people, places, organizations, products, events (e.g., SteveJobs, the GoldenGateBridge, the Pixar company, the iPhone7, the WoodstockConcert), • the semantic classes to which entities belong (e.g., SteveJobs type entrepreneur, SteveJobs type computerPioneer, SteveJobs type ZenBuddhist),

• relationships between entities (e.g., SteveJobs founded AppleInc, SteveJobs invented iPhone, SteveJobs diedOf PancreaticCancer), as well as • their validity times (e.g., SteveJobs wasCEOof Pixar [1986,2006]).

This concept of a comprehensive KB goes back to pioneering work in Artificial Intelligence on universal knowledge bases in the 1980s and 1990s, most notably, the Cyc project at MCC in Austin [START_REF] Lenat | CYC: A Large-Scale Investment in Knowledge Infrastructure[END_REF] and the WordNet project at Princeton [START_REF] Fellbaum | WordNet: An Electronic Lexical Database[END_REF]. However, these knowledge collections have been hand-crafted and manually curated. Thus, knowledge acquisition was inherently limited in scope and scale. Starting this millenium with the Semantic Web vision, domainspecific ontologies [START_REF] Staab | Handbook on Ontologies[END_REF] have been developed, but these are also manually created. In the last decade, automatic knowledge harvesting from Web and text sources has become a major research avenue, and has made substantial practical impact. Knowledge harvesting is the core methodology for the automatic construction of large knowledge bases, going beyond manually compiled knowledge collections like Cyc or WordNet.

These achievements are rooted in academic research and community projects. Salient projects that started ten to fifteen years ago are DBpedia [START_REF] Auer | DBpedia: A Nucleus for a Web of Open Data[END_REF], Freebase [START_REF] Bollacker | Freebase: a Collaboratively Created Graph Database for Structuring Human Knowledge[END_REF], KnowItAll [START_REF] Etzioni | Unsupervised Named-Entity Extraction from the Web: an Experimental Study[END_REF], WebOfConcepts [START_REF] Dalvi | A Web of Concepts[END_REF], WikiTaxonomy [START_REF] Ponzetto | Deriving a Large-Scale Taxonomy from Wikipedia[END_REF] and Yago [START_REF] Suchanek | YAGO: a Core of Semantic Knowledge[END_REF]. More recent projects with publicly available data include BabelNet [START_REF] Navigli | BabelNet: The Automatic Construction, Evaluation and Application of a Multilingual Semantic Network[END_REF], Con-ceptNet [START_REF] Speer | Representing General Relational Knowledge in ConceptNet 5[END_REF], DeepDive [START_REF] Shin | Incremental Knowledge Base Construction Using DeepDive[END_REF], EntityCube (aka. Renlifang) [START_REF] Nie | Statistical Entity Extraction From the Web[END_REF], KnowledgeVault [START_REF] Dong | Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion[END_REF], NELL [START_REF] Carlson | Toward an Architecture for Never-Ending Language Learning[END_REF] Probase [START_REF] Wu | Probase: a Probabilistic Taxonomy for Text Understanding[END_REF], Wikidata [START_REF] Vrandecic | Wikidata: a free collaborative knowledgebase[END_REF], XLore [START_REF] Wang | Xlore: a Large-Scale English-Chinese Bilingual Knowledge Graph[END_REF].

The largest general-purpose KB's with publicly accessible contents are Ba-belNet (babelnet.org), DBpedia (dbpedia.org), Wikidata (http://wikidata.org) and Yago (yago-knowledge.org). They contain millions of entities, organized in hundreds to hundred thousands of semantic classes, and hundred millions to billions of relational facts on entities. These and other knowledge resources are interlinked at the entity level, forming the Web of Linked Open Data [START_REF] Heath | Linked Data: Evolving the Web into a Global Data Space[END_REF].

Our own endeavor on knowledge harvesting was motivated by research on semantic search. Later it became the Yago-Naga project, with the first release of the Yago KB (yago-knowledge.org) in February 2007. The strength of Yago is its rich type system with hundred thousands of classes. When IBM Watson won the Jeopardy quiz show, it leveraged Yago's knowledge of fine-grained entity types for semantic type checking [28]. More recent Yago releases incorporated temporal and spatial knowledge [START_REF] Hoffart | YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia[END_REF] and multilingual properties [START_REF] Rebele | YAGO: a Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames[END_REF]. Yago is maintained as a joint project of the Max Planck Institute for Informatics and the Télécom ParisTech University.

Over the last five years, knowledge harvesting has been adopted at big industrial stakeholders, and large KB's have become a key asset in a variety of commercial applications, including semantic search (see, e.g., [START_REF] Bast | Semantic Search on Text and Knowledge Bases[END_REF]), analytics (e.g., aggregating by entities), recommendations and data integration (i.e., to combine heterogeneous datasets in and across enterprises). Examples are the Google Knowledge Graph (with Freebase as a catalyst), the use of KB's in IBM Watson, Amazon's Evi, the Baidu Knowledge Graph, Facebook's Graph Search, Microsoft Satori, Wolfram Alpha as well as domain-specific knowledge bases in business, finance, life sciences, and more (e.g., at Bloomberg, Mayo Clinic, Siemens, Walmart, etc.). In addition, KB's have found wide use as a distant supervision source for a variety of tasks in natural language processing, such as entity linking.

This article gives an overview of knowledge harvesting. Section 2 reviews research achievements and the state of the art, identifying key principles and best-practice methods. Section 3 presents open challenges that provide opportunities for future research.

Achievements

Knowledge Base Model

Knowledge representation has received great attention in AI research, leading to sophisticated forms of epistemic logics and, with the advent of the Semantic Web, description logics for ontologies [START_REF] Staab | Handbook on Ontologies[END_REF]. However, none of these powerful models ever led to sizable collections of knowledge. The main reason then was the lack of data to populate knowledge models with interesting instances.

Entities, Classes and Relations:

The former limitation of sparse data on instances was eventually overcome by the advent of knowledge-sharing communities like Wikipedia and the increasing availability of public datasets. This enabled the extraction of entities and their properties from high-quality Internet sources to populate a KB. Most of today's KB's use a simple relational representation where all knowledge is cast into grounded formulas of n-ary predicates (i.e., relation symbols). Constants (i.e., 0-ary predicates) denote entities or literal values (e.g., coordinates, dates and other numbers), unary predicates correspond to classes of same-type entities, and higher-arity predicates are used to capture entity attributes or relationships with other entities.

SPO Triples:

The widely used RDF data model even restricts n-ary relations to be binary and casts the unary predicates for class membership into the binary form entity type class -in infix notation, with type being the predicate. The elemtary formulas in an RDF-compliant KB are also called subject-predicateobject triples, or SPO triples for short. S is required to be an entity, whereas O can be either an entity (e.g., someone's spouse) or a literal (e.g., someone's birthdate). The following shows examples for such triples (in conceptual form, disregarding the specifics of the RDF syntax): The two triples in the first line above are about class membership. In standard logics these would be written as unary-predicate formulas: computerPioneer (SteveJobs) and entrepreneur (SteveJobs). This form of knowledge about instances of classes forms the backbone of a KB. The two triples in the second line above organize classes in a subsumption hierarchy of subtypes and supertypes. To avoid that this subclassOf predicate takes second-order form, the class predicates are cast into binary form with the type predicate. All triples for the type and subclassOf predicate constitute the taxonomic knowledge of a KB. Prior work on ontologies mostly focused on intensional knowledge centered on the subclassOf predicate, and hardly captured any instances for the type predicate.

Many KB's require that the subject arguments of type be individual entities (aka. named entities) that can be uniquely identified in the real world, this way disregarding abstract entities (aka. general concepts) which are prone to subjective interpretation. For example, concepts such as universe, love or quantum physics are intentionally excluded to stay clear of potential pitfalls. This way, it has become feasible to construct huge KB's with millions of entities for many thousands of classes with negligible error rate and very high agreement on what is correct knowledge.

Taxonomic knowledge is already a huge asset for applications like search and analytics. For example, a query such as "Which singers have won the Nobel prize?" can be easily answered from the following triples (by intersecting instances of different classes):

BobDylan type singer BobDylan type NobelPrizeLaureates

Likewise, an analytic task that compares frequencies of queries, clicks or references for musicians vs. politicians, is made easy by a KB that provides the class memberships of entities along with subclass knowledge such as: singer subclassOf musician stateSecretary subclassOf politician SPO triples with predicates other than type or subclassOf are referred to as facts. The largest KB's contain billions of facts for several thousand different predicates. The predicates of interest (e.g., bornIn, hasDaughter, namedAfter etc.) are often pre-specified. We will reconsider this point in Section 2.2.5.

Beyond SPO Triples: KB's with this focus on binary relational facts are often called knowledge graphs (KG's), as they correspond to labeled graphs with nodes denoting entities and predicate-labeled edges for the facts. However, the restriction to binary predicates is an oversimplification. There are many cases where ternary and higher-arity relations are needed to represent real-world situations. One important case is temporal knowledge: extending facts with their validity times, a timepoint or a timespan. For example, capturing when Steve Jobs (co-) founded Apple or from when until when he was CEO of his various companies calls for ternary predicates with an additional time argument:

SteveJobs founded AppleInc 1-April-1976 SteveJobs wasCEOof AppleInc [9-July-1997, 24-Aug-2011] Generally, events often require three or more arguments to fully capture them: several entities involved in various roles, time, place, etc. As an example, consider the football match where Germany won 7:1 against Brazil on 8-July-2014 in Belo Horizonte. Such a composite fact cannot be broken down into binary facts without losing information. The RDF data model then usually escapes to so-called reification: assigning an entity id to the entire event, and adding multiple triples for the event's different aspects with the entity id as their common subject argument. However, this technique makes querying and reasoning with knowledge more tedious. Higher-arity predicates, on the other hand, are a natural representation.

Canonicalization: An important point in capturing crisp knowledge is the uniqueness of representing entities and facts. Many (but not all) KB's strive to canonicalize all arguments in their SPO triples and other facts. For classes, this implies that differently named classes have (potentially) different instances (e.g., singer vs. songwriter vs. musician); conversely, differently named classes that would always agree on their instances should be unified (e.g., humans vs. people). For entities, the same principle applies: an entity can have different names, but should be captured only once with a unique id. For example, knowledge about the company Apple should not be registered twice or spread across different names like "Apple", "Apple Inc.", "Apple company". Instead, we need to map all relevant facts to the same entity, canoncially denoted, for example, as AppleInc, and keep its diverse surface names as labels (aka. alias names). This issue calls for named entity disambiguation (aka. entity linking) (see, e.g., [START_REF] Ling | Design Challenges for Entity Linking[END_REF][START_REF] Shen | Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions[END_REF]). For populating a KB, this step can be integrated into a knowledge harvesting method or dealt with by specialized methods and tools.

The canonicalization principle carries over to entire facts. For example, a high-quality KB should represent the gist of the sentences "Jobs was born in San Francisco." and "Apple's charismatic Steve is from the City by the Bay" in a single fact SteveJobs bornIn SanFrancisco. Without this unique representation, querying a KB and using it for inference would be awkward.

Lessons Learned: A key point in enabling knowledge harvesting at large scale has been to use a simple SPO-style representation as a backbone. This is sufficient for a core KB to capture taxonomic classes and basic facts about millions of individual entities. We emphasize, however, that this does not rule out additional knowledge using higher-arity predicates and more sophisticated representations. In particular, a rich KB should also comprise intensional assertions about the world: constraints that couple different predicates or rules for deducing additional facts. For example, specifying that a person can have only one birthplace and that the birthplace must be an entity of type location, is a vital piece of knowledge. Obviously, this calls for more expressive predicate logics. We will come back to this in Section 2.2.4.

Knowledge Gathering and Cleaning

For a KB model as outlined above, the core task of knowledge harvesting is to i) identify appropriate data sources as input, ii) extract entities, classes and relational facts, and iii) organize them into a clean KB. This builds on techniques from the area of information extraction (IE), based on patterns in Web pages and text documents (see [START_REF] Chiticariu | Transparent Machine Learning for Information Extraction: State-of-the-Art and the Future[END_REF][START_REF] Sarawagi | Information Extraction[END_REF] for surveys). However, IE copes with one source as fixed input (e.g., one Web site), whereas knowledge harvesting has freedom in choosing its sources and can exploit redundancy and statistics across many sources. Important knowledge is often expressed in many sources in complementary ways. We can pick low-hanging fruit by choosing the best suitable sources and treat sources with different degrees of noise in very different ways.

This principle suggests a layered approach where we first tap sources with limited noise in content and structure, using robust extraction methods for highquality output. This is why many KB's intensively build on harvesting semi-structured elements of Wikipedia: category names, infoboxes, lists, headings, etc. For specific domains (e.g., health), there are often specific sources that should be prioritized (e.g., repositories such as Medscape, DrugBank, FAERS, etc.).

As a second stage, additional knowledge can then be harvested from riskier sources. We will see below that the previously compiled high-quality knowledge is beneficial in filtering noise and cleaning candidate facts. In other words, once we have a strong core KB, it is an asset in acquiring more knowledge, deeper knowledge and better knowledge -without degrading in quality.

Harvesting Entities and Classes

The first goal of knowledge harvesting is to compile a comprehensive set of semantic classes (e.g., guitarists, electric guitarists, left-handed guitarists, etc.) and their instances (i.e., individual entities such as Bob Dylan, Jimmy Page, Jimi Hendrix, etc.). In addition, we want to capture subsumptions among classes. We could address this task ab initio, starting with raw Internet contents, but it would be unwise to ignore pre-existing high-quality resources. First, classic work on linguistics and cognition led to WordNet [START_REF] Fellbaum | WordNet: An Electronic Lexical Database[END_REF], a large repository of words and word senses with lexical relations like synonymy, antonymy, hypernymy (i.e., subsumption), etc. This can be seen as a source of more than 100,000 semantically classes, carefully organized into a subclass/superclass taxonomy. Second, knowledge-centric communities like Wikipedia organize articles in category systems. While these were noisy in the early years of Wikipedia (with improper or misleading categories), the editorial guidelines and manual curation of Wikipedia have eventually led to a very rich and reasonably accurate system of about half a million categories. Given these prior assets, one line of successful research has derived taxonomic knowledge for high-quality KB's from WordNet and Wikipedia [START_REF] Navigli | BabelNet: The Automatic Construction, Evaluation and Application of a Multilingual Semantic Network[END_REF][START_REF] Ponzetto | Deriving a Large-Scale Taxonomy from Wikipedia[END_REF][START_REF] Suchanek | YAGO: a Core of Semantic Knowledge[END_REF]. The Yago KB, for example, has carefully unified WordNet classes and Wikipedia categories and constructed a high-quality taxonomy with more than 300,000 fine-grained classes.

Once we settle on a class taxonomy, the next step is to populate the classes with individual entities (where one entity can belong to several classes). Here, Wikipedia is by far the largest and best asset to tap into, as it already organizes articles about entities within its category system. Most of the large KB's have seized this opportunity. It is straightforward when the taxonomy is based on Wikipedia categories. For connecting to WordNet classes, though, clever alignment and pruning techniques are needed [START_REF] Suchanek | YAGO: a Core of Semantic Knowledge[END_REF]. The philosophy of "picking lowhanging fruit first" also carries over beyond Wikipedia. For example, Yago has integrated GeoNames (geonames.org) for spatial entities [START_REF] Hoffart | YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia[END_REF]. For health entities like diseases, symptoms, drugs, etc., manually curated sources like UMLS, MeSH, DrugBank, etc. can be used very effectively (see, e.g., [START_REF] Ernst | KnowLife: a Versatile Approach for Constructing a Large Knowledge Graph for Biomedical Sciences[END_REF]). Generally, entities of specific types (e.g., books, songs, medical drugs, etc.) can often be harvested from dedicated sources or specific identifier systems [START_REF] Talaika | IBEX: Harvesting Entities from the Web Using Unique Identifiers[END_REF]. As a caveat, we note that this does not completely cover the world of emerging entities like new events or people who suddenly become notable (e.g., a new singer). This aspect of knowledge dynamics will be discussed in Section 2.3.

A major alternative to relying on Wikipedia-style sources is to tap all kinds of Internet sources at large scale. A classic approach for this line of taxonomy induction research is to use so-called Hearst patterns like X such as Y or X, Y and other Z and match them against Web and text contents to extract class-entity or subclass-superclass pairs [START_REF] Hearst | Automatic Acquisition of Hyponyms from Large Text Corpora[END_REF]. Using advanced data-mining and machine-learning techniques, this idea can be greatly generalized, to tap into additional patterns, http links and HTML tables (where a column name and cell value may indicate a class-entity pair), or into query-and-click logs of big search engines (see, e.g., [START_REF] Craven | Learning to Construct Knowledge Bases from the World Wide Web[END_REF][START_REF] Kozareva | Learning Arguments and Supertypes of Semantic Relations Using Recursive Patterns[END_REF][START_REF] Pasca | Open-Domain Fine-Grained Class Extraction from Web Search Queries[END_REF][START_REF] Venetis | Recovering Semantics of Tables on the Web[END_REF][START_REF] Wang | Iterative Set Expansion of Named Entities Using the Web[END_REF][START_REF] Wu | Probase: a Probabilistic Taxonomy for Text Understanding[END_REF]). These are powerful methods; however, they require large-scale machinery and access to big data like complete Web crawls or search engine logs.

Gathering Facts from Wikipedia

The most straightforward way of gathering binary facts is to harvest infoboxes in Wikipedia. These provide attributes and relationships of the entity featured in an article, in semi-structured form. Here is an example in the wiki markup language:

{{ Infobox person | name = Steve Jobs | birth date = Birth date|1955|2|24|mf=y | birth place = [[San Francisco]], California, U.

S. | death cause = [[Pancreatic cancer]] and [[respiratory arrest]] }}

Naturally, these P and O components for SPO triples can be extracted by regular expressions (i.e., finite state automata). These expressions may even be automatically learned from samples with manual markup. Unfortunately, even Wikipedia infoboxes exhibit noise and terminological diversity. For example, birthplaces could be stated differently in different articles: birth place = ..., born in = ..., born in city = ..., etc.; and the values may be encoded in different ways (e.g., with or without the state in a country). To cope with this heterogeneity, type-checking the outputs of a regex matcher is a boost in quality [START_REF] Hoffart | YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia[END_REF][START_REF] Suchanek | YAGO: a Core of Semantic Knowledge[END_REF]. Here, having rich taxonomic knowledge -entities and their fine-grained types -is a huge benefit. The result is clean facts in canonicalized form.

There are other opportunities to extract facts from semi-structured elements (i.e., headings, tables, etc.) in Wikipedia or similar high-quality sources. As the diversity of how facts are expressed increases, this calls for stochastic variants of automata, like Conditional Random Fields (CRF's). Facts from infoboxes can be used to train CRF-based extractors (e.g., [START_REF] Wu | Weld: Information Extraction from Wikipedia: Moving Down the Long Tail[END_REF]).

Gathering Fact Candidates from Text

For high recall (i.e., gathering as many facts as possible), we eventually need to tap into natural language text as input, facing even higher degrees of noise and variability. In this setting, pattern-based harvesting has been the method of choice. For example, birthplaces of people are often expressed by phrases such as "his birthplace is", "her hometown", "is from", etc. Of course, it would be daunting to manually specify such patterns for hundreds or thousands of predicates. Instead, a distantly supervised approach has become prevalent, centered on the principle of pattern-fact duality [START_REF] Agichtein | Snowball: Extracting Relations from Large Plain-Text Collections[END_REF][START_REF] Brin | Extracting Patterns and Relations from the World Wide Web[END_REF][START_REF] Etzioni | Unsupervised Named-Entity Extraction from the Web: an Experimental Study[END_REF][START_REF] Mintz | Distant Supervision for Relation Extraction without Labeled Data[END_REF]. For each relation of interest, a small set of seed facts is needed, for example, the correct birthplaces of a few prominent people (which could be obtained from semi-structured high-quality sources). These facts can be matched against a corpus (or the entire Internet) by searching for sentences that contain the subject entity and the object entity. The key idea then is that facts frequently co-occur with connecting phrases (e.g., verbal phrases) that can be distilled into patterns. The patterns in turn co-occur with other, newly seen facts. This procedure -alternating between facts and patterns -can be iterated, and eventually yields a large number of new fact candidates. Patterns can be surface phrases, but can also cover generalizations such as lifting the words "his" and "her" into personal pronouns, capture nonadjacent words, or use dependency parsing to consider the syntactic structure of sentences. Also, HTML tables in Web pages can be tapped for fact harvesting in a similar vein; this has been successfully pursued, for example, by the NELL and Knowledge Vault projects [START_REF] Dong | Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion[END_REF][START_REF] Mitchell | Never-Ending Learning[END_REF].

Of course, this gathering process needs to be comlemented by computing statistical measures of confidence and support. Otherwise, spurious patterns may easily lead to drifting targets. For example, starting with Steve Job's birthplace San Francisco among the seed facts, the method could pick up the pattern "his favorite place" after a few iterations. By judicious thresholding, one can obtain a good set of assertions for facts. This approach typically results in an accuracy around 80% -that is, there are still 20% of the assertions incorrect, due to noise in the patterns.

Cleaning Fact Candidates

The high recall of the outlined gathering methods comes at the expense of potentially degrading in precision: introducing many false candidates for facts. For example, we may obtain the following assertions for the birthplace relation:

SteveJobs birthplace SanFrancisco SteveJobs birthplace USA SteveJobs birthplace Kyoto SteveJobs birthplace AppleInc

These fact candidates may be derived from diverse patterns for the birthplace predicate, such as "his hometown", "citizen of", "his favorite place" or "mostly at". The resulting triples have to be cleaned: removing spurious assertions, and also mapping patterns of good triples -"his hometown" in the above case -onto canonicalized predicates. Next, we discuss methods for this purpose.

Consistency Constraints:

A key idea is to mimic human skepticism: use plausibility considerations to rule out dubious assertions. In technical terms, we can impose consistency constraints on the candidate space, to discard assertions that violate certain conditions. The simplest idea is to enforce type constraints, and this actually provides enormous mileage towards building a high-quality KB. For the above example, the relation about where people are born should be specified with a type signature birthplace: person × location, or more specifically birthplace: person × city. This constitutes a logical constraint: ∀x, y (birthplace(x, y) ⇒ (type(x, person) ∧ type(y, city)))

The constraint is violated by 2 of the 4 candidate facts above, leaving only San Francisco and Kyoto as possible cities where Steve Jobs was born.

This logics-based approach is far more general than mere type checking. There are other kinds of constraints to be harnessed, most notably, functional constraints -many relations are actually functions -and inclusion constraints between different relations. For example, each person can have only one birthplace, and birthplaces are usually among the cities where a person has lived: ∀x, y, z ((birthplace(x, y) ∧ birthplace(x, z)) ⇒ y = z) ∀x, y (birthplace(x, y) ⇒ livedIn(x, y))

These constraints have to be manually specified by a knowledge engineer. However, this is a fairly easy modeling task, and not a bottleneck at all. There are also techniques for automatically learning constraints from data (see, e.g., [START_REF] Carlson | Toward an Architecture for Never-Ending Language Learning[END_REF][START_REF] Mitchell | Never-Ending Learning[END_REF]), but this comes at a higher risk. In general, constraints can be hard or soft: absolutely excluding any violation or tolerating a certain degree of exceptions. For example, the above formula that couples birthplace and livedIn is soft. In such cases, the constraints may be weighted. Fact candidates are weighted as well, typically by some notion of confidence based on statistics from the gathering stage. This opens the way to deciding between San Francisco and Kyoto for the birthplace of Steve Jobs.

Consistency Reasoning: Fact candidates should not solely be checked against each constraint in isolation. Instead, it is beneficial to perform joint inference over a set of assertions and a set of constraints. Consider, for example, the following noisy candidates and soft constraints, with the last two constraints stating that someone can either be a scientist or a musician, but never both:

BobDylan hasWon Grammy BobDylan hasWon LiteratureNobelPrize BobDylan hasWon PhysicsNobelprize ∀x (hasW on(x, Grammy) ⇒ type(x, musician)) ∀x (hasW on(x, P hysicsN obelP rize) ⇒ type(x, scientist)) ∀x (type(x, scientist) ⇒ ¬type(x, musician)) ∀x (type(x, musician) ⇒ ¬type(x, scientist))

There is no way to keep all three fact candidates while enforcing all constraints. There are two different combinations of prizes, though, that are consistent. By viewing the data as a set of logical formulas, this becomes a test for satisfiability. By grounding the constraints with the constants from the candidate fact pool, the problem is reduced to an instance of the MaxSat problem.

Moreover, since each individual formula has a weight (see above), the task is to compute a Weighted MaxSat solution. Although this is a classical NP-hard problem, there are good approximation techniques and there are ways of customizing them to this specific task of knowledge cleaning [START_REF] Nakashole | Scalable Knowledge Harvesting with High Precision and High Recall[END_REF][START_REF] Suchanek | SOFIE: a Self-Organizing Framework for Information Extraction[END_REF]. In the example, assume that the weight for the Grammy fact is much higher than the weight for the Physics Nobel Prize and the weights for the three constraints are identical. Then, the best solution is to accept the facts about the Grammy and Literature Nobel Prize while dropping the assertion about the Physics Nobel Prize.

Probabilistic Graphical Models: The above line of thought has been very fruitful for knowledge cleaning and comes in a variety of ways: MaxSat reasoning is one approach, integer linear programming another one, and there are also powerful probabilistic inference methods along these lines. The latter include especially probabilistic graphical models, where random variables for accepting or refuting fact candidates are coupled through logical constraints [START_REF] Domingos | Markov Logic: An Interface Layer for Artificial Intelligence[END_REF][START_REF] Koller | Probabilistic Graphical Models[END_REF]. In this setting, the MAP inference (MAP = maximum a posteriori) is equivalent to solving a weighted MaxSat problem. Approximation algorithms include SAT solvers, Monte Carlo sampling, variational calculus and more. Applications to knowledge harvesting have been developed, among others, by [START_REF] Carlson | Toward an Architecture for Never-Ending Language Learning[END_REF][START_REF] Pujara | Knowledge Graph Identification[END_REF][START_REF] Shin | Incremental Knowledge Base Construction Using DeepDive[END_REF][START_REF] Suchanek | SOFIE: a Self-Organizing Framework for Information Extraction[END_REF][START_REF] Yao | Structured Relation Discovery using Generative Models[END_REF][START_REF] Zhu | StatSnowball: a Statistical Approach to Extracting Entity Relationships[END_REF]. Constraints have also been leveraged for estimating the confidence in specific extractions and for training fact extraction methods (see, e.g., [START_REF] Mitchell | Never-Ending Learning[END_REF]).

Beyond Triples: Methods of this kind can be further extended to go beyond binary relations. An important use case is temporal knowledge where facts need to be annotated with timepoints or timespans when they are valid [START_REF] Ling | Temporal Information Extraction[END_REF][START_REF] Talukdar | Coupled Temporal Scoping of Relational Facts[END_REF][START_REF] Wang | Coupling Label Propagation and Constraints for Temporal Fact Extraction[END_REF]. For example, properly interpreting a fact such as BobDylan spouseOf SaraDylan requires the corresponding time scope [Nov-1965, June-1977]. So strictly speaking, we are looking at a ternary relation here: spouseOf: person × person × time. This is a special case of higher-arity relations, often but not only in combination with entities of type event. Knowledge harvesting methods, as outlined above, can be further extended to this end (see, e.g., [START_REF] Krause | Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web[END_REF]).

Other Approaches

A potential concern about the above methods is their limitation to prespecified predicates. For example, we can harvest composers of songs or artists who covered songs only after a human curator provides the relevant predicates with type signatures: composedMusic: musician × song and coveredMusic: musician × song. On the other hand, facts on song lyrics being about specific people cannot be harvested unless we explicitly model such a predicate. Therefore, we refer to the presented methods as model-driven knowledge harvesting.

Open IE: The paradigm of Open Information Extraction (Open IE) [START_REF] Banko | Open Information Extraction from the Web[END_REF][START_REF] Del Corro | ClausIE: Clause-based Open Information Extraction[END_REF][START_REF] Mausam | Open Language Learning for Information Extraction[END_REF] offers unsupervised harvesting of fact triples in an open-ended manner, without any modeling effort. Using linguistic patterns, Open IE collects all kinds of triples where S, P and O are meaningful phrases, typically noun phrases for S and O and verbal phrases for P. These are not canonicalized; so outputs could be: "Dylan's Hurricane" "covers the story of" "the black boxer Carter" "Hurricane" "is a protest song about" "racist victim Carter" "Sara" "is a love song about" "Dylan's wife" "the love song Sara" "is about" "his ex-wife Sara Dylan"

Such output requires disambiguating the S and O arguments of the phraselevel triples, which is typically an entity-linking task [START_REF] Shen | Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions[END_REF] against an existing KB of entities. Canonicalizing the P components, on the other hand, is an open challenge as the space of possible predicates is unknown in this open-ended setting. Research along these lines, based on clustering techniques, includes [START_REF] Galarraga | Canonicalizing Open Knowledge Bases[END_REF].

Deep Learning: With recent breakthroughs in deep learning [START_REF] Lecun | Deep Learning[END_REF], an intriguing thought could be to bypass the explicit construction of a KB, and rather use end-to-end learning on a per-task basis (e.g., question answering or describing videos). However, this raises caveats. First, it would require huge amounts of labeled training data which are often unavailable. Second, this expensive training would have to be repeated for every new task. Third, machine learning outputs (i.e., predictions, recommendations, answers or even decisions) are not easily explainable to human users. Explicit KB's, on the other hand, are a reusable asset for many tasks, they can inform and constrain the learning of models, and they support user-comprehensible explanations.

Knowledge Evolution and Quality

Change is the only constant in knowledge. Attribute values of entities (e.g., city populations) and relationships between entities (e.g., the CEO of a company or a person's spouse) change over time. Moreover, new entities of interest are created all the time and need to be added to the KB (e.g., new songs, sports matches, babies of celebrities). Also, existing entities may be irrelevant for a KB at some point, but become prominent at a later point. Typical examples are when a "garage band" or "garage company" becomes succesful. If Wikipedia had already existed in 1976, it would probably have dismissed Apple for insufficient notability. Temporal Knowledge: So KB's must be continuously updated. This requires keeping versions of facts, along with their temporal validity scopes. We already discussed methods for harvesting temporal knowledge in Section 2.2.4. Some of the major KB's have rigorously followed this principle (e.g., [START_REF] Hoffart | YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia[END_REF]). Active Knowledge: For some kinds of highly dynamic and specialized knowledge, explicitly capturing fact versions is not practically feasible. For example, the chart positions of a song and the box office counts of a movie change so rapidly that it is hardly meaningful to materialize such facts in the KB. Instead, a preferable way is to keep links to specialized databases and to Web services that return up-to-date values on demand [START_REF] Preda | Active Knowledge: Dynamically Enriching RDF Knowledge Bases by Web Services[END_REF]. Emerging Entities: A specific aspect of dynamic knowledge is to cope with newly emerging entities. When discovering entity names in input sources (text, Web tables, etc.), we first aim to disambiguate them onto the already known entities in the KB. However, even if there is a good match in the KB, it is not necessarily the proper interpretation. For example, when the documentary movie "Amy" was first mentioned a few years ago, it would have been tempting to link the name to the soul singer Amy Winehouse. However, although the movie is about the singer's life, the two entities must not be confused. To rectify this situation, each observed name should always be potentially associated with an additional virtual candidate: none of the known entities [START_REF] Hoffart | Discovering Emerging Entities with Ambiguous Names[END_REF]. When the evidence for the name denoting an out-of-KB entity is stronger than for a known entity, we capture the name as a new entity, along with its context. After a while, we will thus obtain a repository of recently emerging entity names. Then, clustering techniques can be used to group the names, and knowledge curators can be asked to confirm these canoncialized groups. Finally, the confirmed entities can be registered in the KB. To keep the effort for the curation step as low as possible, new entity candidates should be presented with informative context [START_REF] Hoffart | The Knowledge Awakens: Keeping Knowledge Bases Fresh with Emerging Entities[END_REF].

On-the-fly Knowledge Bases: A specific case for out-of-KB entities is constructing ad-hoc KB's on the fly. Suppose a new corpus of documents becomes available, for example, the Panama Papers or a batch of articles on a hot political or health topic, such as the UK Brexit or Zika infections. Then we should be able to automatically build a domain-and corpus-specific KB overnight, to support journalists and analysts in exploring the topic and analyzing particularly interesting issues.

Knowledge Curation: As a KB is continuously updated and keeps growing, it is virtually inevitable that errors sneak in and degrade the KB quality. Versioning of facts helps to control the maintenance process, but may also lead to conflicting versions. For example, when a new name for the CEO for a company is detected by harvesting fresh online sources, should this be added as a new fact or is it, perhaps, evidence that the previous fact in the KB was incorrect? One case leads to a new version, the other should result in overwriting the prior version. Even worse, an error may be detected only post-hoc, days or weeks after a new version was added and became interlinked with other facts.

Thus, quality assurance requires a fundamental solution. Fact cleaning, as discussed in Section 2.2.4 is an important element, and so is versioning. However, more is needed for a comprehensive approach. Today, very large KB's resort to manual curation, by having human volunteers (e.g., in an online community like Wikidata) or paid workers (in a commercial KB) checking newly added or altered facts. Such a crowdsourcing solution can be orchestrated in various ways, with the goal of optimizing the benefit/cost ratio (e.g., [START_REF] Kobren | Getting More for Less: Optimized Crowdsourcing with Dynamic Tasks and Goals[END_REF]).

Fact Checking: An alternative or complement to human curators is to harness the sources of evidence for doubtful facts in a more principled manner. This may entail actively searching for evidence or counter-evidence about facts, as a continuous background process. A typical approach is to consider a small number of alternative O values for given S and P of a fact, such as birthplaces for Steve Jobs: San Francisco vs. Cupertino vs. Kyoto. Then statistics derived from Internet sources (i.e., databases, Web sites, news, etc.) can guide the analysis and assessment of candidates towards finding the truth (see [START_REF] Li | A Survey on Truth Discovery[END_REF] for a survey). This form of knowledge corroboration or knowledge fusion aggregates the observation confidence from different sources, where sources are weighted by their trustworthiness. Therefore, reasoning on truth (of statements) and trust (of sources) are often intertwined. Not surprisingly, joint inference methods, based on probabilistic models, have been pursued to this end (e.g., [START_REF] Dong | Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources[END_REF][START_REF] Mukherjee | People on Drugs: Credibility of User Statements in Health Communities[END_REF][START_REF] Pasternack | Latent Credibility Analysis[END_REF][START_REF] Popat | Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media[END_REF]).

Challenges

Notwithstanding the great advances of knowledge harvesting over the last decade, there are major challenges left open -raising the bar for what computers should know. In the following, we discuss a few strategic challenges, pointing out opportunities for future research.

Knowledge Base Coverage

No matter how large a KB can grow, it will never be fully complete. The gaps, relative to an ideal KB, take different forms as discussed next.

Locally Incomplete Knowledge: This form of incompleteness can be formally characterized by referring to SPO triples that are in the KB and triples that should ideally be included but are missing. The most obvious case is when some O values are absent for a given S and P value -for example, when the KB contains some movies of a director but not all of them. Another case is when for a given P value, we have O values for some S but not all of them -for example, knowing spouses of some people but not knowing any spouses for other married people. The challenge here is not just to fill these gaps, but to realize when and where gaps exist. Reasoning over locally closed worlds is one recent approach [START_REF] Galarraga | Predicting Completeness in Knowledge Bases[END_REF]. More research is needed to equip KB's with self-reflection abilities to automatically detect their own gaps.

Long-tail Entities and Classes: There are many lesser known musicians, regional politicians and good but not exactly famous scientists. How can we identify these long-tail entities in the Internet, and harvest facts about them? Long-tail classes pose a similar problem: even with hundred thousands of classes in some KB's, one could always add more interesting ones. For example, what if we want to capture classes like YogaPractitioners or BobDylanFans (both of which would have Steve Jobs as a member)? Where in the class taxonomy should these classes be placed, and how can we find their instances?

Missing Facts: KB's have largely been constructed in an opportunistic manner, tapping into Wikipedia and its semi-structured data elements. If something is not explicitly said in Wikipedia or stated only in sophisticated form in the article's text, most KB's will miss it. This has resulted in high coverage of elementary facts like birthdates, marriages, albums of musicians, etc., but has neglected a diversity of salient facts that stand out to a human but are not easily captured by a machine. For example, what is notable about Bob Dylan's album Blonde on Blonde? KB's offer the release date, the list of songs, etc. To a human, however, salient properties are, for example, that this was one of the first albums on which Dylan "went electric" (using electric instruments -irritating many of his folk music fans), and that its song "Sad Eyed Lady of the Lowlands" is about Dylan's wife Sara. The song was later covered by Joan Baez, who had a romantic relationship with Bob Dylan in the early 1960s and with Steve Jobs in the early 1980s. Her song "Diamonds and Rust" is about Bob Dylan. None of this is captured by any KB; most do not even have the proper predicates like romanceWith or songIsAbout.

Commonsense, Rules and Socio-Cultural Knowledge

Automatically constructed KB's have mostly focused on harvesting encyclopedic fact knowledge. However, for semantic search and other intelligent applications (e.g., conversational bots in social media), machines need a broader understanding of the world: properties of everyday objects, human activities, plausibility invariants and more. This overriding goal calls for various research directions. Commonsense: One objective is to distill commonsense from Internet sources. This is about properties of objects like size, color, shape, parts or substance of which an object is made of, etc., and knowledge on which objects are used for which activities as well as when and where certain activities typically happen. For example, a rock concert involves musicians, instruments -almost always including drums and guitars, speakers, a microphone for the singer; the typical location is a stage, and so on. This background knowledge is beneficial for the interpretation of user questions, and also for retrieving images and videos when queries refer to abstractions or emotions that cannot be directly matched by captions, tags or other text. Today's search engines perform poorly on queries such as "exhausted band at hippie concert" (where users may want to find footage of concerts by the Grateful Dead or the Doors). Recent work on acquiring commonsense includes ConceptNet [START_REF] Speer | Representing General Relational Knowledge in ConceptNet 5[END_REF] and WebChild [START_REF] Tandon | WebChild: Harvesting and Organizing Commonsense Knowledge from the Web[END_REF]; research with the specific focus of organizing knowledge on human activities includes [START_REF] Tandon | Knowlywood: Mining Activity Knowledge From Hollywood Narratives[END_REF]. There is, however, still a long way to go for computers to learn what every child knows. Visual Knowledge: Commonsense knowledge is often more expressed in visual form than in textual sources; for example, think of colors, shapes and sizes of objects. This observation entails the dual goals of i) tapping visual contents like images and videos for acquiring knowledge and ii) constructing a KB about visual properties. Along the latter lines, ImageNet [START_REF] Deng | ImageNet: A Large-Scale Hierarchical Image Database[END_REF] is the most notable endeavor, which has populated a large fraction of WordNet classes with images. The NEIL project [START_REF] Chen | NEIL: Extracting Visual Knowledge from Web Data[END_REF] has a gone a step further by extracting visual relationships between objects. WebChild has acquired different kinds of part-whole knowledge, at large scale, from combining textual and visual cues [START_REF] Tandon | Commonsense in Parts: Mining Part-Whole Relations from the Web and Image Tags[END_REF]. This is an instantiation of the general challenge of jointly distilling knowledge from vision and language (e.g., from movie scenes and their narratives [START_REF] Rohrbach | A Dataset for Movie Description[END_REF]). Rules: Another key element for advancing the intelligent behavior of machines is to capture invariants over certain kinds of facts, in the form of logical rules. For example, a rule about scientists and their advisors could state that the advisor be on the faculty of the scientist's alma mater -as of the time when the scientist graduated. Similarly to the role of constraints in fact cleaning (see Section 2.2.4), rules can have exceptions. But regardless of their soft nature, rules can be a great asset to answer more queries and to infer additional facts for KB completion. For example, a person who has or had a position on the government of a certain country, is most likely a citizen of that country. To acquire this kind of intensional knowledge, rule mining methods have been applied to large KB's [START_REF] Galarraga | Fast Rule Mining in Ontological Knowledge Bases with AMIE+[END_REF]. However, the state of the art has major limitations: rules are restricted in their logical form to Horn clauses or least clauses. This disallows rules with existential quantifiers or with disjunctions in the rule head; for example, expressing that every human person has a mother and that every human is male or female, would be beyond the current scope. An alternative approach to KB completion, which bypasses logical representations, is to start with a tensor of SPO triples and use matrix or tensor factorization methods to predict additional triples (similarly to recommender systems) [START_REF] Nickel | A Review of Relational Machine Learning for Knowledge Graphs[END_REF]. However, the derivation of new facts is not easily explainable with such methods.

Commonsense rules were already in the focus of the seminal Cyc project [START_REF] Lenat | CYC: A Large-Scale Investment in Knowledge Infrastructure[END_REF], but Cyc relied on human experts to manually specify logical axioms. A major challenge with automatic rule mining arises from the open world assumption that underlies KB's and the bias in observations from Internet sources. For example, if a KB does not contain any person who has won two different Nobel prizes, this should not imply a functionality constraint or cardinality constraint. In fact, Marie Curie is a counterexample anyway, but some KB's may have only a partial list of her awards. Likewise, a KB may have a restricted view on the wealth of entrepreneurs: for example, all founders of IT companies have become billionaires. This can be caused by the bias in the KB construction (e.g., by harvesting only successful entrepreneurs from Wikipedia), and should not entail a rule in the open world.

Socio-Cultural Knowledge: Another dimension where today's KB's have a huge gap is the socio-cultural context of facts or rules. Consider statements on people making discoveries and inventions. On first glance, one would expect that these are objective and universally agreed upon. On second thought, however, it becomes clear that it depends on the background and viewpoint of users. For example, most people in the US would say that the computer was invented by Eckert and Mauchley, whereas a German would give the credit to Konrad Zuse and a British may point out Alan Turing (or perhaps Charles Babbage). This depends not just on geographical context; for example, teenagers may widely think of Steve Jobs as the (re-) inventor of the (mobile) computer. For commonsense knowledge, it is even more critical to capture socio-cultural contexts. For example, shaking hands when people meet is the usual way of welcoming someone only in parts of the world. In other regions, people often hug or kiss each other (e.g., in France), or make gestures with both hands (e.g., in Thailand).

Conclusion

Knowledge harvesting has made great impact in enabling the automatic construction of large knowledge bases, sometimes called knowledge graphs. These have become essential assets in search and analytics over Internet contents and enterprise data. In addition to reviewing the underlying methodological achievements, this article has pointed out open challenges towards the next level of machine knowledge.

The success of deep learning, to enable smart computer behavior by training on raw data, may open up new perspectives on knowledge harvesting as well. Do computers need this kind of explicit knowledge representation at all, or is taskspecific end-to-end learning in a sub-symbolic manner sufficient? We believe that machine knowledge and machine learning are complementary assets: the more you know the better you learn, and better learning enables acquiring more and deeper knowledge. A final challenge and research opportunity is to explore this potential synergy.